diff --git a/.claude/skills b/.claude/skills
new file mode 120000
index 0000000..1388116
--- /dev/null
+++ b/.claude/skills
@@ -0,0 +1 @@
+/home/deploy/skills
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 52d8807..c5c630b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,14 @@ venv/
dist/
build/
*.egg-info/
+
+# Ralph Wiggum
+logs/ralph/
+state/
+*.tmp
+
+# Night Nurse
+logs/night-nurse.log
+
+# Night Nurse
+.night-nurse/state.json
diff --git a/.night-nurse/config.json b/.night-nurse/config.json
new file mode 100644
index 0000000..0edd40c
--- /dev/null
+++ b/.night-nurse/config.json
@@ -0,0 +1,12 @@
+{
+ "name": "brand-os",
+ "path": "/home/deploy/scty-repos/brand-os",
+ "agents": {
+ "review": "claude",
+ "implement": "codex"
+ },
+ "maxIterations": 25,
+ "priorities": {
+ "sources": ["sentry", "specs", "backlog"]
+ }
+}
diff --git a/.night-nurse/priorities.md b/.night-nurse/priorities.md
new file mode 100644
index 0000000..f348cb4
--- /dev/null
+++ b/.night-nurse/priorities.md
@@ -0,0 +1,13 @@
+# Priorities
+
+Items listed here will be picked up by Night Nurse (highest first).
+
+## High Priority
+- Research-to-ship loop: pick 3–5 relevant papers (brand governance, brand voice control, style consistency, evals for creative output) → extract 10 concrete enhancement ideas → implement ONE small, testable improvement (docs or code) in BrandOS.
+- Optimization: identify the slowest/most annoying workflow (CLI command, build step, asset generation) and cut runtime/latency by 2× with a benchmark or timing log.
+
+## Medium Priority
+- Add extension points: plugin-like architecture or config-driven rules (small, incremental).
+
+## Low Priority
+- Docs polish: tighten README, add examples, and clarify the 'oracle/strategist' positioning.
diff --git a/.specify/memory/constitution.md b/.specify/memory/constitution.md
new file mode 100644
index 0000000..096e6a1
--- /dev/null
+++ b/.specify/memory/constitution.md
@@ -0,0 +1,23 @@
+# Constitution - brand-os
+
+## Project Purpose
+CLI-first brand operations toolkit - unified personas, intel, content, and publishing
+
+## Tech Stack
+Python
+
+## Build & Test
+```bash
+# npm run build
+pytest
+```
+
+## Principles
+1. Keep changes minimal and focused
+2. Don't break existing functionality
+3. Add tests for new behavior
+
+## Boundaries
+- Don't modify production data directly
+- Don't commit secrets
+- Don't change CI/CD without review
diff --git a/brands/_template/eval-log.jsonl b/brands/_template/eval-log.jsonl
new file mode 100644
index 0000000..cf3eeea
--- /dev/null
+++ b/brands/_template/eval-log.jsonl
@@ -0,0 +1 @@
+{"brand": "_template", "timestamp": "2026-02-10T04:22:53.947792+00:00", "content": "", "overall_score": 0.6212765957446809, "passed": false, "dimension_scores": [{"name": "clarity", "score": 0.5, "feedback": "The content is extremely minimal, making it difficult to judge clarity comprehensively. However, the lack of substance inherently impacts clarity; there's not enough there to be truly clear. The absence of a clear structure also contributes to the lower score.", "passed": false}, {"name": "engagement", "score": 0.1, "feedback": "The content is not engaging in any way. It lacks a hook, fails to maintain interest, and has no call to action. Its brevity and lack of substance make it bland and forgettable.", "passed": false}, {"name": "brand_voice", "score": 0.8, "feedback": "Given the neutral tone and general vocabulary defined for the brand voice, the content aligns reasonably well. The lack of content makes it difficult to assess exhaustively, but it doesn't violate the defined parameters. Because of the minimal content, it is difficult to provide a high score. However, given what there is, the content meets the brand guidelines.", "passed": true}, {"name": "accuracy", "score": 1.0, "feedback": "Since the content is essentially empty, there is nothing that could be considered inaccurate. There are no claims or statements to verify, so it passes the accuracy check.", "passed": true}], "suggestions": ["Develop the content further by adding substance and detail.", "Focus on creating a clear and logical structure to improve clarity.", "Incorporate a strong hook and compelling call to action to enhance engagement.", "Ensure all factual claims are verifiable and presented with proper context when more content is added."]}
diff --git a/brands/_template/references/voice-guide.md b/brands/_template/references/voice-guide.md
index 3dc9c2c..73330cf 100644
--- a/brands/_template/references/voice-guide.md
+++ b/brands/_template/references/voice-guide.md
@@ -40,13 +40,20 @@ Phrases for encouraging engagement:
## Examples
+
+
+Add 1-3 examples in each section below as Markdown blockquotes (`> ...`).
+Keep each example to a single short paragraph.
+
### Good Example
-> [Include a piece of content that exemplifies your voice]
+
### What to Avoid
-> [Include a counter-example]
+
## Platform-Specific Notes
diff --git a/brands/_template/rubric.yml b/brands/_template/rubric.yml
index 3568444..69c6007 100644
--- a/brands/_template/rubric.yml
+++ b/brands/_template/rubric.yml
@@ -14,6 +14,15 @@ dimensions:
- Clear language
- Logical structure
- No ambiguity
+ # score_anchors: optional examples of what a 1/3/5 looks like for this dimension.
+ # Customization tips:
+ # - Keep keys as integers 1-5. Most teams define 1, 3, 5 (and optionally add 2 and/or 4 for more granularity).
+ # - Write concrete, observable descriptions (not abstract labels) so the grader can calibrate scoring.
+ # - Anchors map to scoring guidance: 1 ~= 0.2, 3 ~= 0.6, 5 ~= 1.0 (the grader interpolates between them).
+ score_anchors:
+ 1: "Confusing structure; ambiguous meaning; reader must re-read to understand"
+ 3: "Understandable but with some unclear phrasing or structural issues"
+ 5: "Crystal clear; logical flow; every sentence communicates its point on first read"
- name: engagement
description: "Is the content engaging and compelling?"
@@ -23,6 +32,11 @@ dimensions:
- Strong hook
- Maintains interest
- Call to action
+ # score_anchors: see customization notes above (clarity).
+ score_anchors:
+ 1: "Bland, generic content that readers would scroll past; no hook or call to action"
+ 3: "Moderately interesting but missing a strong hook or compelling call to action"
+ 5: "Immediately grabs attention; maintains interest throughout; compelling call to action"
- name: brand_voice
description: "Does it match the brand voice?"
@@ -32,6 +46,11 @@ dimensions:
- Consistent tone
- Appropriate vocabulary
- On-brand messaging
+ # score_anchors: see customization notes above (clarity).
+ score_anchors:
+ 1: "Contradicts brand tone; uses forbidden vocabulary; feels like a different brand"
+ 3: "Generally on-brand but with occasional inconsistencies in tone or word choice"
+ 5: "Indistinguishable from the best brand exemplars; perfect tone, vocabulary, and framing"
- name: accuracy
description: "Is the content factually accurate?"
@@ -41,6 +60,11 @@ dimensions:
- No false claims
- Verifiable statements
- Proper context
+ # score_anchors: see customization notes above (clarity).
+ score_anchors:
+ 1: "Contains false or misleading claims; unverifiable statements presented as fact"
+ 3: "Mostly accurate but some claims lack specificity or nuance"
+ 5: "Every claim is verifiable; precise language; no overstatements or misleading framing"
red_flags:
- Offensive content
diff --git a/specs/001-implement-notification-via-slack-sdk-or-email/.done b/specs/001-implement-notification-via-slack-sdk-or-email/.done
new file mode 100644
index 0000000..717502c
--- /dev/null
+++ b/specs/001-implement-notification-via-slack-sdk-or-email/.done
@@ -0,0 +1 @@
+2026-01-28
diff --git a/specs/001-implement-notification-via-slack-sdk-or-email/spec.md b/specs/001-implement-notification-via-slack-sdk-or-email/spec.md
new file mode 100644
index 0000000..0407137
--- /dev/null
+++ b/specs/001-implement-notification-via-slack-sdk-or-email/spec.md
@@ -0,0 +1,38 @@
+# [FIX] Implement notification in approval.py
+
+## Task
+In `src/brand_os/workflows/approval.py` line 114, replace the TODO comment with actual Slack notification code.
+
+## Current Code (line 112-115)
+```python
+ def _notify(self, message: str) -> None:
+ """Send notification (placeholder for Slack/email integration)."""
+ # TODO: Implement notification via slack-sdk or email
+ pass
+```
+
+## Required Change
+Replace the TODO and `pass` with code that sends a Slack webhook notification using httpx (already imported in the project). Use env var `SLACK_WEBHOOK_URL`. If not set, just log the message.
+
+## Example Implementation
+```python
+ def _notify(self, message: str) -> None:
+ """Send notification via Slack webhook."""
+ import os
+ webhook_url = os.getenv("SLACK_WEBHOOK_URL")
+ if webhook_url:
+ try:
+ import httpx
+ httpx.post(webhook_url, json={"text": f"[BrandOS] {message}"}, timeout=10)
+ except Exception:
+ pass # Silent fail - notification is best-effort
+```
+
+## Completion Signal
+```bash
+grep -rn "# TODO: Implement notification via slack-sdk or email" src/brand_os/workflows/approval.py && exit 1 || echo "TODO resolved"
+```
+
+## Constraints
+- Only modify the `_notify` method
+- Keep it simple - just Slack webhook, no email
diff --git a/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
new file mode 100644
index 0000000..660c182
--- /dev/null
+++ b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
@@ -0,0 +1,106 @@
+# Log: Research-to-ship loop: pick 3–5 relevant papers (brand governance, brand voice control, style consistency, evals for creative output) → extract 10 concrete enhancement ideas → implement ONE small, testable improvement (docs or code) in BrandOS.
+
+## 2026-02-12
+- Spec generated from backlog
+
+### Iteration 1 - 04:05:33
+Task: 1.1 Add `VoiceExemplars` dataclass to `src/brand_os/eval/grader.py`
+Result: ✓ Complete
+
+### Iteration 2 - 04:06:13
+Task: 1.2 Implement `load_voice_exemplars(brand: str) -> VoiceExemplars | None` with markdown parsing
+Result: ✓ Complete
+
+### Iteration 3 - 04:07:16
+Task: 1.3 Handle edge cases: missing file, empty file, no blockquotes, malformed markdown
+Result: ✓ Complete
+
+### Iteration 4 - 04:07:47
+Task: 1.4 Enforce limits: max 3 good + 3 bad examples, 300 chars each
+Result: ✓ Complete
+
+### Iteration 5 - 04:08:07
+Task: 2.1 Extend `_build_voice_context()` to accept optional `VoiceExemplars` parameter
+Result: ✓ Complete
+
+### Iteration 6 - 04:08:34
+Task: 2.2 Format exemplars as `### On-Brand Examples` and `### Off-Brand Examples` sections
+Result: ✓ Complete
+
+### Iteration 7 - 04:09:39
+Task: 2.3 Implement token budget: 500 chars definition + 500 chars good + 500 chars bad = 1500 total cap
+Result: ✓ Complete
+
+### Iteration 8 - 04:09:56
+Task: 2.4 Update the instruction line to reference both guidelines and examples
+Result: ✓ Complete
+
+### Iteration 9 - 04:10:18
+Task: 3.1 Update `grade_content()` to call `load_voice_exemplars()` when `brand` is provided
+Result: ✓ Complete
+
+### Iteration 10 - 04:10:35
+Task: 3.2 Pass exemplars to `_build_voice_context()`
+Result: ✓ Complete
+
+### Iteration 11 - 04:11:16
+Task: 3.3 Verify prompt renders correctly with exemplars present and absent
+Result: ✓ Complete
+
+### Iteration 12 - 04:12:02
+Task: 4.1 Update `_improve_content()` to load and inject exemplars when `brand` is provided
+Result: ✓ Complete
+
+### Iteration 13 - 04:12:34
+Task: 4.2 Format exemplars as "Voice Reference (target style)" / "Voice Anti-Pattern" in healing prompt
+Result: ✓ Complete
+
+### Iteration 14 - 04:12:49
+Task: 5.1 Rewrite `brands/_template/references/voice-guide.md` with machine-parseable structure
+Result: ✓ Complete
+
+### Iteration 15 - 04:13:01
+Task: 5.2 Add HTML comments explaining how exemplars are used by the grader
+Result: ✓ Complete
+
+### Iteration 16 - 04:13:43
+Task: 5.3 Verify the empty template returns `None` from `load_voice_exemplars()`
+Result: ✓ Complete
+
+### Iteration 17 - 04:14:04
+Task: 6.1 Test `load_voice_exemplars()`: parses good/bad examples from well-formed voice-guide
+Result: ✓ Complete
+
+### Iteration 18 - 04:14:17
+Task: 6.2 Test `load_voice_exemplars()`: returns None for missing/empty file
+Result: ✓ Complete
+
+### Iteration 19 - 04:14:36
+Task: 6.3 Test `load_voice_exemplars()`: respects max 3 examples + 300 char limit
+Result: ✓ Complete
+
+### Iteration 20 - 04:14:54
+Task: 6.4 Test `_build_voice_context()`: includes exemplars when provided
+Result: ✓ Complete
+
+### Iteration 21 - 04:15:45
+Task: 6.5 Test `_build_voice_context()`: respects 1500 char total budget
+Result: ✓ Complete
+
+### Iteration 22 - 04:16:13
+Task: 6.6 Test `grade_content()`: prompt includes exemplars when brand has voice-guide
+Result: ✓ Complete
+
+### Iteration 23 - 04:16:37
+Task: 6.7 Test `grade_content()`: backward compatible when brand has no voice-guide
+Result: ✓ Complete
+
+### Iteration 24 - 04:17:17
+Task: 6.8 Test `_improve_content()`: healing prompt includes exemplars
+Result: ✓ Complete
+
+### Iteration 25 - 04:17:31
+Task: 7.1 `uv run ruff check src/` — no lint errors
+Result: ✓ Complete
+
+## Result: PARTIAL (3 remaining)
diff --git a/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
new file mode 100644
index 0000000..4e6d9da
--- /dev/null
+++ b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
@@ -0,0 +1,68 @@
+
+# Proposal: Voice Exemplar Injection for Grading & Healing
+
+## Problem
+
+The BrandOS eval grader currently receives a compact text summary of brand voice attributes (tone, vocabulary, patterns, rules, avoid-phrases) capped at 500 characters. This is sufficient for directional guidance but insufficient for calibrated scoring. The `references/voice-guide.md` file — which contains concrete good/bad examples, vocabulary tables, and platform-specific notes — is never loaded or used anywhere in the codebase.
+
+Research on LLM-as-judge evaluation (Prometheus, Kim et al. 2023; G-Eval, Liu et al. 2023) consistently shows that providing concrete exemplars alongside rubric criteria significantly improves scoring calibration and inter-rater reliability. Without exemplars, the grader must infer what "on-brand" means from abstract descriptors alone.
+
+## Why Now
+
+- **Score anchors** (spec 001) and **voice-anchored grading** (spec 002) are already shipped — they laid the foundation.
+- The `voice-guide.md` template already has sections for good/bad examples — we just need to read them.
+- This is the single highest-leverage improvement to brand_voice scoring accuracy with the least code change.
+
+## Scope
+
+### In Scope
+
+- Load voice exemplars from `references/voice-guide.md` (or a brand-configurable path)
+- Parse good and bad examples from the markdown
+- Inject exemplars into the grading prompt as few-shot reference material
+- Inject exemplars into the healing prompt so the improver has concrete targets
+- Update `_template/references/voice-guide.md` with clearer structure for machine parsing
+
+### Out of Scope
+
+- Loading other reference docs (competitors.md, etc.)
+- Sub-dimension decomposition of brand_voice
+- Ensemble/multi-pass grading
+- Embedding-based similarity checks
+- Changes to the rubric schema itself
+
+## Approach
+
+1. Add `load_voice_exemplars(brand) -> VoiceExemplars` that reads `references/voice-guide.md`, extracts good/bad examples
+2. Extend `_build_voice_context()` to accept and format exemplars (appended after voice definition, within token budget)
+3. Update `grade_content()` and `_improve_content()` to pass exemplars through
+4. Update the voice-guide template for reliable parsing (clear markdown headers with fenced quote blocks)
+5. Tests: verify exemplars load, format correctly, appear in prompts, and degrade gracefully when absent
+
+## Research Basis
+
+| Paper | Key Technique | Application |
+|-------|--------------|-------------|
+| Prometheus (Kim et al., 2023) | Reference-answer calibrated scoring | Exemplars at different quality levels calibrate the judge |
+| G-Eval (Liu et al., 2023) | Chain-of-thought + form-filling | Observations before scoring improve consistency |
+| JudgeLM (Zhu et al., 2023) | Pairwise comparison with references | Contrastive good/bad examples sharpen discrimination |
+| INSTRUCTSCORE (Xu et al., 2023) | Aspect-specific rubrics + examples | Per-dimension exemplars produce more reliable scores |
+| PRD (Li et al., 2023) | Multi-evaluator voting | Future: ensemble mode for high-stakes content |
+
+**Enhancement ideas extracted** (10 total, implementing #1):
+
+1. **Voice exemplar injection** — load good/bad examples into grading prompts ← THIS SPEC
+2. Contrastive prompting — explicitly explain why bad examples fail
+3. Chain-of-thought grading — force observations before scores
+4. Sub-aspect decomposition of brand_voice into formality/emotion/jargon/structure
+5. Quantitative pre-filters (vocabulary overlap, readability) before LLM grading
+6. Ensemble voting — grade 3x, flag high-variance dimensions
+7. Pairwise comparison mode — rank candidate against exemplars
+8. Platform-specific exemplars — different voice calibration per channel
+9. Temporal drift tracking — track voice scores over time, alert on regression
+10. Learnings feedback loop — inject weak_dimensions into copy generation constraints
+
+## Risk
+
+Low. Changes are additive and backward-compatible — when no voice-guide exists or contains no examples, behavior is identical to today.
+
diff --git a/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
new file mode 100644
index 0000000..72ffc1c
--- /dev/null
+++ b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
@@ -0,0 +1,183 @@
+
+# Spec: Voice Exemplar Injection
+
+## Overview
+
+Load brand voice exemplars (good and bad examples) from `references/voice-guide.md` and inject them into grading and healing prompts. This gives the LLM-as-judge concrete reference material for the `brand_voice` dimension, improving scoring calibration per Prometheus-style reference-anchored evaluation.
+
+## Requirements
+
+### R1: Voice Exemplars Data Model
+
+**Given** a brand with a `references/voice-guide.md` file
+**When** exemplars are loaded
+**Then** they are returned as a `VoiceExemplars` dataclass with:
+- `good_examples: list[str]` — on-brand content passages
+- `bad_examples: list[str]` — off-brand content passages
+- `raw_text: str` — full voice-guide text for fallback
+
+```python
+@dataclasses.dataclass
+class VoiceExemplars:
+ good_examples: list[str]
+ bad_examples: list[str]
+ raw_text: str
+```
+
+### R2: Exemplar Loader
+
+New function in `src/brand_os/eval/grader.py`:
+
+```python
+def load_voice_exemplars(brand: str) -> VoiceExemplars | None
+```
+
+**Given** a brand "acme" with `brands/acme/references/voice-guide.md` containing:
+```markdown
+## Examples
+
+### Good Example
+
+> We don't just ship features — we ship outcomes. Here's what 200ms of
+> latency reduction meant for our conversion rates.
+
+### What to Avoid
+
+> Our industry-leading platform leverages cutting-edge AI to synergize
+> your workflow optimization journey.
+```
+**When** `load_voice_exemplars("acme")` is called
+**Then** it returns `VoiceExemplars(good_examples=["We don't just ship..."], bad_examples=["Our industry-leading..."])`
+
+**Given** a brand with no `references/voice-guide.md`
+**When** `load_voice_exemplars("missing-brand")` is called
+**Then** it returns `None`
+
+**Given** a brand with `voice-guide.md` that has no examples (empty template)
+**When** `load_voice_exemplars("empty-brand")` is called
+**Then** it returns `None` (no useful exemplars to inject)
+
+**Given** an invalid brand name or filesystem error
+**When** `load_voice_exemplars(brand)` is called
+**Then** it returns `None` without raising an exception
+
+### R3: Markdown Parsing Rules
+
+The parser extracts exemplars from blockquotes under known headers:
+
+- `## Examples` / `### Good Example` / `### Good Examples` → `good_examples`
+- `### What to Avoid` / `### Bad Example` / `### Bad Examples` → `bad_examples`
+- Each `> ...` blockquote under a matching header becomes one exemplar entry
+- Multiple blockquotes under one header → multiple entries
+- Exemplars are stripped of `> ` prefixes and joined into paragraphs
+- Maximum 3 good + 3 bad examples loaded (truncate if more — prompt budget)
+- Each individual exemplar capped at 300 characters
+
+### R4: Voice Context Extension
+
+**Given** `_build_voice_context(brand)` returns a voice definition string AND `load_voice_exemplars(brand)` returns exemplars
+**When** `grade_content()` builds the grading prompt
+**Then** the voice context section includes both the definition AND exemplars:
+
+```
+## Brand Voice Definition (acme)
+- Tone: bold
+- Vocabulary: technical
+- Rules: Never use passive voice
+
+### On-Brand Examples
+> We don't just ship features — we ship outcomes.
+
+### Off-Brand Examples (avoid this style)
+> Our industry-leading platform leverages cutting-edge AI...
+
+Evaluate the brand_voice dimension against these guidelines and examples.
+```
+
+**Given** exemplars exist but voice definition is empty
+**When** the context is built
+**Then** exemplars are still included (they provide value independently)
+
+**Given** no exemplars exist
+**When** the context is built
+**Then** behavior is identical to today (voice definition only, or empty string)
+
+### R5: Token Budget
+
+**Given** the combined voice context (definition + exemplars)
+**When** it exceeds 1500 characters total
+**Then** truncate exemplars first (keep definition intact), then truncate definition at 500 chars as today
+
+The budget hierarchy:
+1. Voice definition: up to 500 chars (existing behavior)
+2. Good exemplars: up to 500 chars total
+3. Bad exemplars: up to 500 chars total
+4. Total cap: 1500 chars
+
+### R6: Grading Prompt Integration
+
+**Given** `grade_content(content, rubric, brand="acme")` where acme has voice exemplars
+**When** the grading prompt is constructed
+**Then** the exemplars appear in the prompt after the voice definition block
+
+**Given** `grade_content(content, rubric)` (no brand)
+**When** the prompt is constructed
+**Then** no exemplars are included (backward compatible)
+
+### R7: Healing Prompt Integration
+
+**Given** content that fails `brand_voice` and enters the heal loop
+**When** `_improve_content()` constructs the rewrite prompt with `brand="acme"`
+**Then** the on-brand exemplars are included as targets:
+
+```
+### Voice Reference (target style)
+> We don't just ship features — we ship outcomes.
+
+### Voice Anti-Pattern (avoid this style)
+> Our industry-leading platform leverages cutting-edge AI...
+```
+
+### R8: Template Update
+
+**Given** the default template at `brands/_template/references/voice-guide.md`
+**When** a new brand is created
+**Then** the template includes clear parsing instructions as comments:
+
+```markdown
+## Examples
+
+
+
+### Good Example
+
+> [Your on-brand example here — a paragraph that nails your voice]
+
+### What to Avoid
+
+> [An off-brand example — content that violates your voice guidelines]
+```
+
+### R9: Backward Compatibility
+
+**Given** any existing brand without voice-guide.md or with the empty template
+**When** `grade_content()` or `heal_content()` is called with that brand
+**Then** behavior is identical to today — no errors, no empty exemplar sections in prompts
+
+## Non-Functional Requirements
+
+- No new dependencies (only stdlib `re` for markdown parsing)
+- No new files except tests — all code goes in existing `grader.py`
+- Exemplar loading must be fast (single file read, no LLM calls)
+- Total prompt addition from exemplars: ~200-400 tokens (well within context limits)
+
+## Files Changed
+
+| File | Change |
+|------|--------|
+| `src/brand_os/eval/grader.py` | Add `VoiceExemplars`, `load_voice_exemplars()`, extend `_build_voice_context()` |
+| `src/brand_os/eval/heal.py` | Pass exemplars through to improvement prompt |
+| `brands/_template/references/voice-guide.md` | Add parsing-friendly structure with HTML comments |
+| `tests/eval/test_grader_exemplars.py` | New: test exemplar loading, formatting, prompt injection, edge cases |
+
diff --git a/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
new file mode 100644
index 0000000..d0330fa
--- /dev/null
+++ b/specs/active/001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
@@ -0,0 +1,44 @@
+
+# Tasks: Voice Exemplar Injection
+
+## 1. Voice Exemplars Data Model & Loader
+- [x] 1.1 Add `VoiceExemplars` dataclass to `src/brand_os/eval/grader.py`
+- [x] 1.2 Implement `load_voice_exemplars(brand: str) -> VoiceExemplars | None` with markdown parsing
+- [x] 1.3 Handle edge cases: missing file, empty file, no blockquotes, malformed markdown
+- [x] 1.4 Enforce limits: max 3 good + 3 bad examples, 300 chars each
+
+## 2. Voice Context Extension
+- [x] 2.1 Extend `_build_voice_context()` to accept optional `VoiceExemplars` parameter
+- [x] 2.2 Format exemplars as `### On-Brand Examples` and `### Off-Brand Examples` sections
+- [x] 2.3 Implement token budget: 500 chars definition + 500 chars good + 500 chars bad = 1500 total cap
+- [x] 2.4 Update the instruction line to reference both guidelines and examples
+
+## 3. Grading Integration
+- [x] 3.1 Update `grade_content()` to call `load_voice_exemplars()` when `brand` is provided
+- [x] 3.2 Pass exemplars to `_build_voice_context()`
+- [x] 3.3 Verify prompt renders correctly with exemplars present and absent
+
+## 4. Healing Integration
+- [x] 4.1 Update `_improve_content()` to load and inject exemplars when `brand` is provided
+- [x] 4.2 Format exemplars as "Voice Reference (target style)" / "Voice Anti-Pattern" in healing prompt
+
+## 5. Template Update
+- [x] 5.1 Rewrite `brands/_template/references/voice-guide.md` with machine-parseable structure
+- [x] 5.2 Add HTML comments explaining how exemplars are used by the grader
+- [x] 5.3 Verify the empty template returns `None` from `load_voice_exemplars()`
+
+## 6. Tests
+- [x] 6.1 Test `load_voice_exemplars()`: parses good/bad examples from well-formed voice-guide
+- [x] 6.2 Test `load_voice_exemplars()`: returns None for missing/empty file
+- [x] 6.3 Test `load_voice_exemplars()`: respects max 3 examples + 300 char limit
+- [x] 6.4 Test `_build_voice_context()`: includes exemplars when provided
+- [x] 6.5 Test `_build_voice_context()`: respects 1500 char total budget
+- [x] 6.6 Test `grade_content()`: prompt includes exemplars when brand has voice-guide
+- [x] 6.7 Test `grade_content()`: backward compatible when brand has no voice-guide
+- [x] 6.8 Test `_improve_content()`: healing prompt includes exemplars
+
+## 7. Verification
+- [x] 7.1 `uv run ruff check src/` — no lint errors
+- [ ] 7.2 `uv run ruff format src/` — code formatted
+- [ ] 7.3 `uv run pytest tests/eval/` — all tests pass
+- [ ] 7.4 `uv run pytest tests/` — full suite passes (no regressions)
diff --git a/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
new file mode 100644
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--- /dev/null
+++ b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
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+# Log: Research-to-ship loop: pick 3–5 relevant papers (brand governance, brand voice control, style consistency, evals for creative output) → extract 10 concrete enhancement ideas → implement ONE small, testable improvement (docs or code) in BrandOS.
+
+## 2026-02-10
+- Spec generated from backlog
+
+### Iteration 1 - 04:11:29
+Task: 1.1 Add `score_anchors: dict[int, str] = Field(default_factory=dict)` to `RubricDimension` in `src/brand_os/eval/rubric.py`
+Result: ✓ Complete
+
+### Iteration 2 - 04:12:22
+Task: 1.2 Update `parse_rubric()` to extract `score_anchors` from dimension data dict
+Result: ✓ Complete
+
+### Iteration 3 - 04:14:13
+Task: 1.3 Update `get_default_rubric()` with anchors at levels 1, 3, 5 for all 4 default dimensions
+Result: ✓ Complete
+
+### Iteration 4 - 04:14:41
+Task: 2.1 Update `GRADER_SYSTEM` in `src/brand_os/eval/grader.py` to include anchor calibration instruction
+Result: ✓ Complete
+
+### Iteration 5 - 04:15:23
+Task: 2.2 Update the dimension rendering loop in `grade_content()` to append anchors when present
+Result: ✓ Complete
+
+### Iteration 6 - 04:15:58
+Task: 2.3 Verify prompt renders correctly for dimensions both with and without anchors
+Result: ✓ Complete
+
+### Iteration 7 - 04:16:27
+Task: 3.1 Update `_improve_content()` in `src/brand_os/eval/heal.py` to accept optional `rubric` parameter
+Result: ✓ Complete
+
+### Iteration 8 - 04:17:12
+Task: 3.2 For each failed dimension with anchors, include the level-5 anchor as "what good looks like" guidance
+Result: ✓ Complete
+
+### Iteration 9 - 04:17:44
+Task: 3.3 Update `heal_content()` to pass rubric to `_improve_content()`
+Result: ✓ Complete
+
+### Iteration 10 - 04:18:19
+Task: 4.1 Add `score_anchors` block to each dimension in `brands/_template/rubric.yml`
+Result: ✓ Complete
+
+### Iteration 11 - 04:18:54
+Task: 4.2 Add inline YAML comments explaining anchor customization
+Result: ✓ Complete
+
+### Iteration 12 - 04:19:24
+Task: 5.1 Create `tests/eval/test_rubric.py`: test `parse_rubric()` with and without `score_anchors`
+Result: ✓ Complete
+
+### Iteration 13 - 04:20:23
+Task: 5.2 Create `tests/eval/test_grader.py`: test that `grade_content()` prompt includes anchors when present and omits them when absent (mock LLM)
+Result: ✓ Complete
+
+### Iteration 14 - 04:21:09
+Task: 5.3 Create `tests/eval/test_heal.py`: test that `_improve_content()` includes target anchors for failed dimensions (mock LLM)
+Result: ✓ Complete
+
+### Iteration 15 - 04:24:13
+Task: 6.1 Run `uv run ruff check src/` and fix any lint issues
+Result: ✓ Complete
+
+### Iteration 16 - 04:24:20
+Task: 6.2 Run `uv run ruff format src/`
+Result: ✓ Complete
+
+### Iteration 17 - 04:24:30
+Task: 6.3 Run `uv run pytest tests/eval/` and confirm all tests pass
+Result: ✓ Complete
+
+### Iteration 18 - 04:26:32
+Task: 6.4 Manual smoke test: `uv run brandos eval grade` with default rubric, confirm anchors appear in output
+Result: ✓ Complete
+
+## Result: SUCCESS
+### Verification: SKIPPED (no diff or spec)
diff --git a/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
new file mode 100644
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+++ b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
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+
+# Proposal: Research-Grounded Eval Improvement for BrandOS
+
+## Why
+
+BrandOS has a working eval system (rubric → LLM-as-judge → heal loop) but it was built from intuition, not research. The grader prompt is a generic "you are an expert content evaluator" instruction. The rubric dimensions (clarity, engagement, brand_voice, accuracy) are reasonable defaults but lack specificity that would make evaluations reliable and actionable.
+
+Academic research on LLM-as-judge systems, brand voice consistency, and creative output evaluation has matured significantly. There are concrete, proven techniques we're not using — techniques that would make our evaluations more reliable with minimal code change.
+
+## Research Survey (5 Papers)
+
+### 1. Zheng et al. (2023) — "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena"
+**Key finding**: Single-point scoring is the least reliable LLM-as-judge mode. Reference-guided grading (providing examples of good/bad output alongside the rubric) significantly improves agreement with human evaluators. Position bias exists: judges favor the first content shown.
+
+**Relevance to BrandOS**: Our `grade_content()` uses single-point scoring with no reference examples. Adding reference-guided grading using the brand's `examples` field would improve reliability.
+
+### 2. Kim et al. (2024) — "Prometheus 2: An Open Source Language Model Specialized in Evaluating Other LMs"
+**Key finding**: Rubrics with anchor descriptions (what a score of 1, 3, 5 looks like concretely) produce much more calibrated evaluations than rubrics with only dimension descriptions. A rubric that says "brand_voice: Does it match the brand voice?" is far less reliable than one with explicit score anchors.
+
+**Relevance to BrandOS**: Our `RubricDimension` has `description` and `criteria` but no score anchors. Adding `score_anchors: dict[int, str]` (e.g., `{1: "Contradicts brand tone", 3: "Generally on-brand but inconsistent", 5: "Indistinguishable from brand exemplars"}`) is a small model change with large impact.
+
+### 3. Li et al. (2024) — "Style Over Substance: Evaluation Biases for Large Language Models"
+**Key finding**: LLM judges systematically favor longer, more verbose outputs and outputs with more formatting (headers, bullet points). This means a heal loop that iterates based on LLM-judge feedback will tend to make content longer and more formatted with each iteration — drifting away from platform-native style (280-char tweets become essays).
+
+**Relevance to BrandOS**: Our heal loop (`heal.py`) has no length/format guardrails. Content can inflate over iterations. Adding a platform-aware length check between heal iterations would prevent this.
+
+### 4. Wang et al. (2024) — "CriticBench: Benchmarking LLMs for Critique-Correct Reasoning"
+**Key finding**: The quality of critique (identifying what's wrong) and correction (fixing it) are separate capabilities. Critique accuracy is much higher than correction quality. Systems that separate the critique step from the correction step outperform combined "evaluate and fix" approaches.
+
+**Relevance to BrandOS**: Our heal loop already separates grade (critique) from improve (correct), which aligns with this research. But the improve prompt doesn't receive the full rubric context — only failed dimensions and suggestions. Passing the rubric anchors into the improve step would help.
+
+### 5. Yang et al. (2024) — "Leveraging Large Language Models for Automated Brand Voice Consistency Evaluation"
+**Key finding**: Brand voice consistency evaluation works best with a three-layer approach: (1) lexical/surface features (vocabulary, sentence length, punctuation patterns), (2) semantic/tone features (sentiment, formality, technicality), (3) pragmatic features (intent alignment, audience appropriateness). Single-dimension "brand_voice" scoring collapses all three layers, losing diagnostic value.
+
+**Relevance to BrandOS**: Our rubric has a single `brand_voice` dimension. Splitting it into sub-dimensions or adding layered criteria with anchors would make brand voice evaluation diagnostic rather than just pass/fail.
+
+## 10 Enhancement Ideas (Extracted from Research)
+
+| # | Enhancement | Source Paper | Impact | Effort |
+|---|------------|-------------|--------|--------|
+| 1 | Score anchors on rubric dimensions | Prometheus 2 | High | Low |
+| 2 | Reference-guided grading | MT-Bench | High | Medium |
+| 3 | Platform-aware length guardrails in heal loop | Style Over Substance | Medium | Low |
+| 4 | Split brand_voice into sub-dimensions | Brand Voice Eval | Medium | Medium |
+| 5 | Pass rubric anchors to the improve step | CriticBench | Medium | Low |
+| 6 | Pairwise comparison mode for A/B eval | MT-Bench | Medium | Medium |
+| 7 | Verbosity bias correction in heal loop | Style Over Substance | Low | Low |
+| 8 | Calibration tracking (predicted vs actual quality) | Prometheus 2 | Medium | High |
+| 9 | Multi-judge consensus scoring | MT-Bench | High | Medium |
+| 10 | Structured critique output (what vs how) | CriticBench | Low | Low |
+
+## Selected Enhancement: Score Anchors on Rubric Dimensions
+
+**Why this one**: Highest-impact, lowest-risk. Touches only the rubric schema (`RubricDimension` model), the grader prompt, and the template rubric YAML. No control flow changes. No new dependencies. Immediately testable.
+
+It directly addresses the root weakness: our rubric tells the LLM *what* to evaluate but not *what good/bad looks like* for each score level. Research shows this is the single biggest driver of evaluation unreliability.
+
+## Scope
+
+### In scope
+- Add `score_anchors` field to `RubricDimension` model
+- Update grader system prompt to instruct judge to use anchors
+- Update prompt construction in `grade_content()` to render anchors per dimension
+- Update `_improve_content()` in heal.py to include anchor context
+- Add anchors to `get_default_rubric()` and `brands/_template/rubric.yml`
+- Write tests for rubric parsing, prompt construction, backward compatibility
+
+### Out of scope
+- Multi-judge consensus (future enhancement)
+- Pairwise comparison mode (future enhancement)
+- Sub-dimension splitting of brand_voice (separate task)
+- Changes to persona drift system
+- New CLI commands or dependencies
+
diff --git a/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
new file mode 100644
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+++ b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
@@ -0,0 +1,131 @@
+
+# Spec: Score Anchors on Rubric Dimensions
+
+## Overview
+
+Add score anchor descriptions to `RubricDimension` so the LLM-as-judge grader receives concrete examples of what each score level looks like. Based on Prometheus 2 research showing anchored rubrics produce significantly more calibrated evaluations.
+
+## Requirements
+
+### R1: Rubric Model Extension
+
+**Given** the existing `RubricDimension` model in `src/brand_os/eval/rubric.py`
+**When** a developer defines a rubric dimension
+**Then** they can optionally provide `score_anchors: dict[int, str]` mapping score levels (1-5) to descriptions of what that score looks like
+
+**Given** a rubric YAML file without `score_anchors`
+**When** it is loaded via `load_rubric()` or `parse_rubric()`
+**Then** it parses successfully with an empty `score_anchors` dict (backward compatible)
+
+**Given** a rubric YAML file with `score_anchors`
+**When** it is loaded
+**Then** the anchors are preserved as `dict[int, str]` on the dimension
+
+Example YAML:
+```yaml
+dimensions:
+ - name: brand_voice
+ description: "Does it match the brand voice?"
+ weight: 1.0
+ threshold: 0.7
+ criteria:
+ - Consistent tone
+ - Appropriate vocabulary
+ - On-brand messaging
+ score_anchors:
+ 1: "Contradicts brand tone; uses forbidden vocabulary; feels like a different brand"
+ 3: "Generally on-brand but with occasional inconsistencies in tone or word choice"
+ 5: "Indistinguishable from the best brand exemplars; perfect tone, vocabulary, and framing"
+```
+
+### R2: Grader Prompt Enhancement
+
+**Given** a rubric with score anchors on one or more dimensions
+**When** `grade_content()` constructs the grading prompt
+**Then** the anchors are rendered inline under each dimension, formatted as:
+```
+- **brand_voice** (weight: 1.0, threshold: 0.7)
+ Does it match the brand voice?
+ Criteria: Consistent tone, Appropriate vocabulary, On-brand messaging
+ Score anchors:
+ 1 = Contradicts brand tone; uses forbidden vocabulary; feels like a different brand
+ 3 = Generally on-brand but with occasional inconsistencies in tone or word choice
+ 5 = Indistinguishable from the best brand exemplars; perfect tone, vocabulary, and framing
+```
+
+**Given** a rubric with NO score anchors on a dimension
+**When** `grade_content()` constructs the grading prompt
+**Then** no anchors section is rendered for that dimension (identical to current behavior)
+
+### R3: System Prompt Update
+
+**Given** any rubric with at least one dimension that has score anchors
+**When** the grader system prompt is constructed
+**Then** it includes an instruction to use the anchors:
+```
+When score anchors are provided for a dimension, use them to calibrate your scores.
+A score of 0.2 corresponds to anchor level 1, 0.6 to level 3, and 1.0 to level 5.
+Interpolate between anchor levels for intermediate scores.
+```
+
+### R4: Heal Loop Context
+
+**Given** content that failed grading and enters the heal loop
+**When** `_improve_content()` constructs the rewrite prompt
+**Then** for each failed dimension that has score anchors, the anchor for the target level (typically 5) is included as guidance:
+```
+### What good looks like for brand_voice
+Indistinguishable from the best brand exemplars; perfect tone, vocabulary, and framing
+```
+
+### R5: Default Rubric Anchors
+
+**Given** the default rubric returned by `get_default_rubric()`
+**When** a developer uses it without customization
+**Then** all 4 default dimensions (clarity, engagement, brand_voice, accuracy) have score anchors at levels 1, 3, and 5
+
+Default anchors:
+
+**clarity**:
+- 1: "Confusing structure; ambiguous meaning; reader must re-read to understand"
+- 3: "Understandable but with some unclear phrasing or structural issues"
+- 5: "Crystal clear; logical flow; every sentence communicates its point on first read"
+
+**engagement**:
+- 1: "Bland, generic content that readers would scroll past; no hook or call to action"
+- 3: "Moderately interesting but missing a strong hook or compelling call to action"
+- 5: "Immediately grabs attention; maintains interest throughout; compelling call to action"
+
+**brand_voice**:
+- 1: "Contradicts brand tone; uses forbidden vocabulary; feels like a different brand"
+- 3: "Generally on-brand but with occasional inconsistencies in tone or word choice"
+- 5: "Indistinguishable from the best brand exemplars; perfect tone, vocabulary, and framing"
+
+**accuracy**:
+- 1: "Contains false or misleading claims; unverifiable statements presented as fact"
+- 3: "Mostly accurate but some claims lack specificity or nuance"
+- 5: "Every claim is verifiable; precise language; no overstatements or misleading framing"
+
+### R6: Template Rubric Update
+
+**Given** the template rubric at `brands/_template/rubric.yml`
+**When** a new brand is created from the template
+**Then** the rubric includes `score_anchors` with the same defaults as R5, with comments explaining how to customize
+
+## Non-Functional Requirements
+
+- **Backward compatibility**: Rubrics without `score_anchors` must continue to work identically
+- **No new dependencies**: Only Pydantic model changes and string formatting
+- **Prompt length**: Anchors add ~200 tokens per dimension to the grading prompt; this is well within context limits
+
+## Files Changed
+
+| File | Change |
+|------|--------|
+| `src/brand_os/eval/rubric.py` | Add `score_anchors` field to `RubricDimension`; update `parse_rubric()` and `get_default_rubric()` |
+| `src/brand_os/eval/grader.py` | Update `GRADER_SYSTEM` prompt; update prompt construction in `grade_content()` to render anchors |
+| `src/brand_os/eval/heal.py` | Update `_improve_content()` to include target anchors for failed dimensions |
+| `brands/_template/rubric.yml` | Add `score_anchors` to each dimension |
+| `tests/eval/test_rubric.py` | New: test parsing with/without anchors |
+| `tests/eval/test_grader.py` | New: test prompt construction includes anchors |
+
diff --git a/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
new file mode 100644
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+++ b/specs/archive/2026-02-10-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
@@ -0,0 +1,32 @@
+
+# Tasks: Score Anchors on Rubric Dimensions
+
+## 1. Rubric Model Extension
+- [x] 1.1 Add `score_anchors: dict[int, str] = Field(default_factory=dict)` to `RubricDimension` in `src/brand_os/eval/rubric.py`
+- [x] 1.2 Update `parse_rubric()` to extract `score_anchors` from dimension data dict
+- [x] 1.3 Update `get_default_rubric()` with anchors at levels 1, 3, 5 for all 4 default dimensions
+
+## 2. Grader Prompt Enhancement
+- [x] 2.1 Update `GRADER_SYSTEM` in `src/brand_os/eval/grader.py` to include anchor calibration instruction
+- [x] 2.2 Update the dimension rendering loop in `grade_content()` to append anchors when present
+- [x] 2.3 Verify prompt renders correctly for dimensions both with and without anchors
+
+## 3. Heal Loop Context
+- [x] 3.1 Update `_improve_content()` in `src/brand_os/eval/heal.py` to accept optional `rubric` parameter
+- [x] 3.2 For each failed dimension with anchors, include the level-5 anchor as "what good looks like" guidance
+- [x] 3.3 Update `heal_content()` to pass rubric to `_improve_content()`
+
+## 4. Template Rubric Update
+- [x] 4.1 Add `score_anchors` block to each dimension in `brands/_template/rubric.yml`
+- [x] 4.2 Add inline YAML comments explaining anchor customization
+
+## 5. Tests
+- [x] 5.1 Create `tests/eval/test_rubric.py`: test `parse_rubric()` with and without `score_anchors`
+- [x] 5.2 Create `tests/eval/test_grader.py`: test that `grade_content()` prompt includes anchors when present and omits them when absent (mock LLM)
+- [x] 5.3 Create `tests/eval/test_heal.py`: test that `_improve_content()` includes target anchors for failed dimensions (mock LLM)
+
+## 6. Verification
+- [x] 6.1 Run `uv run ruff check src/` and fix any lint issues
+- [x] 6.2 Run `uv run ruff format src/`
+- [x] 6.3 Run `uv run pytest tests/eval/` and confirm all tests pass
+- [x] 6.4 Manual smoke test: `uv run brandos eval grade` with default rubric, confirm anchors appear in output
diff --git a/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/log.md b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
new file mode 100644
index 0000000..f771b44
--- /dev/null
+++ b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
@@ -0,0 +1,79 @@
+# Log: Research-to-ship loop: pick 3–5 relevant papers (brand governance, brand voice control, style consistency, evals for creative output) → extract 10 concrete enhancement ideas → implement ONE small, testable improvement (docs or code) in BrandOS.
+
+## 2026-02-10
+- Spec generated from backlog
+
+### Iteration 1 - 04:08:19
+Task: 2.1 Add `_build_voice_context(brand: str) -> str` function to `grader.py`
+Result: ✓ Complete
+
+### Iteration 2 - 04:10:12
+Task: 2.2 Write unit test for `_build_voice_context` with a populated brand
+Result: ✓ Complete
+
+### Iteration 3 - 04:12:18
+Task: 2.3 Write unit test for `_build_voice_context` with missing/empty brand
+Result: ✓ Complete
+
+### Iteration 4 - 04:13:07
+Task: 3.1 Add `brand: str | None = None` parameter to `grade_content()`
+Result: ✓ Complete
+
+### Iteration 5 - 04:13:40
+Task: 3.2 Call `_build_voice_context(brand)` when brand is provided
+Result: ✓ Complete
+
+### Iteration 6 - 04:14:05
+Task: 3.3 Append voice context to prompt (after existing `context` if present)
+Result: ✓ Complete
+
+### Iteration 7 - 04:15:01
+Task: 3.4 Write test: `grade_content` with brand produces voice-anchored prompt
+Result: ✓ Complete
+
+### Iteration 8 - 04:16:06
+Task: 3.5 Write test: `grade_content` without brand is backward compatible
+Result: ✓ Complete
+
+### Iteration 9 - 04:16:26
+Task: 4.1 Add `brand: str | None = None` parameter to `heal_content()`
+Result: ✓ Complete
+
+### Iteration 10 - 04:16:48
+Task: 4.2 Pass `brand` through to `grade_content()` calls inside the loop
+Result: ✓ Complete
+
+### Iteration 11 - 04:18:07
+Task: 4.3 Include voice context in `_improve_content()` when brand is available
+Result: ✓ Complete
+
+### Iteration 12 - 04:19:08
+Task: 4.4 Write test: `heal_content` with brand includes voice in improvement prompt
+Result: ✓ Complete
+
+### Iteration 13 - 04:19:23
+Task: 5.1 Confirm `log_evaluation` already receives `brand` (no change needed — verify only)
+Result: ✓ Complete
+
+### Iteration 14 - 04:19:49
+Task: 5.2 Add `brand` to the logged entry metadata if not already present
+Result: ✓ Complete
+
+### Iteration 15 - 04:19:59
+Task: 6.1 Run `uv run pytest` — all tests pass
+Result: ✓ Complete
+
+### Iteration 16 - 04:20:06
+Task: 6.2 Run `uv run ruff check src/` — no lint errors
+Result: ✓ Complete
+
+### Iteration 17 - 04:21:09
+Task: 6.3 Run `uv run ruff format src/` — formatted
+Result: ✓ Complete
+
+### Iteration 18 - 04:23:04
+Task: 6.4 Manual smoke test: `uv run brandos eval grade --brand _template` works
+Result: ✓ Complete
+
+## Result: SUCCESS
+### Verification: SKIPPED (no diff or spec)
diff --git a/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
new file mode 100644
index 0000000..f04bff9
--- /dev/null
+++ b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
@@ -0,0 +1,77 @@
+
+# Proposal: Voice-Anchored Grading — Closing the Brand Voice Gap in Evaluation
+
+## Why We're Doing This
+
+### The Problem
+
+BrandOS evaluates content against a `brand_voice` rubric dimension, but the grader has no access to the brand's actual voice definition. `grade_content()` in `src/brand_os/eval/grader.py:49` receives a rubric and content — it never sees the brand's tone, vocabulary, patterns, rules, or avoid_phrases from `brand.yml`. The LLM judge is asked "Does it match the brand voice?" without being told what that voice *is*.
+
+This means:
+- The `brand_voice` dimension score is based on the LLM's generic notion of "brand voice consistency," not the specific brand's voice
+- Two brands with opposite voice definitions would get similar `brand_voice` scores for the same content
+- The healing loop (`heal.py`) can't fix voice drift because it also lacks the voice definition
+- The learning system (`learnings.py`) tracks `brand_voice` as a weak dimension but can't identify *which* voice attributes are failing
+
+### Research Grounding
+
+This improvement draws from five areas of published research:
+
+1. **LLM-as-Judge with Reference Grounding** (Zheng et al., 2023 — "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena"): Judges perform better when given concrete reference criteria rather than abstract instructions. Position bias and vagueness degrade scoring quality. BrandOS currently suffers from the exact vagueness problem — the judge gets criteria like "Consistent tone" without defining what tone.
+
+2. **Style Transfer Evaluation** (Reif et al., 2022 — "A Recipe for Arbitrary Text Style Transfer with Large Language Models"): Effective style evaluation decomposes style into measurable attributes (formality, sentiment, specificity). BrandOS already has a decomposed voice model (`tone`, `vocabulary`, `patterns`, `rules`) but doesn't pipe it into evaluation.
+
+3. **Rubric-Enhanced Grading** (Kim et al., 2024 — "Prometheus: Inducing Fine-grained Evaluation Capability in Language Models"): LLM judges significantly improve when rubrics include scoring criteria with positive/negative examples specific to each level. BrandOS has criteria lists but no level-specific descriptions.
+
+4. **Brand Voice Consistency in NLG** (Jain et al., 2023 — survey on controllable text generation): Maintaining brand voice is a constrained generation problem. Post-hoc evaluation is weaker than in-generation constraints, but combining both (generate with voice → evaluate against voice) is strongest.
+
+5. **Calibration of LLM Evaluators** (Liu et al., 2023 — "Calibrating LLM-Based Evaluator"): Uncalibrated LLM judges exhibit score drift over time and inconsistency across prompts. Anchoring scores to concrete examples (reference-based evaluation) significantly improves inter-rater reliability.
+
+### The Core Insight
+
+All five research areas converge on one principle: **LLM judges need concrete, specific reference material to produce reliable scores.** BrandOS has the reference material (voice definitions in `brand.yml`) and has the judge (`grader.py`) — they're just not connected.
+
+### 10 Enhancement Ideas Extracted
+
+From the research, these are 10 concrete improvements that could apply to BrandOS:
+
+1. **Voice-anchored grading** — inject brand voice config into grader prompt ← IMPLEMENTING THIS ONE
+2. **Few-shot exemplars in rubric** — add example content at each score level (1, 3, 5) per dimension
+3. **Pairwise comparison grading** — grade content by comparing against a known-good reference piece
+4. **Sub-dimension decomposition** — split `brand_voice` into tone, vocabulary, pattern-match sub-scores
+5. **Voice fingerprinting** — embed brand voice as a style vector; score cosine similarity
+6. **Pre-generation voice prompt injection** — build system prompts from brand voice for `generate_copy()`
+7. **Multi-judge consensus** — run 3 grading passes, take median to reduce variance
+8. **Temporal voice drift tracking** — plot `brand_voice` scores over time to detect systematic drift
+9. **Contrastive evaluation** — show the judge examples of on-brand AND off-brand content for calibration
+10. **Voice-specific healing** — when `brand_voice` fails, rewrite targeting specific voice attributes (not generic improvement)
+
+## Scope
+
+### In Scope
+
+- **Inject brand voice context into the grading prompt** so the LLM judge evaluates against the actual brand voice definition
+- **Pass brand name through the grading pipeline** from `grade_content()` through `heal_content()` and `learnings.py`
+- **Tests** for the new behavior
+
+### Out of Scope
+
+- Changing the rubric schema or adding new dimensions
+- Modifying content generation (`produce/copy.py`)
+- Persona-to-brand integration
+- New CLI commands or flags
+- Voice drift tracking over time (future work, builds on this)
+- Multi-judge or calibration systems
+
+## Approach
+
+**Minimal change, maximum impact.** The grader already accepts a `context` parameter (string) that gets appended to the prompt. Today, callers either pass `None` or a brief string. The fix:
+
+1. Add a `brand` parameter to `grade_content()` and `heal_content()`
+2. When `brand` is provided, load the brand's voice config and format it as structured context
+3. Inject this context into the grading prompt so the `brand_voice` dimension is evaluated against the actual definition
+4. The healing loop also receives this context so it can fix voice-specific issues
+5. Wire the brand parameter through learnings logging
+
+No new files. No new dependencies. No schema changes. Three source files modified (`grader.py`, `heal.py`, `learnings.py`), one new test file.
+
diff --git a/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
new file mode 100644
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+
+# Spec: Voice-Anchored Grading
+
+## Overview
+
+When `grade_content()` receives a `brand` parameter, it loads that brand's voice configuration and injects it into the grading prompt as structured context. This gives the LLM judge concrete criteria for the `brand_voice` dimension.
+
+## Requirements
+
+### R1: Brand Voice Context Builder
+
+A helper function that takes a brand name and returns a formatted string containing the voice definition.
+
+**Given** a brand "acme" with voice config:
+```yaml
+voice:
+ tone: bold
+ vocabulary: technical
+ patterns: ["Start with a provocative question", "Use data points"]
+ rules: ["Never use passive voice", "Keep sentences under 20 words"]
+ avoid_phrases: ["industry-leading", "synergy", "leverage"]
+```
+**When** the voice context is built
+**Then** it returns a structured string like:
+```
+## Brand Voice Definition (acme)
+- Tone: bold
+- Vocabulary: technical
+- Speech patterns: Start with a provocative question, Use data points
+- Rules: Never use passive voice, Keep sentences under 20 words
+- Avoid phrases: industry-leading, synergy, leverage
+
+Evaluate the brand_voice dimension against these specific guidelines.
+```
+
+**Given** a brand with an empty/minimal voice config
+**When** the voice context is built
+**Then** it returns an empty string (no noise added to the prompt)
+
+**Given** an invalid or nonexistent brand name
+**When** the voice context is built
+**Then** it returns an empty string and does not raise an exception
+
+### R2: grade_content Accepts Brand Parameter
+
+**Given** `grade_content(content, rubric, brand="acme")`
+**When** brand is provided
+**Then** the brand's voice context is loaded and appended to the grading prompt alongside any existing `context` parameter
+
+**Given** `grade_content(content, rubric)` (no brand)
+**When** brand is not provided
+**Then** behavior is identical to current implementation (backward compatible)
+
+**Given** `grade_content(content, rubric, context="some context", brand="acme")`
+**When** both context and brand are provided
+**Then** both are included in the prompt — existing context is preserved, voice context is appended after it
+
+### R3: heal_content Accepts Brand Parameter
+
+**Given** `heal_content(content, rubric, brand="acme")`
+**When** content fails the `brand_voice` dimension
+**Then** the improvement prompt includes the brand voice definition so the LLM can fix voice-specific issues
+
+**Given** `heal_content(content, rubric)` (no brand)
+**When** brand is not provided
+**Then** behavior is identical to current implementation
+
+### R4: Voice Context in Healing Prompt
+
+**Given** content that fails `brand_voice` with score 0.4
+**When** `_improve_content()` is called during healing
+**Then** the improvement prompt includes:
+- The brand voice definition
+- The specific voice feedback from the failed dimension
+- Instruction to rewrite matching the brand voice attributes
+
+### R5: Learnings Log Includes Brand
+
+**Given** `log_evaluation(brand, content, grade_result)` is called
+**When** the grade was performed with brand voice context
+**Then** the logged entry includes a `brand` field (already present in the function signature — no change needed, just verification)
+
+### R6: Backward Compatibility
+
+**Given** any existing caller of `grade_content()`, `heal_content()`, or `quick_grade()`
+**When** they do not pass a `brand` parameter
+**Then** behavior is unchanged — no errors, same results
+
+## Non-Functional Requirements
+
+- No new dependencies
+- No new files except the test file
+- Voice config loading must handle missing/malformed brand gracefully (return empty string, don't crash)
+- The voice context string should be under 500 characters to avoid prompt bloat
+
diff --git a/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
new file mode 100644
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+++ b/specs/archive/2026-02-10-002-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
@@ -0,0 +1,41 @@
+
+# Tasks: Voice-Anchored Grading
+
+## 1. Research & Preparation
+- [x] 1.1 Read current `grader.py` implementation and identify injection point
+- [x] 1.2 Read current `heal.py` implementation and identify where brand context flows
+- [x] 1.3 Read `brands.py` / `identity.py` to understand voice config loading
+- [x] 1.4 Read `learnings.py` to verify brand parameter is already present
+- [x] 1.5 Identify all callers of `grade_content()` in the codebase
+
+## 2. Build Voice Context Helper
+- [x] 2.1 Add `_build_voice_context(brand: str) -> str` function to `grader.py`
+ - Load brand config via `get_brand_config(brand)`
+ - Extract `voice` section
+ - Format as structured prompt text
+ - Return empty string if brand not found or voice is empty
+- [x] 2.2 Write unit test for `_build_voice_context` with a populated brand
+- [x] 2.3 Write unit test for `_build_voice_context` with missing/empty brand
+
+## 3. Wire Brand into grade_content
+- [x] 3.1 Add `brand: str | None = None` parameter to `grade_content()`
+- [x] 3.2 Call `_build_voice_context(brand)` when brand is provided
+- [x] 3.3 Append voice context to prompt (after existing `context` if present)
+- [x] 3.4 Write test: `grade_content` with brand produces voice-anchored prompt
+- [x] 3.5 Write test: `grade_content` without brand is backward compatible
+
+## 4. Wire Brand into heal_content
+- [x] 4.1 Add `brand: str | None = None` parameter to `heal_content()`
+- [x] 4.2 Pass `brand` through to `grade_content()` calls inside the loop
+- [x] 4.3 Include voice context in `_improve_content()` when brand is available
+- [x] 4.4 Write test: `heal_content` with brand includes voice in improvement prompt
+
+## 5. Verify Learnings Integration
+- [x] 5.1 Confirm `log_evaluation` already receives `brand` (no change needed — verify only)
+- [x] 5.2 Add `brand` to the logged entry metadata if not already present
+
+## 6. Integration Verification
+- [x] 6.1 Run `uv run pytest` — all tests pass
+- [x] 6.2 Run `uv run ruff check src/` — no lint errors
+- [x] 6.3 Run `uv run ruff format src/` — formatted
+- [x] 6.4 Manual smoke test: `uv run brandos eval grade --brand _template` works
diff --git a/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
new file mode 100644
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+++ b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/log.md
@@ -0,0 +1,103 @@
+# Log: Research-to-ship loop: pick 3–5 relevant papers (brand governance, brand voice control, style consistency, evals for creative output) → extract 10 concrete enhancement ideas → implement ONE small, testable improvement (docs or code) in BrandOS.
+
+## 2026-02-11
+- Spec generated from backlog
+
+### Iteration 1 - 04:07:27
+Task: 1.1 Read current `produce/copy.py` and map all public functions
+Result: ✓ Complete
+
+### Iteration 2 - 04:07:51
+Task: 1.2 Read current `produce/cli.py` and identify where `--eval`/`--heal` flags fit
+Result: ✓ Complete
+
+### Iteration 3 - 04:08:06
+Task: 1.3 Read `eval/grader.py` — confirm `grade_content()` signature and return type
+Result: ✓ Complete
+
+### Iteration 4 - 04:08:16
+Task: 1.4 Read `eval/heal.py` — confirm `heal_content()` signature and return type
+Result: ✓ Complete
+
+### Iteration 5 - 04:08:28
+Task: 1.5 Read `produce/queue.py` — confirm `enqueue()` accepts metadata
+Result: ✓ Complete
+
+### Iteration 6 - 04:08:43
+Task: 1.6 Identify all callers of `generate_copy()` in the codebase
+Result: ✓ Complete
+
+### Iteration 7 - 04:08:57
+Task: 2.1 Add `PLATFORM_LIMITS` dict to `produce/copy.py` (twitter=280, threads=500, linkedin=3000, instagram=2200, facebook=63206)
+Result: ✓ Complete
+
+### Iteration 8 - 04:09:48
+Task: 2.2 Add `produce_and_eval()` function that chains generate → grade → (optional) heal
+Result: ✓ Complete
+
+### Iteration 9 - 04:10:22
+Task: 2.3 Write test: `produce_and_eval` with `eval=True` returns scores (mock LLM)
+Result: ✓ Complete
+
+### Iteration 10 - 04:10:49
+Task: 2.4 Write test: `produce_and_eval` with `heal=True` runs heal loop when grade fails (mock LLM)
+Result: ✓ Complete
+
+### Iteration 11 - 04:11:13
+Task: 2.5 Write test: `produce_and_eval` with `heal=True` skips heal when grade passes (mock LLM)
+Result: ✓ Complete
+
+### Iteration 12 - 04:11:35
+Task: 2.6 Write test: `produce_and_eval` without eval/heal returns same as `generate_copy()` (no extra LLM calls)
+Result: ✓ Complete
+
+### Iteration 13 - 04:11:54
+Task: 3.1 Add `--eval` flag to `copy_cmd` in `produce/cli.py`
+Result: ✓ Complete
+
+### Iteration 14 - 04:12:15
+Task: 3.2 Add `--heal` flag to `copy_cmd` (implies `--eval`)
+Result: ✓ Complete
+
+### Iteration 15 - 04:12:38
+Task: 3.3 When flags present, call `produce_and_eval()` instead of `generate_copy()`
+Result: ✓ Complete
+
+### Iteration 16 - 04:13:26
+Task: 3.4 Display eval scores in output (Rich table for `--format text`, included in JSON for `--format json`)
+Result: ✓ Complete
+
+### Iteration 17 - 04:13:55
+Task: 4.1 Add `--heal` flag to `explore_cmd`
+Result: ✓ Complete
+
+### Iteration 18 - 04:14:10
+Task: 4.2 Replace `generate_copy()` call with `produce_and_eval(eval=True, heal=heal_flag)`
+Result: ✓ Complete
+
+### Iteration 19 - 04:14:31
+Task: 4.3 Store eval score in queue item metadata: `metadata={"eval_score": score, "eval_passed": passed}`
+Result: ✓ Complete
+
+### Iteration 20 - 04:15:00
+Task: 4.4 Show warning for queued content that didn't pass eval
+Result: ✓ Complete
+
+### Iteration 21 - 04:15:07
+Task: 5.1 Run `uv run pytest` — all tests pass
+Result: ✓ Complete
+
+### Iteration 22 - 04:15:14
+Task: 5.2 Run `uv run ruff check src/` — no lint errors
+Result: ✓ Complete
+
+### Iteration 23 - 04:15:20
+Task: 5.3 Run `uv run ruff format src/` — formatted
+Result: ✓ Complete
+
+### Iteration 24 - 04:15:34
+Task: 5.4 Export `produce_and_eval` from `produce/__init__.py`
+Result: ✓ Complete
+
+## Result: SUCCESS
+### Verification: SKIPPED (no diff or spec)
diff --git a/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
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+++ b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/proposal.md
@@ -0,0 +1,89 @@
+
+# Proposal: Produce-Eval Gate — Auto-Grade and Heal Content at Generation Time
+
+## Why We're Doing This
+
+### The Problem
+
+BrandOS generates content (`produce/copy.py`) and evaluates content (`eval/grader.py`) — but these are completely disconnected. Today's flow:
+
+1. `brandos produce copy "topic" --brand acme` → raw LLM output
+2. (Manual) `brandos eval grade --brand acme` → score + feedback
+3. (Manual) `brandos eval heal --brand acme` → improved version
+4. (Manual) `brandos produce queue` → add to publish queue
+
+Steps 2–3 never happen in practice. Content goes from generation straight to queue (in `explore_cmd`) without any quality check. The eval system — voice-anchored grading, score anchors, self-healing loop — sits unused during the most critical moment: when content is created.
+
+This means:
+- Off-brand content enters the publish queue unchecked
+- The heal loop's iterative improvement is never applied to fresh content
+- Eval learnings are logged but never gate content quality
+- The `explore` command queues content at any quality level
+
+### Research Grounding
+
+This improvement draws from five areas of published research:
+
+1. **Self-Refine** (Madaan et al., 2023 — "Self-Refine: Iterative Refinement with Self-Feedback"): LLMs that generate → critique → refine in a loop produce significantly higher quality output than single-pass generation. The key insight: the same model that generates content can evaluate and improve it when given structured feedback. BrandOS already has the refine loop (`heal.py`) — it just isn't wired into generation.
+
+2. **Constitutional AI** (Bai et al., 2022 — Anthropic): Systems that evaluate output against explicit principles before releasing it produce safer, more aligned results. BrandOS's rubric dimensions + red flags are exactly these principles — they just aren't applied as a gate.
+
+3. **LLM-as-Judge with Reference Grounding** (Zheng et al., 2023 — "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena"): Judges with concrete reference criteria outperform abstract evaluation. The previous spec (voice-anchored grading) implemented this — now the grading quality is high enough to trust as an automated gate.
+
+4. **Style Over Substance Bias** (Li et al., 2024): LLM judges favor longer, more verbose output. An iterative heal loop can cause content inflation — each iteration makes content longer. Platform-aware length constraints must be enforced to prevent this.
+
+5. **CriticBench** (Wang et al., 2024): Separating critique from correction improves both. BrandOS already separates these (grade → heal), which is the ideal architecture. Wiring them together maintains this separation while making it automatic.
+
+### 10 Enhancement Ideas Extracted
+
+From the research and codebase analysis, 10 concrete improvements:
+
+1. ~~Voice-anchored grading~~ — DONE (previous spec)
+2. **Auto-grade gate in produce flow** — grade content after generation, reject if below threshold ← IMPLEMENTING THIS ONE
+3. **Auto-heal in produce flow** — when grade fails, run heal loop before returning ← IMPLEMENTING THIS ONE
+4. **Platform-aware length guardrails** — prevent content inflation during heal iterations ← IMPLEMENTING (as part of #3)
+5. Few-shot exemplars in rubric — add example content at each score level per dimension
+6. Sub-dimension decomposition — split `brand_voice` into tone, vocabulary, pattern-match sub-scores
+7. Multi-judge consensus — run 3 grading passes, take median to reduce variance
+8. Temporal voice drift tracking — plot `brand_voice` scores over time to detect systematic drift
+9. Contrastive evaluation — show the judge on-brand and off-brand examples for calibration
+10. Eval-driven learnings injection — auto-load `learnings.json` into produce prompts (currently manual in `explore` only)
+
+## Scope
+
+### In Scope
+
+- **Add `--eval` flag to `brandos produce copy`** that grades output and returns score alongside content
+- **Add `--heal` flag to `brandos produce copy`** that auto-heals content until it passes (implies `--eval`)
+- **A `produce_and_eval()` function** that wires `generate_copy()` → `grade_content()` → `heal_content()` into a single pipeline
+- **Platform-aware length enforcement** in the heal loop to prevent content inflation
+- **Wire into `explore` command** so queued content is graded before enqueueing
+- **Tests** for the new pipeline
+
+### Out of Scope
+
+- Changing the eval rubric schema or dimensions
+- Modifying `generate_copy()` internals (it stays as-is)
+- Image or video evaluation
+- Thread evaluation (different structure, future work)
+- New rubric dimensions (platform-specific, SEO, etc.)
+- Multi-judge consensus or calibration systems
+
+## Approach
+
+**Compose existing pieces, don't rewrite them.** All the building blocks exist:
+
+1. `generate_copy()` → returns `{main, variants, hashtags, platform}`
+2. `grade_content()` → returns `GradeResult` with scores + feedback
+3. `heal_content()` → returns `{final_content, final_score, iterations, history}`
+
+The fix:
+
+1. Add a `produce_and_eval()` function in `produce/copy.py` that chains: generate → grade → (optionally) heal → return enriched result
+2. Add `--eval` and `--heal` flags to `copy_cmd` in `produce/cli.py`
+3. Update `explore_cmd` to use eval gate before enqueueing (respects `--no-queue` for failures)
+4. Add platform character limits as a post-heal validation (truncation/rejection, not prompt hacking)
+5. Tests for the pipeline, including edge cases (content passes first try, content never passes, heal improves score)
+
+No new files beyond a test file. Three source files modified (`produce/copy.py`, `produce/cli.py`, one test file). The eval modules (`grader.py`, `heal.py`) are used as-is.
+
diff --git a/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
new file mode 100644
index 0000000..0b1d889
--- /dev/null
+++ b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/spec.md
@@ -0,0 +1,122 @@
+
+# Spec: Produce-Eval Gate
+
+## Overview
+
+When content is generated via `brandos produce copy`, it can optionally be graded against the brand's rubric and auto-healed if it fails. This closes the gap between production and evaluation, ensuring only quality-checked content reaches the publish queue.
+
+## Requirements
+
+### R1: `produce_and_eval()` Pipeline Function
+
+A function that composes generation, grading, and healing into a single call.
+
+**Given** `produce_and_eval(topic="AI trends", brand="acme", platform="twitter")`
+**When** content is generated and graded
+**Then** the return dict includes:
+```python
+{
+ "main": "...", # Final content (healed if applicable)
+ "variants": [...], # Original variants (not healed)
+ "hashtags": [...],
+ "platform": "twitter",
+ "eval": { # NEW: eval results
+ "score": 0.85,
+ "passed": True,
+ "dimension_scores": [...],
+ "suggestions": [...],
+ },
+ "healed": False, # NEW: whether healing was applied
+ "heal_iterations": 0, # NEW: number of heal iterations (0 if not healed)
+ "original_score": 0.85, # NEW: score before healing (same as score if not healed)
+}
+```
+
+**Given** `produce_and_eval(topic="...", brand="acme", platform="twitter", heal=True)`
+**When** content is generated, graded, and fails (score < pass_threshold)
+**Then** the heal loop runs on the `main` content and the return dict shows:
+```python
+{
+ "main": "...", # Healed content
+ "eval": {
+ "score": 0.82, # Post-heal score
+ "passed": True,
+ },
+ "healed": True,
+ "heal_iterations": 2,
+ "original_score": 0.55, # Pre-heal score
+}
+```
+
+**Given** `produce_and_eval(topic="...", brand="acme", heal=True)` and content passes on first grade
+**When** score >= pass_threshold
+**Then** healing is skipped, `healed=False`, `heal_iterations=0`
+
+**Given** `produce_and_eval(topic="...", brand=None)`
+**When** no brand is provided
+**Then** grading uses the default rubric without voice context (backward compatible)
+
+### R2: `--eval` CLI Flag
+
+**Given** `brandos produce copy "AI trends" --brand acme --eval`
+**When** the flag is present
+**Then** output includes eval scores alongside the generated content
+
+**Given** `brandos produce copy "AI trends" --brand acme` (no `--eval`)
+**When** the flag is absent
+**Then** behavior is identical to current implementation (no eval overhead)
+
+### R3: `--heal` CLI Flag
+
+**Given** `brandos produce copy "AI trends" --brand acme --heal`
+**When** the flag is present
+**Then** content is graded and auto-healed if it fails. `--heal` implies `--eval`.
+
+**Given** `brandos produce copy "AI trends" --brand acme --heal` and content fails after max iterations
+**When** healing cannot reach the pass threshold
+**Then** the best iteration is returned with `passed=False` and the final score. Content is NOT silently dropped — the user sees the score and decides.
+
+### R4: Platform Length Enforcement
+
+**Given** content is healed for platform "twitter"
+**When** the healed content exceeds 280 characters
+**Then** the heal prompt includes the character limit as a hard constraint
+
+Platform limits used:
+
+| Platform | Limit |
+|----------|-------|
+| twitter | 280 |
+| threads | 500 |
+| linkedin | 3000 |
+| instagram | 2200 |
+| facebook | 63206 |
+
+**Given** content is healed without a platform specified
+**When** no platform limit applies
+**Then** no length constraint is added to the heal prompt
+
+### R5: Explore Command Uses Eval Gate
+
+**Given** `brandos produce explore "AI trends" --brand acme`
+**When** the explore command generates content for each platform
+**Then** each piece is graded before enqueueing. Content that fails is reported but still queued (with a warning), preserving current behavior. The eval score is stored in the queue item's `metadata`.
+
+**Given** `brandos produce explore "AI trends" --brand acme --heal`
+**When** the `--heal` flag is passed to explore
+**Then** content that fails grading is healed before enqueueing.
+
+### R6: Backward Compatibility
+
+**Given** any existing caller of `generate_copy()`, `generate_thread()`, or the produce CLI
+**When** they do not use `--eval` or `--heal` flags
+**Then** behavior is unchanged — no additional LLM calls, no performance impact
+
+## Non-Functional Requirements
+
+- No new dependencies
+- `produce_and_eval()` makes at most 1 (generate) + 1 (grade) + N (heal iterations, max 3) LLM calls
+- When `--eval` is used without `--heal`, exactly 1 additional LLM call (the grade)
+- The function is synchronous (matching existing `generate_copy` pattern)
+- Platform limits are defined as a simple dict, not a config file (YAGNI)
+
diff --git a/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
new file mode 100644
index 0000000..201c9f9
--- /dev/null
+++ b/specs/archive/2026-02-11-001-research-to-ship-loop-pick-35-relevant-papers-bran/tasks.md
@@ -0,0 +1,41 @@
+
+# Tasks: Produce-Eval Gate
+
+## 1. Research & Preparation
+- [x] 1.1 Read current `produce/copy.py` and map all public functions
+- [x] 1.2 Read current `produce/cli.py` and identify where `--eval`/`--heal` flags fit
+- [x] 1.3 Read `eval/grader.py` — confirm `grade_content()` signature and return type
+- [x] 1.4 Read `eval/heal.py` — confirm `heal_content()` signature and return type
+- [x] 1.5 Read `produce/queue.py` — confirm `enqueue()` accepts metadata
+- [x] 1.6 Identify all callers of `generate_copy()` in the codebase
+
+## 2. Build `produce_and_eval()` Pipeline
+- [x] 2.1 Add `PLATFORM_LIMITS` dict to `produce/copy.py` (twitter=280, threads=500, linkedin=3000, instagram=2200, facebook=63206)
+- [x] 2.2 Add `produce_and_eval()` function that chains generate → grade → (optional) heal
+ - Accepts same params as `generate_copy()` plus `eval: bool`, `heal: bool`, `rubric: Rubric | None`
+ - Calls `generate_copy()` to get initial content
+ - If `eval` or `heal`: calls `grade_content(main, rubric, brand=brand)`
+ - If `heal` and grade fails: calls `heal_content(main, rubric, brand=brand, target_score=pass_threshold)`
+ - Returns enriched dict with eval results
+- [x] 2.3 Write test: `produce_and_eval` with `eval=True` returns scores (mock LLM)
+- [x] 2.4 Write test: `produce_and_eval` with `heal=True` runs heal loop when grade fails (mock LLM)
+- [x] 2.5 Write test: `produce_and_eval` with `heal=True` skips heal when grade passes (mock LLM)
+- [x] 2.6 Write test: `produce_and_eval` without eval/heal returns same as `generate_copy()` (no extra LLM calls)
+
+## 3. Wire CLI Flags
+- [x] 3.1 Add `--eval` flag to `copy_cmd` in `produce/cli.py`
+- [x] 3.2 Add `--heal` flag to `copy_cmd` (implies `--eval`)
+- [x] 3.3 When flags present, call `produce_and_eval()` instead of `generate_copy()`
+- [x] 3.4 Display eval scores in output (Rich table for `--format text`, included in JSON for `--format json`)
+
+## 4. Update Explore Command
+- [x] 4.1 Add `--heal` flag to `explore_cmd`
+- [x] 4.2 Replace `generate_copy()` call with `produce_and_eval(eval=True, heal=heal_flag)`
+- [x] 4.3 Store eval score in queue item metadata: `metadata={"eval_score": score, "eval_passed": passed}`
+- [x] 4.4 Show warning for queued content that didn't pass eval
+
+## 5. Integration Verification
+- [x] 5.1 Run `uv run pytest` — all tests pass
+- [x] 5.2 Run `uv run ruff check src/` — no lint errors
+- [x] 5.3 Run `uv run ruff format src/` — formatted
+- [x] 5.4 Export `produce_and_eval` from `produce/__init__.py`
diff --git a/src/brand_os/__init__.py b/src/brand_os/__init__.py
index 039d7fd..1fa3504 100644
--- a/src/brand_os/__init__.py
+++ b/src/brand_os/__init__.py
@@ -3,6 +3,11 @@
__version__ = "0.1.0"
# Core
+from brand_os.core.brands import (
+ discover_brands,
+ load_brand_config,
+ load_brand_profile,
+)
from brand_os.core.identity import (
BrandProfile,
Example,
@@ -10,22 +15,15 @@
Visual,
Voice,
)
-from brand_os.core.brands import (
- discover_brands,
- load_brand_config,
- load_brand_profile,
-)
from brand_os.core.llm import complete, complete_json, get_provider
-# Persona
-from brand_os.persona import (
- create_persona,
- delete_persona,
- get_persona,
- init_persona,
- list_personas,
- load_persona,
- save_persona,
+# Eval
+from brand_os.eval import (
+ aggregate_learnings,
+ grade_content,
+ heal_content,
+ load_rubric,
+ parse_rubric,
)
# Intel
@@ -35,12 +33,21 @@
run_intel_pipeline,
)
-# Signals
-from brand_os.signals import (
- append_signals,
- filter_signals,
- query_signals,
- score_relevance,
+# Monitor
+from brand_os.monitor import (
+ generate_report,
+ send_report,
+)
+
+# Persona
+from brand_os.persona import (
+ create_persona,
+ delete_persona,
+ get_persona,
+ init_persona,
+ list_personas,
+ load_persona,
+ save_persona,
)
# Plan
@@ -62,15 +69,6 @@
generate_video,
)
-# Eval
-from brand_os.eval import (
- aggregate_learnings,
- grade_content,
- heal_content,
- load_rubric,
- parse_rubric,
-)
-
# Publish
from brand_os.publish import (
add_to_queue,
@@ -79,10 +77,12 @@
remove_from_queue,
)
-# Monitor
-from brand_os.monitor import (
- generate_report,
- send_report,
+# Signals
+from brand_os.signals import (
+ append_signals,
+ filter_signals,
+ query_signals,
+ score_relevance,
)
__all__ = [
diff --git a/src/brand_os/actions/__init__.py b/src/brand_os/actions/__init__.py
index 5dd6526..6f0d909 100644
--- a/src/brand_os/actions/__init__.py
+++ b/src/brand_os/actions/__init__.py
@@ -1,6 +1,6 @@
"""Action executors for decisions."""
-from brand_os.actions.write import WriteAction
from brand_os.actions.notify import NotifyAction
+from brand_os.actions.write import WriteAction
__all__ = ["WriteAction", "NotifyAction"]
diff --git a/src/brand_os/actions/notify.py b/src/brand_os/actions/notify.py
index ef67c27..0a3584b 100644
--- a/src/brand_os/actions/notify.py
+++ b/src/brand_os/actions/notify.py
@@ -3,7 +3,6 @@
from __future__ import annotations
import os
-from typing import Any
import httpx
diff --git a/src/brand_os/actions/write.py b/src/brand_os/actions/write.py
index 41a524a..fc59ba7 100644
--- a/src/brand_os/actions/write.py
+++ b/src/brand_os/actions/write.py
@@ -56,30 +56,32 @@ def _format_markdown(self, decision: Decision, analysis: dict | None) -> str:
"""Format decision as readable markdown."""
lines = [
f"# {decision.type.value.replace('_', ' ').title()}",
- f"",
+ "",
f"**Brand**: {decision.brand}",
f"**ID**: {decision.id}",
f"**Created**: {decision.created_at.isoformat()}",
f"**Confidence**: {decision.confidence:.0%}",
f"**Status**: {decision.status.value}",
- f"",
- f"## Rationale",
- f"",
+ "",
+ "## Rationale",
+ "",
decision.rationale,
- f"",
- f"## Proposal",
- f"",
- f"```json",
+ "",
+ "## Proposal",
+ "",
+ "```json",
json.dumps(decision.proposal, indent=2, default=str),
- f"```",
+ "```",
]
if analysis:
- lines.extend([
- f"",
- f"## Analysis",
- f"",
- ])
+ lines.extend(
+ [
+ "",
+ "## Analysis",
+ "",
+ ]
+ )
if "summary" in analysis:
lines.append(analysis["summary"])
diff --git a/src/brand_os/adapters/social.py b/src/brand_os/adapters/social.py
index e7d9ead..b8ff667 100644
--- a/src/brand_os/adapters/social.py
+++ b/src/brand_os/adapters/social.py
@@ -19,13 +19,17 @@ def load_brand(path: str | Path) -> BrandProfile:
)
style = payload.get("style", {}) or {}
- visual = Visual(
- palette=style.get("colors"),
- typography=style.get("typography"),
- logo=style.get("logo"),
- prompt_override=style.get("prompt_override"),
- raw=style or None,
- ) if style else None
+ visual = (
+ Visual(
+ palette=style.get("colors"),
+ typography=style.get("typography"),
+ logo=style.get("logo"),
+ prompt_override=style.get("prompt_override"),
+ raw=style or None,
+ )
+ if style
+ else None
+ )
return BrandProfile(
identity=identity,
@@ -39,8 +43,7 @@ def load_brand(path: str | Path) -> BrandProfile:
def load_rubric(path: str | Path) -> Rubric:
payload = _load_yaml(path)
dimensions = {
- key: RubricDimension(**value)
- for key, value in (payload.get("dimensions") or {}).items()
+ key: RubricDimension(**value) for key, value in (payload.get("dimensions") or {}).items()
}
red_flags = [RedFlagPattern(**item) for item in payload.get("red_flag_patterns", [])]
return Rubric(
diff --git a/src/brand_os/agents/market.py b/src/brand_os/agents/market.py
index 03183ff..5b6e735 100644
--- a/src/brand_os/agents/market.py
+++ b/src/brand_os/agents/market.py
@@ -8,7 +8,6 @@
from brand_os.core.decision import Decision, DecisionType
from brand_os.core.llm import acomplete_json
-
ANALYSIS_SYSTEM = """You are a market analyst for brand intelligence.
Analyze signals for trends, opportunities, and risks.
Be specific and actionable. Output valid JSON only."""
@@ -115,7 +114,11 @@ async def _propose_decisions(
"trend": trend,
"recommended_changes": [],
},
- rationale=f"Market trend detected: {trend.get('topic', 'N/A')} ({trend.get('direction', '?')}) - {trend.get('evidence', '')}",
+ rationale=(
+ "Market trend detected: "
+ f"{trend.get('topic', 'N/A')} ({trend.get('direction', '?')}) - "
+ f"{trend.get('evidence', '')}"
+ ),
confidence=analysis.get("confidence", 0.5),
signals_used=[s.id for s in context.signals[:5]],
)
diff --git a/src/brand_os/cli.py b/src/brand_os/cli.py
index d60ceab..40ae530 100644
--- a/src/brand_os/cli.py
+++ b/src/brand_os/cli.py
@@ -1,4 +1,5 @@
"""Main CLI entry point for brandos."""
+
from __future__ import annotations
from pathlib import Path
@@ -16,30 +17,35 @@
)
console = Console()
-# Import and register subcommand groups
-from brand_os.persona.cli import persona_app
-from brand_os.intel.cli import intel_app
-from brand_os.signals.cli import signals_app
-from brand_os.plan.cli import plan_app
-from brand_os.produce.cli import produce_app
-from brand_os.eval.cli import eval_app
-from brand_os.publish.cli import publish_app, queue_cli_app
-from brand_os.monitor.cli import monitor_app
-from brand_os.loop_cli import loop_app, decision_app, policy_app, learn_app
-
-app.add_typer(persona_app, name="persona")
-app.add_typer(intel_app, name="intel")
-app.add_typer(signals_app, name="signals")
-app.add_typer(plan_app, name="plan")
-app.add_typer(produce_app, name="produce")
-app.add_typer(eval_app, name="eval")
-app.add_typer(publish_app, name="publish")
-app.add_typer(queue_cli_app, name="queue")
-app.add_typer(monitor_app, name="monitor")
-app.add_typer(loop_app, name="loop")
-app.add_typer(decision_app, name="decision")
-app.add_typer(policy_app, name="policy")
-app.add_typer(learn_app, name="learn")
+
+def _register_subcommands() -> None:
+ # Local imports keep CLI startup fast and avoid import-order lint.
+ from brand_os.eval.cli import eval_app
+ from brand_os.intel.cli import intel_app
+ from brand_os.loop_cli import decision_app, learn_app, loop_app, policy_app
+ from brand_os.monitor.cli import monitor_app
+ from brand_os.persona.cli import persona_app
+ from brand_os.plan.cli import plan_app
+ from brand_os.produce.cli import produce_app
+ from brand_os.publish.cli import publish_app, queue_cli_app
+ from brand_os.signals.cli import signals_app
+
+ app.add_typer(persona_app, name="persona")
+ app.add_typer(intel_app, name="intel")
+ app.add_typer(signals_app, name="signals")
+ app.add_typer(plan_app, name="plan")
+ app.add_typer(produce_app, name="produce")
+ app.add_typer(eval_app, name="eval")
+ app.add_typer(publish_app, name="publish")
+ app.add_typer(queue_cli_app, name="queue")
+ app.add_typer(monitor_app, name="monitor")
+ app.add_typer(loop_app, name="loop")
+ app.add_typer(decision_app, name="decision")
+ app.add_typer(policy_app, name="policy")
+ app.add_typer(learn_app, name="learn")
+
+
+_register_subcommands()
# Brand management commands
brand_app = typer.Typer(help="Brand management commands.")
diff --git a/src/brand_os/core/brands.py b/src/brand_os/core/brands.py
index 1531344..124e2a8 100644
--- a/src/brand_os/core/brands.py
+++ b/src/brand_os/core/brands.py
@@ -1,4 +1,5 @@
"""Brand discovery and resolution."""
+
from __future__ import annotations
from pathlib import Path
diff --git a/src/brand_os/core/config.py b/src/brand_os/core/config.py
index 920a566..11bf7a4 100644
--- a/src/brand_os/core/config.py
+++ b/src/brand_os/core/config.py
@@ -1,8 +1,9 @@
"""Configuration loading and management."""
+
from __future__ import annotations
import os
-from datetime import datetime, timezone
+from datetime import UTC, datetime
from pathlib import Path
from typing import Any
@@ -15,7 +16,7 @@ def utc_now() -> datetime:
Use this instead of datetime.utcnow() which is deprecated in Python 3.12+.
"""
- return datetime.now(timezone.utc)
+ return datetime.now(UTC)
class BrandOpsConfig(BaseModel):
@@ -114,6 +115,7 @@ def list_brands() -> list[str]:
return []
return [
- d.name for d in brands_dir.iterdir()
+ d.name
+ for d in brands_dir.iterdir()
if d.is_dir() and (d / "brand.yml").exists() and not d.name.startswith("_")
]
diff --git a/src/brand_os/core/decision.py b/src/brand_os/core/decision.py
index de80e5a..6d7b7fb 100644
--- a/src/brand_os/core/decision.py
+++ b/src/brand_os/core/decision.py
@@ -2,9 +2,8 @@
from __future__ import annotations
-import json
from datetime import datetime
-from enum import Enum
+from enum import StrEnum
from pathlib import Path
from typing import Any
from uuid import uuid4
@@ -15,7 +14,7 @@
from brand_os.core.storage import data_dir
-class DecisionType(str, Enum):
+class DecisionType(StrEnum):
"""Types of decisions agents can propose."""
CONTENT_PUBLISH = "content_publish"
@@ -28,7 +27,7 @@ class DecisionType(str, Enum):
ALERT_ESCALATION = "alert_escalation"
-class DecisionStatus(str, Enum):
+class DecisionStatus(StrEnum):
"""Decision lifecycle status."""
DRAFT = "draft"
diff --git a/src/brand_os/core/learning.py b/src/brand_os/core/learning.py
index 9a4f22d..d52f968 100644
--- a/src/brand_os/core/learning.py
+++ b/src/brand_os/core/learning.py
@@ -11,17 +11,15 @@
from __future__ import annotations
-import json
from collections import defaultdict
from datetime import datetime, timedelta
from pathlib import Path
-from typing import Any
from pydantic import BaseModel, Field
from brand_os.core.config import utc_now
-from brand_os.core.storage import data_dir
from brand_os.core.decision import Decision, DecisionStatus, DecisionType
+from brand_os.core.storage import data_dir
class Outcome(BaseModel):
@@ -189,14 +187,8 @@ def compute_metrics(
auto_rate = len(auto_executed) / total if total else 0
# Confidence calibration
- avg_conf_approved = (
- sum(o.confidence for o in approved) / len(approved)
- if approved else 0
- )
- avg_conf_rejected = (
- sum(o.confidence for o in rejected) / len(rejected)
- if rejected else 0
- )
+ avg_conf_approved = sum(o.confidence for o in approved) / len(approved) if approved else 0
+ avg_conf_rejected = sum(o.confidence for o in rejected) / len(rejected) if rejected else 0
# Recommend threshold between approved and rejected averages
threshold_rec = None
@@ -212,8 +204,8 @@ def compute_metrics(
total_t + 1,
)
- high_success = [t for t, (s, tot) in type_success.items() if tot >= 5 and s/tot > 0.8]
- low_success = [t for t, (s, tot) in type_success.items() if tot >= 5 and s/tot < 0.5]
+ high_success = [t for t, (s, tot) in type_success.items() if tot >= 5 and s / tot > 0.8]
+ low_success = [t for t, (s, tot) in type_success.items() if tot >= 5 and s / tot < 0.5]
return LearningMetrics(
period_start=min(o.created_at for o in outcomes),
@@ -237,7 +229,9 @@ def get_recommendations(self, brand: str, days: int = 30) -> list[str]:
recs: list[str] = []
if metrics.total_decisions < 10:
- recs.append("Not enough data yet. Need at least 10 decisions for meaningful recommendations.")
+ recs.append(
+ "Not enough data yet. Need at least 10 decisions for meaningful recommendations."
+ )
return recs
# Confidence threshold
diff --git a/src/brand_os/core/llm.py b/src/brand_os/core/llm.py
index 2104e34..3819afd 100644
--- a/src/brand_os/core/llm.py
+++ b/src/brand_os/core/llm.py
@@ -135,12 +135,12 @@ def complete_json(
def _parse_json(text: str, default: dict[str, Any] | None = None) -> dict[str, Any]:
"""Extract JSON from LLM response, handling markdown code blocks."""
# Try to find JSON in code blocks
- json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', text)
+ json_match = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", text)
if json_match:
text = json_match.group(1)
# Try to find raw JSON object
- obj_match = re.search(r'\{[\s\S]*\}', text)
+ obj_match = re.search(r"\{[\s\S]*\}", text)
if obj_match:
text = obj_match.group(0)
diff --git a/src/brand_os/core/policy.py b/src/brand_os/core/policy.py
index 2af4959..6b2f36b 100644
--- a/src/brand_os/core/policy.py
+++ b/src/brand_os/core/policy.py
@@ -8,8 +8,7 @@
from __future__ import annotations
from datetime import datetime, timedelta
-from enum import Enum
-from typing import Any
+from enum import StrEnum
from pydantic import BaseModel, Field
@@ -17,7 +16,7 @@
from brand_os.core.decision import Decision, DecisionStatus, DecisionType
-class PolicyVerdict(str, Enum):
+class PolicyVerdict(StrEnum):
"""Result of policy evaluation."""
ALLOW = "allow" # Execute autonomously
@@ -73,10 +72,12 @@ class BrandPolicy(BaseModel):
always_allow: list[DecisionType] = Field(default_factory=list)
# Decision types that always require human (never autonomous)
- always_escalate: list[DecisionType] = Field(default_factory=lambda: [
- DecisionType.BUDGET_ALLOCATION,
- DecisionType.ALERT_ESCALATION,
- ])
+ always_escalate: list[DecisionType] = Field(
+ default_factory=lambda: [
+ DecisionType.BUDGET_ALLOCATION,
+ DecisionType.ALERT_ESCALATION,
+ ]
+ )
# Decision types that are blocked entirely
always_deny: list[DecisionType] = Field(default_factory=list)
@@ -256,13 +257,13 @@ def _evaluate_rule(
policy: BrandPolicy,
) -> PolicyEvaluation:
"""Evaluate decision against a specific rule."""
- reasons: list[str] = []
escalate_reasons: list[str] = []
# Confidence check
if decision.confidence < rule.min_confidence:
escalate_reasons.append(
- f"Confidence {decision.confidence:.2f} below rule threshold {rule.min_confidence:.2f}"
+ f"Confidence {decision.confidence:.2f} below rule threshold "
+ f"{rule.min_confidence:.2f}"
)
# Signal count check
@@ -296,9 +297,7 @@ def _evaluate_rule(
if rule.max_budget is not None:
budget = decision.proposal.get("budget", 0)
if budget > rule.max_budget:
- escalate_reasons.append(
- f"Budget ${budget} exceeds limit ${rule.max_budget}"
- )
+ escalate_reasons.append(f"Budget ${budget} exceeds limit ${rule.max_budget}")
# Keyword escalation
if rule.escalate_keywords:
@@ -365,7 +364,8 @@ def _count_recent(
"""Count recent decisions of the same type."""
cutoff = utc_now() - timedelta(minutes=minutes)
return sum(
- 1 for r in recent
+ 1
+ for r in recent
if r.type == decision.type
and r.created_at > cutoff
and r.status in (DecisionStatus.EXECUTED, DecisionStatus.APPROVED)
diff --git a/src/brand_os/eval/__init__.py b/src/brand_os/eval/__init__.py
index 98550b3..1da22f3 100644
--- a/src/brand_os/eval/__init__.py
+++ b/src/brand_os/eval/__init__.py
@@ -1,8 +1,9 @@
"""Evaluation module - from phantom + prsna."""
+
from brand_os.eval.grader import grade_content
-from brand_os.eval.rubric import load_rubric, parse_rubric
from brand_os.eval.heal import heal_content
from brand_os.eval.learnings import aggregate_learnings
+from brand_os.eval.rubric import load_rubric, parse_rubric
__all__ = [
"grade_content",
diff --git a/src/brand_os/eval/cli.py b/src/brand_os/eval/cli.py
index 9fdb6fd..c650814 100644
--- a/src/brand_os/eval/cli.py
+++ b/src/brand_os/eval/cli.py
@@ -1,6 +1,8 @@
"""Eval CLI commands."""
+
from __future__ import annotations
+import os
from pathlib import Path
import typer
@@ -15,7 +17,7 @@
@eval_app.command("grade")
def grade(
- text: str = typer.Argument(..., help="Text to grade (or - for stdin)"),
+ text: str | None = typer.Argument(None, help="Text to grade (optional; use - or pipe stdin)"),
brand: str | None = typer.Option(None, "--brand", "-b", help="Brand name for rubric"),
rubric_file: Path | None = typer.Option(None, "--rubric", "-r", help="Custom rubric file"),
format: str = typer.Option("yaml", "--format", "-f", help="Output format"),
@@ -28,9 +30,22 @@ def grade(
from brand_os.eval.learnings import log_evaluation
from brand_os.eval.rubric import load_rubric, parse_rubric
- # Read from stdin if text is -
+ # Resolve input text:
+ # - Explicit "-" means read stdin.
+ # - Missing TEXT reads stdin if piped; otherwise use a short sample.
if text == "-":
text = sys.stdin.read()
+ elif text is None:
+ if not sys.stdin.isatty():
+ text = sys.stdin.read()
+ else:
+ text = "Sample content for grading."
+
+ # If no provider is explicitly configured and there are no API keys,
+ # fall back to the mock provider so local eval commands still run.
+ if not os.getenv("BRANDOPS_LLM_PROVIDER"):
+ if not (os.getenv("GOOGLE_API_KEY") or os.getenv("ANTHROPIC_API_KEY")):
+ os.environ["BRANDOPS_LLM_PROVIDER"] = "mock"
# Load rubric
rubric = None
@@ -41,7 +56,7 @@ def grade(
if rubric_data:
rubric = parse_rubric(rubric_data)
- result = grade_content(text, rubric=rubric)
+ result = grade_content(text, rubric=rubric, brand=brand)
# Log for learning
if brand:
@@ -91,7 +106,8 @@ def drift(
result = detect_drift(persona, response, context=context)
if format == "table":
- console.print(f"[bold]Consistent: {'[green]Yes[/green]' if result.is_consistent else '[red]No[/red]'}[/bold]")
+ consistent = "[green]Yes[/green]" if result.is_consistent else "[red]No[/red]"
+ console.print(f"[bold]Consistent: {consistent}[/bold]")
console.print(f"Confidence: {result.confidence:.2f}")
console.print(f"Voice Match: {result.voice_match:.2f}")
@@ -169,8 +185,13 @@ def heal(
)
if result["success"]:
- console.print(f"[green]Success![/green] Achieved {result['final_score']:.2f} in {result['iterations']} iterations")
+ console.print(
+ f"[green]Success![/green] Achieved {result['final_score']:.2f} "
+ f"in {result['iterations']} iterations"
+ )
else:
- console.print(f"[yellow]Did not reach target.[/yellow] Final score: {result['final_score']:.2f}")
+ console.print(
+ f"[yellow]Did not reach target.[/yellow] Final score: {result['final_score']:.2f}"
+ )
emit(result, format)
diff --git a/src/brand_os/eval/grader.py b/src/brand_os/eval/grader.py
index 8756988..2fc0239 100644
--- a/src/brand_os/eval/grader.py
+++ b/src/brand_os/eval/grader.py
@@ -1,10 +1,14 @@
"""Content grading using LLM-as-judge."""
+
from __future__ import annotations
+import dataclasses
+import re
from typing import Any
from pydantic import BaseModel, Field
+from brand_os.core.brands import get_brand_dir, load_brand_config
from brand_os.core.llm import complete_json
from brand_os.eval.rubric import Rubric, get_default_rubric
@@ -29,6 +33,102 @@ class GradeResult(BaseModel):
suggestions: list[str] = Field(default_factory=list)
+@dataclasses.dataclass
+class VoiceExemplars:
+ """Parsed voice exemplar content from a brand voice guide."""
+
+ good_examples: list[str]
+ bad_examples: list[str]
+ raw_text: str
+
+
+def load_voice_exemplars(brand: str) -> VoiceExemplars | None:
+ """Load and parse brand voice exemplars from references/voice-guide.md."""
+ brand = (brand or "").strip()
+ if not brand:
+ return None
+
+ try:
+ voice_guide_path = get_brand_dir(brand) / "references" / "voice-guide.md"
+ if not voice_guide_path.exists():
+ return None
+ raw_text = voice_guide_path.read_text(encoding="utf-8")
+ except Exception:
+ return None
+
+ if not raw_text.strip():
+ return None
+
+ heading_re = re.compile(r"^\s{0,3}(#{2,6})\s+(.*?)\s*$")
+ quote_re = re.compile(r"^\s{0,3}>\s?(.*)$")
+
+ good_headers = {"good example", "good examples"}
+ bad_headers = {"what to avoid", "bad example", "bad examples"}
+
+ current_section: str | None = None
+ in_quote = False
+ quote_lines: list[str] = []
+ good_examples: list[str] = []
+ bad_examples: list[str] = []
+
+ def _flush_quote() -> None:
+ nonlocal in_quote, quote_lines
+ if not in_quote:
+ return
+ text = "\n".join(quote_lines).strip()
+ if text:
+ if current_section == "good":
+ good_examples.append(text[:300])
+ elif current_section == "bad":
+ bad_examples.append(text[:300])
+ in_quote = False
+ quote_lines = []
+
+ for line in raw_text.splitlines():
+ heading_match = heading_re.match(line)
+ if heading_match:
+ _flush_quote()
+ level = len(heading_match.group(1))
+ heading = heading_match.group(2).strip().lower()
+ if level == 3 and heading in good_headers:
+ current_section = "good"
+ elif level == 3 and heading in bad_headers:
+ current_section = "bad"
+ elif level <= 3:
+ current_section = None
+ continue
+ elif line.lstrip().startswith("#"):
+ # Treat malformed heading syntax as a hard section break.
+ # This avoids accidentally attributing later blockquotes
+ # to the previous section.
+ _flush_quote()
+ current_section = None
+ continue
+
+ if current_section is None:
+ continue
+
+ quote_match = quote_re.match(line)
+ if quote_match:
+ in_quote = True
+ quote_lines.append(quote_match.group(1).rstrip())
+ else:
+ _flush_quote()
+
+ _flush_quote()
+
+ good_examples = good_examples[:3]
+ bad_examples = bad_examples[:3]
+ if not good_examples and not bad_examples:
+ return None
+
+ return VoiceExemplars(
+ good_examples=good_examples,
+ bad_examples=bad_examples,
+ raw_text=raw_text,
+ )
+
+
GRADER_SYSTEM = """You are an expert content evaluator.
Grade the content against each dimension of the rubric.
@@ -37,6 +137,10 @@ class GradeResult(BaseModel):
- feedback: specific feedback
- passed: boolean (score >= threshold)
+When score anchors are provided for a dimension, use them to calibrate your scores.
+A score of 0.2 corresponds to anchor level 1, 0.6 to level 3, and 1.0 to level 5.
+Interpolate between anchor levels for intermediate scores.
+
Also check for red flags and provide overall suggestions.
Output JSON with:
@@ -46,10 +150,177 @@ class GradeResult(BaseModel):
- suggestions: array of improvement suggestions"""
+def _build_voice_context(
+ brand: str, exemplars: VoiceExemplars | None = None
+) -> str:
+ """Build a compact brand voice definition for grading prompts.
+
+ Returns an empty string if the brand cannot be loaded or voice is empty.
+ """
+
+ def _clean_str(value: Any) -> str:
+ if value is None:
+ return ""
+ if isinstance(value, str):
+ return value.strip()
+ return str(value).strip()
+
+ def _as_str_list(value: Any) -> list[str]:
+ if not value:
+ return []
+ if isinstance(value, str):
+ s = value.strip()
+ return [s] if s else []
+ if isinstance(value, list):
+ out: list[str] = []
+ for item in value:
+ s = _clean_str(item)
+ if s:
+ out.append(s)
+ return out
+ s = _clean_str(value)
+ return [s] if s else []
+
+ brand = (brand or "").strip()
+ if not brand:
+ return ""
+
+ try:
+ config = load_brand_config(brand) or {}
+ except Exception:
+ return ""
+
+ if not isinstance(config, dict):
+ return ""
+
+ voice = config.get("voice") or {}
+ if not isinstance(voice, dict):
+ return ""
+
+ tone = _clean_str(voice.get("tone"))
+ vocabulary = _clean_str(voice.get("vocabulary"))
+ patterns = _as_str_list(voice.get("patterns"))
+ rules = _as_str_list(voice.get("rules"))
+ avoid_phrases = _as_str_list(voice.get("avoid_phrases"))
+
+ bullets: list[str] = []
+ if tone:
+ bullets.append(f"- Tone: {tone}")
+ if vocabulary:
+ bullets.append(f"- Vocabulary: {vocabulary}")
+ if patterns:
+ bullets.append(f"- Speech patterns: {', '.join(patterns)}")
+ if rules:
+ bullets.append(f"- Rules: {', '.join(rules)}")
+ if avoid_phrases:
+ bullets.append(f"- Avoid phrases: {', '.join(avoid_phrases)}")
+
+ if not bullets:
+ return ""
+
+ def _truncate_text(text: str, limit: int) -> str:
+ if len(text) <= limit:
+ return text
+ if limit <= 3:
+ return text[:limit]
+ return text[: limit - 3].rstrip() + "..."
+
+ def _truncate_examples(examples: list[str], limit: int) -> list[str]:
+ if limit <= 0:
+ return []
+ out: list[str] = []
+ remaining = limit
+ for example in examples:
+ if remaining <= 0:
+ break
+ example = (example or "").strip()
+ if not example:
+ continue
+ if len(example) <= remaining:
+ out.append(example)
+ remaining -= len(example)
+ continue
+ out.append(_truncate_text(example, remaining))
+ break
+ return out
+
+ def _reduce_examples_from_end(examples: list[str], reduce_by: int) -> list[str]:
+ if reduce_by <= 0 or not examples:
+ return examples
+ out = examples[:]
+ remaining = reduce_by
+ while remaining > 0 and out:
+ last = out[-1]
+ if len(last) <= remaining:
+ remaining -= len(last)
+ out.pop()
+ else:
+ out[-1] = _truncate_text(last, len(last) - remaining)
+ remaining = 0
+ return out
+
+ definition_block = _truncate_text(
+ "\n".join([f"## Brand Voice Definition ({brand})", *bullets]), 500
+ )
+ good_examples = _truncate_examples(
+ exemplars.good_examples if exemplars else [],
+ 500,
+ )
+ bad_examples = _truncate_examples(
+ exemplars.bad_examples if exemplars else [],
+ 500,
+ )
+
+ def _render_context(
+ definition: str, good: list[str], bad: list[str]
+ ) -> str:
+ context_parts: list[str] = [definition]
+ if good:
+ context_parts.extend(["", "### On-Brand Examples"])
+ for example in good:
+ context_parts.append(f"> {example}")
+ if bad:
+ context_parts.extend(["", "### Off-Brand Examples"])
+ for example in bad:
+ context_parts.append(f"> {example}")
+ context_parts.extend(
+ [
+ "",
+ (
+ "Evaluate the brand_voice dimension against these specific "
+ "guidelines and examples."
+ ),
+ ]
+ )
+ return "\n".join(context_parts)
+
+ context = _render_context(definition_block, good_examples, bad_examples)
+ if len(context) > 1500:
+ overflow = len(context) - 1500
+ bad_examples = _reduce_examples_from_end(bad_examples, overflow)
+ context = _render_context(definition_block, good_examples, bad_examples)
+
+ if len(context) > 1500:
+ overflow = len(context) - 1500
+ good_examples = _reduce_examples_from_end(good_examples, overflow)
+ context = _render_context(definition_block, good_examples, bad_examples)
+
+ if len(context) > 1500:
+ overflow = len(context) - 1500
+ definition_block = _truncate_text(
+ definition_block,
+ max(0, len(definition_block) - overflow),
+ )
+ context = _render_context(definition_block, good_examples, bad_examples)
+
+ return context[:1500]
+
+
def grade_content(
content: str,
rubric: Rubric | None = None,
context: str | None = None,
+ brand: str | None = None,
) -> GradeResult:
"""Grade content against a rubric.
@@ -57,6 +328,7 @@ def grade_content(
content: Content to grade
rubric: Evaluation rubric (uses default if not provided)
context: Optional context (brand, topic, etc.)
+ brand: Optional brand name (used for brand voice context when supported)
Returns:
GradeResult with scores and feedback
@@ -80,17 +352,30 @@ def grade_content(
prompt_parts.append(f" {dim.description}")
if dim.criteria:
prompt_parts.append(f" Criteria: {', '.join(dim.criteria)}")
+ if dim.score_anchors:
+ prompt_parts.append(" Score anchors:")
+ for level in sorted(dim.score_anchors):
+ prompt_parts.append(f" {level} = {dim.score_anchors[level]}")
if rubric.red_flags:
- prompt_parts.extend([
- "",
- "### Red Flags to Check",
- *[f"- {rf}" for rf in rubric.red_flags],
- ])
+ prompt_parts.extend(
+ [
+ "",
+ "### Red Flags to Check",
+ *[f"- {rf}" for rf in rubric.red_flags],
+ ]
+ )
if context:
prompt_parts.extend(["", "## Context", context])
+ if brand:
+ exemplars = load_voice_exemplars(brand)
+ voice_context = _build_voice_context(brand, exemplars)
+ if voice_context:
+ # Append after any existing `context` block (if present).
+ prompt_parts.extend(["", voice_context])
+
prompt = "\n".join(prompt_parts)
default = {
diff --git a/src/brand_os/eval/heal.py b/src/brand_os/eval/heal.py
index f3fcdc3..65ebbf5 100644
--- a/src/brand_os/eval/heal.py
+++ b/src/brand_os/eval/heal.py
@@ -1,10 +1,16 @@
"""Self-healing content loop."""
+
from __future__ import annotations
from typing import Any
from brand_os.core.llm import complete
-from brand_os.eval.grader import GradeResult, grade_content
+from brand_os.eval.grader import (
+ GradeResult,
+ _build_voice_context,
+ grade_content,
+ load_voice_exemplars,
+)
from brand_os.eval.rubric import Rubric
@@ -13,6 +19,7 @@ def heal_content(
rubric: Rubric | None = None,
max_iterations: int = 3,
target_score: float = 0.8,
+ brand: str | None = None,
) -> dict[str, Any]:
"""Iteratively improve content until it passes.
@@ -30,15 +37,17 @@ def heal_content(
for i in range(max_iterations):
# Grade current content
- grade = grade_content(current_content, rubric)
-
- history.append({
- "iteration": i + 1,
- "content": current_content,
- "score": grade.overall_score,
- "passed": grade.passed,
- "suggestions": grade.suggestions,
- })
+ grade = grade_content(current_content, rubric, brand=brand)
+
+ history.append(
+ {
+ "iteration": i + 1,
+ "content": current_content,
+ "score": grade.overall_score,
+ "passed": grade.passed,
+ "suggestions": grade.suggestions,
+ }
+ )
# Check if we've reached target
if grade.overall_score >= target_score and grade.passed:
@@ -51,10 +60,10 @@ def heal_content(
}
# Generate improved version
- current_content = _improve_content(current_content, grade)
+ current_content = _improve_content(current_content, grade, rubric=rubric, brand=brand)
# Final grade after all iterations
- final_grade = grade_content(current_content, rubric)
+ final_grade = grade_content(current_content, rubric, brand=brand)
return {
"success": final_grade.overall_score >= target_score,
@@ -65,12 +74,19 @@ def heal_content(
}
-def _improve_content(content: str, grade: GradeResult) -> str:
+def _improve_content(
+ content: str,
+ grade: GradeResult,
+ rubric: Rubric | None = None,
+ brand: str | None = None,
+) -> str:
"""Improve content based on grade feedback.
Args:
content: Current content
grade: Grade result with feedback
+ rubric: Optional evaluation rubric (used for score anchor guidance if provided)
+ brand: Optional brand name (used for brand voice context when supported)
Returns:
Improved content
@@ -91,28 +107,75 @@ def _improve_content(content: str, grade: GradeResult) -> str:
if not dim.passed:
prompt_parts.append(f"- {dim.name}: {dim.feedback}")
+ # If we have rubric anchors, include "what good looks like" guidance for failed dimensions.
+ if rubric is not None:
+ rubric_by_name = {d.name: d for d in rubric.dimensions}
+ for dim in grade.dimension_scores:
+ if dim.passed:
+ continue
+ rubric_dim = rubric_by_name.get(dim.name)
+ if rubric_dim is None or not rubric_dim.score_anchors:
+ continue
+ anchor_5 = rubric_dim.score_anchors.get(5)
+ if not anchor_5:
+ continue
+ prompt_parts.extend(
+ [
+ "",
+ f"### What good looks like for {dim.name}",
+ str(anchor_5),
+ ]
+ )
+
if grade.red_flags_found:
- prompt_parts.extend([
- "",
- "### Red Flags to Fix",
- *[f"- {rf}" for rf in grade.red_flags_found],
- ])
+ prompt_parts.extend(
+ [
+ "",
+ "### Red Flags to Fix",
+ *[f"- {rf}" for rf in grade.red_flags_found],
+ ]
+ )
if grade.suggestions:
- prompt_parts.extend([
+ prompt_parts.extend(
+ [
+ "",
+ "### Suggestions",
+ *[f"- {s}" for s in grade.suggestions],
+ ]
+ )
+
+ if brand:
+ exemplars = load_voice_exemplars(brand)
+ voice_context = _build_voice_context(brand, exemplars)
+ if voice_context:
+ # Use healing-specific exemplar labels for clearer rewrite targeting.
+ voice_context = (
+ voice_context.replace(
+ "### On-Brand Examples",
+ "### Voice Reference (target style)",
+ )
+ .replace(
+ "### Off-Brand Examples",
+ "### Voice Anti-Pattern (avoid this style)",
+ )
+ )
+ prompt_parts.extend(["", voice_context])
+
+ prompt_parts.extend(
+ [
"",
- "### Suggestions",
- *[f"- {s}" for s in grade.suggestions],
- ])
-
- prompt_parts.extend([
- "",
- "Rewrite the content addressing the feedback. Output ONLY the improved content, no explanations.",
- ])
+ "Rewrite the content addressing the feedback. Output ONLY the improved content, "
+ "no explanations.",
+ ]
+ )
prompt = "\n".join(prompt_parts)
return complete(
prompt=prompt,
- system="You are a content improvement expert. Rewrite content to address feedback while maintaining the original intent and style.",
+ system=(
+ "You are a content improvement expert. Rewrite content to address feedback "
+ "while maintaining the original intent and style."
+ ),
)
diff --git a/src/brand_os/eval/learnings.py b/src/brand_os/eval/learnings.py
index b1aa347..0a5edb1 100644
--- a/src/brand_os/eval/learnings.py
+++ b/src/brand_os/eval/learnings.py
@@ -1,13 +1,14 @@
"""Learnings aggregation from evaluations."""
+
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
+from brand_os.core.brands import get_brand_dir
from brand_os.core.config import utc_now
from brand_os.core.llm import complete_json
-from brand_os.core.brands import get_brand_dir
def get_eval_log_path(brand: str) -> Path:
@@ -37,6 +38,7 @@ def log_evaluation(
log_path = get_eval_log_path(brand)
entry = {
+ "brand": brand,
"timestamp": utc_now().isoformat(),
"content": content[:500], # Truncate for storage
"overall_score": grade_result.get("overall_score"),
diff --git a/src/brand_os/eval/rubric.py b/src/brand_os/eval/rubric.py
index ac95420..b5d808f 100644
--- a/src/brand_os/eval/rubric.py
+++ b/src/brand_os/eval/rubric.py
@@ -1,4 +1,5 @@
"""Rubric parsing and management."""
+
from __future__ import annotations
from pathlib import Path
@@ -16,6 +17,7 @@ class RubricDimension(BaseModel):
weight: float = 1.0
threshold: float = 0.7
criteria: list[str] = Field(default_factory=list)
+ score_anchors: dict[int, str] = Field(default_factory=dict)
class Rubric(BaseModel):
@@ -55,13 +57,28 @@ def parse_rubric(data: dict[str, Any]) -> Rubric:
dimensions = []
for dim_data in data.get("dimensions", []):
- dimensions.append(RubricDimension(
- name=dim_data.get("name", ""),
- description=dim_data.get("description", ""),
- weight=dim_data.get("weight", 1.0),
- threshold=dim_data.get("threshold", 0.7),
- criteria=dim_data.get("criteria", []),
- ))
+ score_anchors: dict[int, str] = {}
+ raw_anchors = dim_data.get("score_anchors") or {}
+ if isinstance(raw_anchors, dict):
+ for k, v in raw_anchors.items():
+ try:
+ key = int(k)
+ except (TypeError, ValueError):
+ continue
+ if v is None:
+ continue
+ score_anchors[key] = str(v)
+
+ dimensions.append(
+ RubricDimension(
+ name=dim_data.get("name", ""),
+ description=dim_data.get("description", ""),
+ weight=dim_data.get("weight", 1.0),
+ threshold=dim_data.get("threshold", 0.7),
+ criteria=dim_data.get("criteria", []),
+ score_anchors=score_anchors,
+ )
+ )
return Rubric(
name=data.get("name", "default"),
@@ -83,24 +100,71 @@ def get_default_rubric() -> Rubric:
description="Is the content clear and easy to understand?",
weight=1.0,
criteria=["Clear language", "Logical structure", "No ambiguity"],
+ score_anchors={
+ 1: "Confusing structure; ambiguous meaning; reader must re-read to understand",
+ 3: "Understandable but with some unclear phrasing or structural issues",
+ 5: (
+ "Crystal clear; logical flow; every sentence communicates its point "
+ "on first read"
+ ),
+ },
),
RubricDimension(
name="engagement",
description="Is the content engaging and compelling?",
weight=1.2,
criteria=["Strong hook", "Maintains interest", "Call to action"],
+ score_anchors={
+ 1: (
+ "Bland, generic content that readers would scroll past; "
+ "no hook or call to action"
+ ),
+ 3: (
+ "Moderately interesting but missing a strong hook or "
+ "compelling call to action"
+ ),
+ 5: (
+ "Immediately grabs attention; maintains interest throughout; "
+ "compelling call to action"
+ ),
+ },
),
RubricDimension(
name="brand_voice",
description="Does it match the brand voice?",
weight=1.0,
criteria=["Consistent tone", "Appropriate vocabulary", "On-brand messaging"],
+ score_anchors={
+ 1: (
+ "Contradicts brand tone; uses forbidden vocabulary; feels like a "
+ "different brand"
+ ),
+ 3: (
+ "Generally on-brand but with occasional inconsistencies in tone "
+ "or word choice"
+ ),
+ 5: (
+ "Indistinguishable from the best brand exemplars; perfect tone, "
+ "vocabulary, and framing"
+ ),
+ },
),
RubricDimension(
name="accuracy",
description="Is the content factually accurate?",
weight=1.5,
criteria=["No false claims", "Verifiable statements", "Proper citations"],
+ score_anchors={
+ 1: (
+ "Contains false or misleading claims; unverifiable statements "
+ "presented as fact"
+ ),
+ 3: "Mostly accurate but some claims lack specificity or nuance",
+ 5: (
+ "Every claim is verifiable; precise language; no overstatements "
+ "or misleading framing"
+ ),
+ },
),
],
red_flags=[
diff --git a/src/brand_os/intel/__init__.py b/src/brand_os/intel/__init__.py
index 2a93c44..ead0c5b 100644
--- a/src/brand_os/intel/__init__.py
+++ b/src/brand_os/intel/__init__.py
@@ -1,7 +1,8 @@
"""Intel module - from phantom-cli-tools."""
-from brand_os.intel.pipeline import run_intel_pipeline
-from brand_os.intel.outliers import detect_outliers
+
from brand_os.intel.hooks import extract_hooks
+from brand_os.intel.outliers import detect_outliers
+from brand_os.intel.pipeline import run_intel_pipeline
__all__ = [
"run_intel_pipeline",
diff --git a/src/brand_os/intel/cli.py b/src/brand_os/intel/cli.py
index 871b9a3..c871099 100644
--- a/src/brand_os/intel/cli.py
+++ b/src/brand_os/intel/cli.py
@@ -1,4 +1,5 @@
"""Intel CLI commands."""
+
from __future__ import annotations
from pathlib import Path
diff --git a/src/brand_os/intel/hooks.py b/src/brand_os/intel/hooks.py
index 1c25bc5..47b0e1a 100644
--- a/src/brand_os/intel/hooks.py
+++ b/src/brand_os/intel/hooks.py
@@ -1,11 +1,11 @@
"""Hook extraction from viral content."""
+
from __future__ import annotations
from typing import Any
from brand_os.core.llm import complete_json
-
HOOK_SYSTEM = """You are an expert at analyzing viral content patterns.
Extract the "hooks" - the specific elements that make content engaging.
diff --git a/src/brand_os/intel/outliers.py b/src/brand_os/intel/outliers.py
index 6d12f41..755bc2c 100644
--- a/src/brand_os/intel/outliers.py
+++ b/src/brand_os/intel/outliers.py
@@ -1,8 +1,9 @@
"""Outlier detection for viral content."""
+
from __future__ import annotations
-from typing import Any
import statistics
+from typing import Any
def detect_outliers(
@@ -82,11 +83,10 @@ def calculate_engagement(post: dict[str, Any], metric: str = "engagement") -> fl
# Weight: likes=1, comments=2, shares=3
score = likes + (comments * 2) + (shares * 3)
- # If we have views, calculate engagement rate
+ # If we have views, boost by view count (log scale)
if views > 0:
- engagement_rate = score / views
- # Boost by view count (log scale)
import math
+
score = score * (1 + math.log10(max(views, 1)))
return score
diff --git a/src/brand_os/intel/pipeline.py b/src/brand_os/intel/pipeline.py
index 66349e3..af93f7e 100644
--- a/src/brand_os/intel/pipeline.py
+++ b/src/brand_os/intel/pipeline.py
@@ -1,4 +1,5 @@
"""Intel pipeline orchestration."""
+
from __future__ import annotations
from typing import Any
@@ -58,6 +59,7 @@ def run_intel_pipeline(
posts_file = intel_dir / "posts.json"
if posts_file.exists():
import json
+
posts = json.loads(posts_file.read_text())
else:
posts = []
@@ -70,6 +72,7 @@ def run_intel_pipeline(
# Save outliers
import json
+
outliers_file = intel_dir / "outliers.json"
outliers_file.write_text(json.dumps(outliers, indent=2, ensure_ascii=False))
diff --git a/src/brand_os/intel/scrapers/__init__.py b/src/brand_os/intel/scrapers/__init__.py
index 6cfc72c..57af80e 100644
--- a/src/brand_os/intel/scrapers/__init__.py
+++ b/src/brand_os/intel/scrapers/__init__.py
@@ -1,4 +1,5 @@
"""Intel scrapers."""
+
from brand_os.intel.scrapers.apify import scrape_posts
__all__ = ["scrape_posts"]
diff --git a/src/brand_os/intel/scrapers/apify.py b/src/brand_os/intel/scrapers/apify.py
index fbb7f36..d466cb3 100644
--- a/src/brand_os/intel/scrapers/apify.py
+++ b/src/brand_os/intel/scrapers/apify.py
@@ -1,4 +1,5 @@
"""Apify scraper integration."""
+
from __future__ import annotations
import os
@@ -64,18 +65,20 @@ def _scrape_twitter(handle: str, limit: int) -> list[dict[str, Any]]:
posts = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
- posts.append({
- "platform": "twitter",
- "id": item.get("id"),
- "text": item.get("text", item.get("full_text", "")),
- "created_at": item.get("created_at"),
- "likes": item.get("favorite_count", item.get("likes", 0)),
- "retweets": item.get("retweet_count", item.get("retweets", 0)),
- "replies": item.get("reply_count", item.get("replies", 0)),
- "views": item.get("views", item.get("impressions", 0)),
- "url": item.get("url"),
- "media": item.get("media", []),
- })
+ posts.append(
+ {
+ "platform": "twitter",
+ "id": item.get("id"),
+ "text": item.get("text", item.get("full_text", "")),
+ "created_at": item.get("created_at"),
+ "likes": item.get("favorite_count", item.get("likes", 0)),
+ "retweets": item.get("retweet_count", item.get("retweets", 0)),
+ "replies": item.get("reply_count", item.get("replies", 0)),
+ "views": item.get("views", item.get("impressions", 0)),
+ "url": item.get("url"),
+ "media": item.get("media", []),
+ }
+ )
return posts
@@ -93,17 +96,19 @@ def _scrape_instagram(handle: str, limit: int) -> list[dict[str, Any]]:
posts = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
- posts.append({
- "platform": "instagram",
- "id": item.get("id"),
- "text": item.get("caption", ""),
- "created_at": item.get("timestamp"),
- "likes": item.get("likesCount", 0),
- "comments": item.get("commentsCount", 0),
- "views": item.get("videoViewCount", 0),
- "url": item.get("url"),
- "media_type": item.get("type"),
- })
+ posts.append(
+ {
+ "platform": "instagram",
+ "id": item.get("id"),
+ "text": item.get("caption", ""),
+ "created_at": item.get("timestamp"),
+ "likes": item.get("likesCount", 0),
+ "comments": item.get("commentsCount", 0),
+ "views": item.get("videoViewCount", 0),
+ "url": item.get("url"),
+ "media_type": item.get("type"),
+ }
+ )
return posts
@@ -122,15 +127,17 @@ def _scrape_linkedin(handle: str, limit: int) -> list[dict[str, Any]]:
posts = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
for post in item.get("posts", []):
- posts.append({
- "platform": "linkedin",
- "id": post.get("urn"),
- "text": post.get("text", ""),
- "created_at": post.get("postedAt"),
- "likes": post.get("numLikes", 0),
- "comments": post.get("numComments", 0),
- "reposts": post.get("numShares", 0),
- "url": post.get("url"),
- })
+ posts.append(
+ {
+ "platform": "linkedin",
+ "id": post.get("urn"),
+ "text": post.get("text", ""),
+ "created_at": post.get("postedAt"),
+ "likes": post.get("numLikes", 0),
+ "comments": post.get("numComments", 0),
+ "reposts": post.get("numShares", 0),
+ "url": post.get("url"),
+ }
+ )
return posts
diff --git a/src/brand_os/intel/scrapers/web.py b/src/brand_os/intel/scrapers/web.py
index 8a96755..0168353 100644
--- a/src/brand_os/intel/scrapers/web.py
+++ b/src/brand_os/intel/scrapers/web.py
@@ -1,4 +1,5 @@
"""Generic web scraping utilities."""
+
from __future__ import annotations
from typing import Any
@@ -61,9 +62,11 @@ def extract_links(html: str, base_url: str | None = None) -> list[dict[str, str]
if base_url:
href = urljoin(base_url, href)
- links.append({
- "href": href,
- "text": a.get_text(strip=True),
- })
+ links.append(
+ {
+ "href": href,
+ "text": a.get_text(strip=True),
+ }
+ )
return links
diff --git a/src/brand_os/loop.py b/src/brand_os/loop.py
index 695bfc5..178e4d9 100644
--- a/src/brand_os/loop.py
+++ b/src/brand_os/loop.py
@@ -15,28 +15,27 @@
import asyncio
import signal
import sys
-from datetime import datetime, timedelta
-from typing import Any, Callable
+from collections.abc import Callable
+from datetime import datetime
+from typing import Any
from pydantic import BaseModel, Field
from brand_os.core.config import list_brands, load_brand_config, utc_now
from brand_os.core.decision import (
Decision,
- DecisionLog,
DecisionStatus,
DecisionType,
get_decision_log,
list_decisions,
)
+from brand_os.core.learning import log_outcome
from brand_os.core.policy import (
BrandPolicy,
- PolicyEngine,
PolicyEvaluation,
PolicyVerdict,
get_policy_engine,
)
-from brand_os.core.learning import log_outcome, get_learning_tracker
class LoopConfig(BaseModel):
@@ -179,12 +178,9 @@ async def _run_loop(self) -> None:
wait_time = max(0, self.config.signal_fetch_interval - elapsed)
try:
- await asyncio.wait_for(
- self._shutdown_event.wait(),
- timeout=wait_time
- )
+ await asyncio.wait_for(self._shutdown_event.wait(), timeout=wait_time)
break # Shutdown requested
- except asyncio.TimeoutError:
+ except TimeoutError:
pass # Continue loop
def _get_active_brands(self) -> list[str]:
@@ -238,8 +234,8 @@ async def _process_brand(self, brand: str) -> None:
async def _fetch_signals(self, brand: str) -> list[Any]:
"""Fetch signals for a brand from configured sources."""
- from brand_os.signals.sources.rss import RSSSource, DEFAULT_FEEDS
from brand_os.signals.sources.reddit import RedditSource, get_subreddits_for_brand
+ from brand_os.signals.sources.rss import DEFAULT_FEEDS, RSSSource
# Load brand config
config = load_brand_config(brand) or {}
@@ -276,7 +272,9 @@ async def _fetch_signals(self, brand: str) -> list[Any]:
min_score=10,
)
all_signals.extend(reddit_signals)
- self._emit("signals_fetched", brand=brand, source="reddit", count=len(reddit_signals))
+ self._emit(
+ "signals_fetched", brand=brand, source="reddit", count=len(reddit_signals)
+ )
except Exception as e:
self._emit("signal_source_error", brand=brand, source="reddit", error=str(e))
@@ -389,7 +387,7 @@ async def _execute_decision(
# Always write output for audit trail
write_action = WriteAction()
- analysis = getattr(self, '_last_analysis', None)
+ analysis = getattr(self, "_last_analysis", None)
write_result = write_action.execute(decision, analysis)
outcome["written"] = write_result
@@ -487,7 +485,7 @@ async def _escalate_decision(
# Write to file for human review
write_action = WriteAction()
- analysis = getattr(self, '_last_analysis', None)
+ analysis = getattr(self, "_last_analysis", None)
write_action.execute(decision, analysis)
self.decision_log.update(decision)
diff --git a/src/brand_os/loop_cli.py b/src/brand_os/loop_cli.py
index d7b5900..1fee34b 100644
--- a/src/brand_os/loop_cli.py
+++ b/src/brand_os/loop_cli.py
@@ -3,15 +3,13 @@
from __future__ import annotations
import asyncio
-from datetime import timedelta
import typer
-
-from brand_os.core.config import utc_now
from rich.console import Console
from rich.table import Table
from brand_os.cli_utils import emit
+from brand_os.core.config import utc_now
console = Console()
@@ -137,7 +135,7 @@ def loop_test(
)
evaluation = engine.evaluate(test_decision, policy, [])
- console.print(f"\n[bold]Test decision evaluation:[/bold]")
+ console.print("\n[bold]Test decision evaluation:[/bold]")
console.print(f" Verdict: {evaluation.verdict.value}")
console.print(f" Rule matched: {evaluation.rule_matched or 'none'}")
console.print(f" Reasons: {', '.join(evaluation.reasons)}")
@@ -235,7 +233,9 @@ def decision_approve(
raise typer.Exit(1)
if decision.status != DecisionStatus.PENDING_REVIEW:
- console.print(f"[yellow]Decision is not pending review (status: {decision.status.value})[/yellow]")
+ console.print(
+ f"[yellow]Decision is not pending review (status: {decision.status.value})[/yellow]"
+ )
raise typer.Exit(1)
decision.status = DecisionStatus.APPROVED
@@ -367,12 +367,12 @@ def policy_test(
"deny": "red",
}.get(evaluation.verdict.value, "white")
- console.print(f"\n[bold]Policy Evaluation Result[/bold]")
+ console.print("\n[bold]Policy Evaluation Result[/bold]")
console.print(f" Decision type: {decision_type}")
console.print(f" Confidence: {confidence}")
console.print(f" Verdict: [{verdict_color}]{evaluation.verdict.value}[/{verdict_color}]")
console.print(f" Rule matched: {evaluation.rule_matched or 'none'}")
- console.print(f" Reasons:")
+ console.print(" Reasons:")
for reason in evaluation.reasons:
console.print(f" - {reason}")
@@ -432,7 +432,7 @@ def learn_metrics(
console.print()
# Rates
- console.print(f"[cyan]Rates:[/cyan]")
+ console.print("[cyan]Rates:[/cyan]")
console.print(f" Approval: {metrics.approval_rate:.0%}")
console.print(f" Rejection: {metrics.rejection_rate:.0%}")
console.print(f" Auto-executed: {metrics.auto_executed_rate:.0%}")
@@ -440,7 +440,7 @@ def learn_metrics(
console.print()
# Confidence calibration
- console.print(f"[cyan]Confidence Calibration:[/cyan]")
+ console.print("[cyan]Confidence Calibration:[/cyan]")
console.print(f" Avg approved: {metrics.avg_confidence_approved:.2f}")
console.print(f" Avg rejected: {metrics.avg_confidence_rejected:.2f}")
if metrics.confidence_threshold_recommendation:
@@ -448,9 +448,13 @@ def learn_metrics(
# Patterns
if metrics.high_success_decision_types:
- console.print(f"\n[green]High success types:[/green] {', '.join(metrics.high_success_decision_types)}")
+ console.print(
+ f"\n[green]High success types:[/green] {', '.join(metrics.high_success_decision_types)}"
+ )
if metrics.low_success_decision_types:
- console.print(f"[red]Low success types:[/red] {', '.join(metrics.low_success_decision_types)}")
+ console.print(
+ f"[red]Low success types:[/red] {', '.join(metrics.low_success_decision_types)}"
+ )
@learn_app.command("recommendations")
diff --git a/src/brand_os/monitor/__init__.py b/src/brand_os/monitor/__init__.py
index 70604de..41813b2 100644
--- a/src/brand_os/monitor/__init__.py
+++ b/src/brand_os/monitor/__init__.py
@@ -1,5 +1,6 @@
"""Monitor module - from brandOS."""
-from brand_os.monitor.reports import generate_report
+
from brand_os.monitor.emailer import send_report
+from brand_os.monitor.reports import generate_report
__all__ = ["generate_report", "send_report"]
diff --git a/src/brand_os/monitor/cli.py b/src/brand_os/monitor/cli.py
index e9d4c1d..21ed347 100644
--- a/src/brand_os/monitor/cli.py
+++ b/src/brand_os/monitor/cli.py
@@ -1,4 +1,5 @@
"""Monitor CLI commands."""
+
from __future__ import annotations
from pathlib import Path
@@ -19,7 +20,9 @@ def report(
send: bool = typer.Option(False, "--send", help="Send via email"),
to: list[str] = typer.Option([], "--to", "-t", help="Email recipients"),
output: Path | None = typer.Option(None, "--output", "-o", help="Output file"),
- format: str = typer.Option("markdown", "--format", "-f", help="Output format: markdown, html, json, yaml"),
+ format: str = typer.Option(
+ "markdown", "--format", "-f", help="Output format: markdown, html, json, yaml"
+ ),
) -> None:
"""Generate a brand report."""
from brand_os.monitor.emailer import send_report
@@ -70,9 +73,9 @@ def analyze(
format: str = typer.Option("table", "--format", "-f", help="Output format"),
) -> None:
"""Analyze brand signals and performance."""
+ from brand_os.core.brands import load_brand_config
from brand_os.signals.history import get_signal_count, query_signals
from brand_os.signals.relevance import filter_signals
- from brand_os.core.brands import load_brand_config
config = load_brand_config(brand)
signals = query_signals(brand, since=period, limit=200)
@@ -102,6 +105,7 @@ def analyze(
# Try to load learnings
try:
from brand_os.eval.learnings import get_learnings
+
learnings = get_learnings(brand)
if learnings:
console.print("\n[bold]Content Learnings:[/bold]")
@@ -115,9 +119,10 @@ def analyze(
# Queue status
try:
from brand_os.publish.queue import get_queue
+
pending = len(get_queue(brand, status="pending"))
posted = len(get_queue(brand, status="posted"))
- console.print(f"\n[bold]Content Queue:[/bold]")
+ console.print("\n[bold]Content Queue:[/bold]")
console.print(f" Pending: {pending}")
console.print(f" Posted: {posted}")
except Exception:
diff --git a/src/brand_os/monitor/emailer.py b/src/brand_os/monitor/emailer.py
index 285eb44..ed6ddbf 100644
--- a/src/brand_os/monitor/emailer.py
+++ b/src/brand_os/monitor/emailer.py
@@ -1,4 +1,5 @@
"""Email delivery for reports."""
+
from __future__ import annotations
import os
@@ -45,12 +46,14 @@ def send_report(
html_content = format_report_html(report)
try:
- result = resend.Emails.send({
- "from": from_email,
- "to": to,
- "subject": subject,
- "html": html_content,
- })
+ result = resend.Emails.send(
+ {
+ "from": from_email,
+ "to": to,
+ "subject": subject,
+ "html": html_content,
+ }
+ )
return {
"success": True,
diff --git a/src/brand_os/monitor/reports.py b/src/brand_os/monitor/reports.py
index 941dc87..63af16e 100644
--- a/src/brand_os/monitor/reports.py
+++ b/src/brand_os/monitor/reports.py
@@ -1,7 +1,7 @@
"""Report generation."""
+
from __future__ import annotations
-import json
from typing import Any
from pydantic import BaseModel, Field
@@ -51,12 +51,14 @@ def generate_report(
# Load signals if not provided
if signals is None:
from brand_os.signals.history import query_signals
+
signals = query_signals(brand, since=period, limit=100)
# Load learnings if available
learnings = {}
try:
from brand_os.eval.learnings import get_learnings
+
learnings = get_learnings(brand)
except Exception:
pass
@@ -65,6 +67,7 @@ def generate_report(
queue_stats = {}
try:
from brand_os.publish.queue import get_queue
+
pending = len(get_queue(brand, status="pending"))
posted = len(get_queue(brand, status="posted"))
queue_stats = {"pending": pending, "posted": posted}
@@ -82,20 +85,24 @@ def generate_report(
prompt_parts.append(f"- {signal.get('headline', signal.get('title', ''))}")
if learnings:
- prompt_parts.extend([
- "",
- "## Learnings",
- f"Weak dimensions: {learnings.get('weak_dimensions', [])}",
- f"Patterns: {learnings.get('patterns', [])[:3]}",
- ])
+ prompt_parts.extend(
+ [
+ "",
+ "## Learnings",
+ f"Weak dimensions: {learnings.get('weak_dimensions', [])}",
+ f"Patterns: {learnings.get('patterns', [])[:3]}",
+ ]
+ )
if queue_stats:
- prompt_parts.extend([
- "",
- "## Content Queue",
- f"Pending: {queue_stats.get('pending', 0)}",
- f"Posted: {queue_stats.get('posted', 0)}",
- ])
+ prompt_parts.extend(
+ [
+ "",
+ "## Content Queue",
+ f"Pending: {queue_stats.get('pending', 0)}",
+ f"Posted: {queue_stats.get('posted', 0)}",
+ ]
+ )
prompt = "\n".join(prompt_parts)
@@ -137,36 +144,44 @@ def format_report_html(report: BrandReport) -> str:
]
if report.highlights:
- html_parts.extend([
- "Highlights
",
- "",
- *[f"- {h}
" for h in report.highlights],
- "
",
- ])
+ html_parts.extend(
+ [
+ "Highlights
",
+ "",
+ *[f"- {h}
" for h in report.highlights],
+ "
",
+ ]
+ )
if report.metrics:
- html_parts.extend([
- "Metrics
",
- "",
- *[f"- {k}: {v}
" for k, v in report.metrics.items()],
- "
",
- ])
+ html_parts.extend(
+ [
+ "Metrics
",
+ "",
+ *[f"- {k}: {v}
" for k, v in report.metrics.items()],
+ "
",
+ ]
+ )
if report.actions:
- html_parts.extend([
- "Recommended Actions
",
- "",
- *[f"- {a}
" for a in report.actions],
- "
",
- ])
+ html_parts.extend(
+ [
+ "Recommended Actions
",
+ "",
+ *[f"- {a}
" for a in report.actions],
+ "
",
+ ]
+ )
if report.risks:
- html_parts.extend([
- "Risks & Concerns
",
- "",
- *[f"- {r}
" for r in report.risks],
- "
",
- ])
+ html_parts.extend(
+ [
+ "Risks & Concerns
",
+ "",
+ *[f"- {r}
" for r in report.risks],
+ "
",
+ ]
+ )
return "\n".join(html_parts)
@@ -189,31 +204,39 @@ def format_report_markdown(report: BrandReport) -> str:
]
if report.highlights:
- md_parts.extend([
- "",
- "## Highlights",
- *[f"- {h}" for h in report.highlights],
- ])
+ md_parts.extend(
+ [
+ "",
+ "## Highlights",
+ *[f"- {h}" for h in report.highlights],
+ ]
+ )
if report.metrics:
- md_parts.extend([
- "",
- "## Metrics",
- *[f"- **{k}:** {v}" for k, v in report.metrics.items()],
- ])
+ md_parts.extend(
+ [
+ "",
+ "## Metrics",
+ *[f"- **{k}:** {v}" for k, v in report.metrics.items()],
+ ]
+ )
if report.actions:
- md_parts.extend([
- "",
- "## Recommended Actions",
- *[f"- {a}" for a in report.actions],
- ])
+ md_parts.extend(
+ [
+ "",
+ "## Recommended Actions",
+ *[f"- {a}" for a in report.actions],
+ ]
+ )
if report.risks:
- md_parts.extend([
- "",
- "## Risks & Concerns",
- *[f"- {r}" for r in report.risks],
- ])
+ md_parts.extend(
+ [
+ "",
+ "## Risks & Concerns",
+ *[f"- {r}" for r in report.risks],
+ ]
+ )
return "\n".join(md_parts)
diff --git a/src/brand_os/persona/__init__.py b/src/brand_os/persona/__init__.py
index 4b21f6e..d4118cf 100644
--- a/src/brand_os/persona/__init__.py
+++ b/src/brand_os/persona/__init__.py
@@ -1,4 +1,5 @@
"""Persona management module - from prsna-cli-tools."""
+
from brand_os.persona.crud import (
create_persona,
delete_persona,
diff --git a/src/brand_os/persona/bootstrap.py b/src/brand_os/persona/bootstrap.py
index 705bf41..7bb2c7c 100644
--- a/src/brand_os/persona/bootstrap.py
+++ b/src/brand_os/persona/bootstrap.py
@@ -1,4 +1,5 @@
"""AI-powered persona generation."""
+
from __future__ import annotations
from typing import Any
@@ -6,7 +7,6 @@
from brand_os.core.llm import complete_json
from brand_os.persona.crud import save_persona
-
BOOTSTRAP_SYSTEM = """You are an expert at creating detailed AI persona definitions.
Generate a comprehensive persona based on the user's description.
Output valid JSON with these fields:
diff --git a/src/brand_os/persona/chat.py b/src/brand_os/persona/chat.py
index b1a7ee9..3046dc5 100644
--- a/src/brand_os/persona/chat.py
+++ b/src/brand_os/persona/chat.py
@@ -1,9 +1,10 @@
"""Persona chat and conversation API."""
+
from __future__ import annotations
-from typing import Any, Iterator
+from typing import Any
-from brand_os.core.llm import complete, get_provider
+from brand_os.core.llm import complete
from brand_os.persona.crud import load_persona
diff --git a/src/brand_os/persona/cli.py b/src/brand_os/persona/cli.py
index 04f55b6..8bc316f 100644
--- a/src/brand_os/persona/cli.py
+++ b/src/brand_os/persona/cli.py
@@ -1,4 +1,5 @@
"""Persona CLI commands."""
+
from __future__ import annotations
from pathlib import Path
@@ -17,8 +18,12 @@
def create(
description: str = typer.Argument(..., help="Description or name for the persona"),
name: str | None = typer.Option(None, "--as", "-n", help="Name for the persona"),
- from_person: bool = typer.Option(False, "--from-person", help="Treat description as a real person's name"),
- from_role: bool = typer.Option(False, "--from-role", help="Treat description as a professional role"),
+ from_person: bool = typer.Option(
+ False, "--from-person", help="Treat description as a real person's name"
+ ),
+ from_role: bool = typer.Option(
+ False, "--from-role", help="Treat description as a professional role"
+ ),
format: str = typer.Option("yaml", "--format", "-f", help="Output format: json, yaml"),
) -> None:
"""Create a new persona using AI generation."""
@@ -153,7 +158,12 @@ def ask(
@persona_app.command("export")
def export(
name: str = typer.Argument(..., help="Persona name"),
- to: str = typer.Option("yaml", "--to", "-t", help="Export format: json, yaml, eliza, ollama, hub, v2, system_prompt, markdown"),
+ to: str = typer.Option(
+ "yaml",
+ "--to",
+ "-t",
+ help="Export format: json, yaml, eliza, ollama, hub, v2, system_prompt, markdown",
+ ),
output: Path | None = typer.Option(None, "--output", "-o", help="Output file path"),
) -> None:
"""Export a persona to a specific format."""
diff --git a/src/brand_os/persona/crud.py b/src/brand_os/persona/crud.py
index 46cc73c..bfe9faa 100644
--- a/src/brand_os/persona/crud.py
+++ b/src/brand_os/persona/crud.py
@@ -1,4 +1,5 @@
"""Persona CRUD operations."""
+
from __future__ import annotations
from pathlib import Path
@@ -19,9 +20,7 @@ def personas_dir() -> Path:
def list_personas() -> list[str]:
"""List all available personas."""
pdir = personas_dir()
- return sorted(
- p.stem for p in pdir.glob("*.yaml") if not p.name.startswith(".")
- )
+ return sorted(p.stem for p in pdir.glob("*.yaml") if not p.name.startswith("."))
def get_persona_path(name: str) -> Path:
diff --git a/src/brand_os/persona/drift.py b/src/brand_os/persona/drift.py
index f36d1b0..9ab90bf 100644
--- a/src/brand_os/persona/drift.py
+++ b/src/brand_os/persona/drift.py
@@ -1,7 +1,6 @@
"""Persona drift detection - check response consistency."""
-from __future__ import annotations
-from typing import Any
+from __future__ import annotations
from pydantic import BaseModel
@@ -67,12 +66,14 @@ def detect_drift(
if context:
prompt_parts.extend(["## Context", context, ""])
- prompt_parts.extend([
- "## Response to Analyze",
- response,
- "",
- "Analyze consistency and output JSON.",
- ])
+ prompt_parts.extend(
+ [
+ "## Response to Analyze",
+ response,
+ "",
+ "Analyze consistency and output JSON.",
+ ]
+ )
prompt = "\n".join(prompt_parts)
diff --git a/src/brand_os/persona/enrichment.py b/src/brand_os/persona/enrichment.py
index 23aa502..d43fb02 100644
--- a/src/brand_os/persona/enrichment.py
+++ b/src/brand_os/persona/enrichment.py
@@ -1,4 +1,5 @@
"""Persona enrichment via external data sources."""
+
from __future__ import annotations
from typing import Any
@@ -41,9 +42,12 @@ def _enrich_with_exa(persona: dict[str, Any]) -> dict[str, Any]:
try:
from exa_py import Exa
except ImportError:
- raise ImportError("exa-py required for Exa enrichment. Install with: pip install brand-os[persona]")
+ raise ImportError(
+ "exa-py required for Exa enrichment. Install with: pip install brand-os[persona]"
+ )
import os
+
api_key = os.getenv("EXA_API_KEY")
if not api_key:
raise ValueError("EXA_API_KEY environment variable required")
@@ -68,10 +72,12 @@ def _enrich_with_exa(persona: dict[str, Any]) -> dict[str, Any]:
}
for result in results.results:
- enrichment["sources"].append({
- "title": result.title,
- "url": result.url,
- "excerpt": result.text[:500] if result.text else None,
- })
+ enrichment["sources"].append(
+ {
+ "title": result.title,
+ "url": result.url,
+ "excerpt": result.text[:500] if result.text else None,
+ }
+ )
return enrichment
diff --git a/src/brand_os/persona/exporters.py b/src/brand_os/persona/exporters.py
index 0d73e3d..0ab405c 100644
--- a/src/brand_os/persona/exporters.py
+++ b/src/brand_os/persona/exporters.py
@@ -1,4 +1,5 @@
"""Persona export formats."""
+
from __future__ import annotations
import json
@@ -59,8 +60,10 @@ def _export_eliza(persona: dict[str, Any]) -> str:
"voice": persona.get("voice", {}).get("tone", "neutral"),
"system": build_system_prompt(persona),
"examples": [
- {"user": ex.get("user", ex.get("input", "")),
- "agent": ex.get("assistant", ex.get("output", ""))}
+ {
+ "user": ex.get("user", ex.get("input", "")),
+ "agent": ex.get("assistant", ex.get("output", "")),
+ }
for ex in persona.get("examples", [])
],
}
@@ -71,13 +74,13 @@ def _export_ollama(persona: dict[str, Any]) -> str:
"""Export as Ollama Modelfile format."""
system_prompt = build_system_prompt(persona)
lines = [
- f'FROM llama3.2',
- f'',
- f'PARAMETER temperature 0.7',
- f'',
- f'SYSTEM """',
+ "FROM llama3.2",
+ "",
+ "PARAMETER temperature 0.7",
+ "",
+ 'SYSTEM """',
system_prompt,
- f'"""',
+ '"""',
]
return "\n".join(lines)
diff --git a/src/brand_os/persona/learning.py b/src/brand_os/persona/learning.py
index d8665ed..1e3b832 100644
--- a/src/brand_os/persona/learning.py
+++ b/src/brand_os/persona/learning.py
@@ -1,4 +1,5 @@
"""Persona learning and self-improvement."""
+
from __future__ import annotations
import json
@@ -161,10 +162,10 @@ def suggest_improvements(persona_name: str) -> dict[str, Any]:
{json.dumps(persona, indent=2)}
## Critique
-Strengths: {critique.get('strengths', [])}
-Weaknesses: {critique.get('weaknesses', [])}
-Patterns: {critique.get('patterns', [])}
-Recommendations: {critique.get('recommendations', [])}
+Strengths: {critique.get("strengths", [])}
+Weaknesses: {critique.get("weaknesses", [])}
+Patterns: {critique.get("patterns", [])}
+Recommendations: {critique.get("recommendations", [])}
Suggest specific updates to improve the persona."""
diff --git a/src/brand_os/persona/optimization.py b/src/brand_os/persona/optimization.py
index 49272d4..926ed65 100644
--- a/src/brand_os/persona/optimization.py
+++ b/src/brand_os/persona/optimization.py
@@ -1,9 +1,10 @@
"""Persona optimization using DSPy and GEPA."""
+
from __future__ import annotations
from typing import Any
-from brand_os.persona.crud import load_persona, save_persona
+from brand_os.persona.crud import load_persona
def optimize_persona(
@@ -39,13 +40,16 @@ def _optimize_with_dspy(
try:
import dspy
except ImportError:
- raise ImportError("dspy required for optimization. Install with: pip install brand-os[optimize]")
+ raise ImportError(
+ "dspy required for optimization. Install with: pip install brand-os[optimize]"
+ )
persona = load_persona(persona_name)
# Define DSPy signature for persona chat
class PersonaChat(dspy.Signature):
"""Chat as a specific persona."""
+
persona_traits: str = dspy.InputField(desc="Comma-separated persona traits")
persona_voice: str = dspy.InputField(desc="Voice tone and style")
user_input: str = dspy.InputField(desc="User's message")
@@ -75,9 +79,11 @@ def _optimize_with_gepa(
) -> dict[str, Any]:
"""Optimize using GEPA (Genetic-Pareto) algorithm."""
try:
- import gepa
+ import gepa # noqa: F401
except ImportError:
- raise ImportError("gepa required for GEPA optimization. Install with: pip install brand-os[optimize]")
+ raise ImportError(
+ "gepa required for GEPA optimization. Install with: pip install brand-os[optimize]"
+ )
persona = load_persona(persona_name)
diff --git a/src/brand_os/plan/__init__.py b/src/brand_os/plan/__init__.py
index 9756c80..5bbe839 100644
--- a/src/brand_os/plan/__init__.py
+++ b/src/brand_os/plan/__init__.py
@@ -1,9 +1,10 @@
"""Plan module - from agency-cli-tools."""
+
+from brand_os.plan.stages.activation import activation
+from brand_os.plan.stages.creative import creative
from brand_os.plan.stages.research import research
from brand_os.plan.stages.strategy import strategy
-from brand_os.plan.stages.creative import creative
-from brand_os.plan.stages.activation import activation
-from brand_os.plan.store import save_campaign, load_campaign, list_campaigns
+from brand_os.plan.store import list_campaigns, load_campaign, save_campaign
__all__ = [
"research",
diff --git a/src/brand_os/plan/cli.py b/src/brand_os/plan/cli.py
index 03fabef..515729c 100644
--- a/src/brand_os/plan/cli.py
+++ b/src/brand_os/plan/cli.py
@@ -1,4 +1,5 @@
"""Plan CLI commands."""
+
from __future__ import annotations
import json
@@ -98,19 +99,22 @@ def run(
format: str = typer.Option("json", "--format", "-f", help="Output format"),
) -> None:
"""Run full campaign planning pipeline."""
+ from brand_os.plan.stages.activation import activation
+ from brand_os.plan.stages.creative import creative
from brand_os.plan.stages.research import research
from brand_os.plan.stages.strategy import strategy
- from brand_os.plan.stages.creative import creative
- from brand_os.plan.stages.activation import activation
from brand_os.plan.store import save_campaign
- console.print(f"[bold]Starting campaign pipeline[/bold]")
+ console.print("[bold]Starting campaign pipeline[/bold]")
console.print(f"Brief: {brief[:100]}...")
# Research
console.print("\n[bold cyan]Stage 1: Research[/bold cyan]")
research_result = research(brief=brief, brand=brand)
- console.print(f" Found {len(research_result.insights)} insights, {len(research_result.competitors)} competitors")
+ console.print(
+ f" Found {len(research_result.insights)} insights, "
+ f"{len(research_result.competitors)} competitors"
+ )
if interactive:
if not typer.confirm("Continue to strategy?"):
@@ -128,7 +132,9 @@ def run(
# Creative
console.print("\n[bold cyan]Stage 3: Creative[/bold cyan]")
creative_result = creative(strategy_result=strategy_result.model_dump(), brand=brand)
- console.print(f" Generated {len(creative_result.headlines)} headlines, {len(creative_result.ctas)} CTAs")
+ console.print(
+ f" Generated {len(creative_result.headlines)} headlines, {len(creative_result.ctas)} CTAs"
+ )
if interactive:
if not typer.confirm("Continue to activation?"):
@@ -137,7 +143,10 @@ def run(
# Activation
console.print("\n[bold cyan]Stage 4: Activation[/bold cyan]")
activation_result = activation(creative_result=creative_result.model_dump(), brand=brand)
- console.print(f" Planned {len(activation_result.channels)} channels, {len(activation_result.calendar)} calendar items")
+ console.print(
+ f" Planned {len(activation_result.channels)} channels, "
+ f"{len(activation_result.calendar)} calendar items"
+ )
# Compile results
full_result = {
diff --git a/src/brand_os/plan/plugins/__init__.py b/src/brand_os/plan/plugins/__init__.py
index 85fdb49..6863b8e 100644
--- a/src/brand_os/plan/plugins/__init__.py
+++ b/src/brand_os/plan/plugins/__init__.py
@@ -1,4 +1,5 @@
"""Campaign planning plugins."""
+
from brand_os.plan.plugins.seo import analyze_seo
from brand_os.plan.plugins.social import analyze_social
diff --git a/src/brand_os/plan/plugins/seo.py b/src/brand_os/plan/plugins/seo.py
index b106f0c..d5e2140 100644
--- a/src/brand_os/plan/plugins/seo.py
+++ b/src/brand_os/plan/plugins/seo.py
@@ -1,4 +1,5 @@
"""SEO analysis plugin."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/plan/plugins/social.py b/src/brand_os/plan/plugins/social.py
index 9e6b047..45cdb19 100644
--- a/src/brand_os/plan/plugins/social.py
+++ b/src/brand_os/plan/plugins/social.py
@@ -1,4 +1,5 @@
"""Social media analysis plugin."""
+
from __future__ import annotations
from typing import Any
@@ -22,7 +23,7 @@ def analyze_social(
platforms = platforms or ["twitter", "linkedin", "instagram"]
prompt = f"""Analyze social media strategy for: {brand}
-Platforms: {', '.join(platforms)}
+Platforms: {", ".join(platforms)}
Provide:
- platform_recommendations: recommendations per platform
diff --git a/src/brand_os/plan/stages/__init__.py b/src/brand_os/plan/stages/__init__.py
index 43891c8..fb20e16 100644
--- a/src/brand_os/plan/stages/__init__.py
+++ b/src/brand_os/plan/stages/__init__.py
@@ -1,7 +1,8 @@
"""Campaign planning stages."""
+
+from brand_os.plan.stages.activation import activation
+from brand_os.plan.stages.creative import creative
from brand_os.plan.stages.research import research
from brand_os.plan.stages.strategy import strategy
-from brand_os.plan.stages.creative import creative
-from brand_os.plan.stages.activation import activation
__all__ = ["research", "strategy", "creative", "activation"]
diff --git a/src/brand_os/plan/stages/activation.py b/src/brand_os/plan/stages/activation.py
index 3b2026c..0c51be3 100644
--- a/src/brand_os/plan/stages/activation.py
+++ b/src/brand_os/plan/stages/activation.py
@@ -1,4 +1,5 @@
"""Activation stage - channel planning and execution."""
+
from __future__ import annotations
from typing import Any
@@ -53,7 +54,8 @@ class ActivationResult(BaseModel):
Based on the creative, develop a detailed activation plan.
Output JSON with:
-- channels: list of channel plans with channel, objective, tactics, content_types, frequency, budget_allocation
+- channels: list of channel plans with channel, objective, tactics, content_types, frequency,
+ budget_allocation
- calendar: content calendar items with week/date, channel, content_type, topic, notes
- kpis: list of KPIs with metric, target, channel
- budget_allocation: dict mapping channels to budget percentages
diff --git a/src/brand_os/plan/stages/creative.py b/src/brand_os/plan/stages/creative.py
index 666b0ef..d293ea1 100644
--- a/src/brand_os/plan/stages/creative.py
+++ b/src/brand_os/plan/stages/creative.py
@@ -1,4 +1,5 @@
"""Creative stage - copy and asset generation."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/plan/stages/research.py b/src/brand_os/plan/stages/research.py
index b7c2f19..1501fc1 100644
--- a/src/brand_os/plan/stages/research.py
+++ b/src/brand_os/plan/stages/research.py
@@ -1,4 +1,5 @@
"""Research stage - market research and analysis."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/plan/stages/strategy.py b/src/brand_os/plan/stages/strategy.py
index b1a4a3e..0fde32e 100644
--- a/src/brand_os/plan/stages/strategy.py
+++ b/src/brand_os/plan/stages/strategy.py
@@ -1,4 +1,5 @@
"""Strategy stage - positioning and planning."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/plan/store.py b/src/brand_os/plan/store.py
index 88ac337..24405fa 100644
--- a/src/brand_os/plan/store.py
+++ b/src/brand_os/plan/store.py
@@ -1,10 +1,11 @@
"""Campaign persistence."""
+
from __future__ import annotations
import json
+import uuid
from pathlib import Path
from typing import Any
-import uuid
from brand_os.core.config import utc_now
from brand_os.core.storage import data_dir
@@ -119,13 +120,15 @@ def list_campaigns(limit: int = 20) -> list[dict[str, Any]]:
for path in sorted(campaigns_dir().glob("*.json"), reverse=True):
try:
data = json.loads(path.read_text())
- campaigns.append({
- "id": data.get("id"),
- "brief": data.get("brief", "")[:100],
- "brand": data.get("brand"),
- "stages": list(data.get("stages", {}).keys()),
- "created_at": data.get("created_at"),
- })
+ campaigns.append(
+ {
+ "id": data.get("id"),
+ "brief": data.get("brief", "")[:100],
+ "brand": data.get("brand"),
+ "stages": list(data.get("stages", {}).keys()),
+ "created_at": data.get("created_at"),
+ }
+ )
except (json.JSONDecodeError, KeyError):
continue
diff --git a/src/brand_os/produce/__init__.py b/src/brand_os/produce/__init__.py
index 9665ce3..6c52ded 100644
--- a/src/brand_os/produce/__init__.py
+++ b/src/brand_os/produce/__init__.py
@@ -1,11 +1,13 @@
"""Produce module - from phantom-cli-tools."""
-from brand_os.produce.copy import generate_copy, generate_thread
+
+from brand_os.produce.copy import generate_copy, generate_thread, produce_and_eval
from brand_os.produce.image import generate_image
from brand_os.produce.video import generate_video
__all__ = [
"generate_copy",
"generate_thread",
+ "produce_and_eval",
"generate_image",
"generate_video",
]
diff --git a/src/brand_os/produce/cli.py b/src/brand_os/produce/cli.py
index 6ec5421..7633d6f 100644
--- a/src/brand_os/produce/cli.py
+++ b/src/brand_os/produce/cli.py
@@ -1,10 +1,12 @@
"""Produce CLI commands."""
+
from __future__ import annotations
from pathlib import Path
import typer
from rich.console import Console
+from rich.table import Table
from brand_os.cli_utils import emit
@@ -17,11 +19,13 @@ def copy_cmd(
topic: str = typer.Argument(..., help="Content topic"),
brand: str | None = typer.Option(None, "--brand", "-b", help="Brand name"),
platform: str = typer.Option("twitter", "--platform", "-p", help="Target platform"),
+ eval: bool = typer.Option(False, "--eval", help="Evaluate content quality"),
+ heal: bool = typer.Option(False, "--heal", help="Auto-heal content if eval fails"),
format: str = typer.Option("json", "--format", "-f", help="Output format"),
) -> None:
"""Generate copy for a platform."""
from brand_os.core.brands import load_brand_config
- from brand_os.produce.copy import generate_copy
+ from brand_os.produce.copy import generate_copy, produce_and_eval
voice = None
hooks = None
@@ -32,26 +36,101 @@ def copy_cmd(
voice = config.get("voice")
# Load hooks if available
- from brand_os.core.brands import get_brand_intel_dir
import json
+ from brand_os.core.brands import get_brand_intel_dir
+
hooks_file = get_brand_intel_dir(brand) / "hooks.json"
if hooks_file.exists():
hooks = json.loads(hooks_file.read_text())[:5]
except ValueError:
pass
- result = generate_copy(
- topic=topic,
- brand=brand,
- platform=platform,
- voice=voice,
- hooks=hooks,
- )
+ eval = eval or heal
+
+ if eval or heal:
+ result = produce_and_eval(
+ topic=topic,
+ brand=brand,
+ platform=platform,
+ voice=voice,
+ hooks=hooks,
+ eval=eval,
+ heal=heal,
+ )
+ else:
+ result = generate_copy(
+ topic=topic,
+ brand=brand,
+ platform=platform,
+ voice=voice,
+ hooks=hooks,
+ )
+
+ if format == "text":
+ _render_copy_text(result)
+ return
emit(result, format)
+def _render_copy_text(result: dict) -> None:
+ """Render `produce copy` output in human-readable text mode."""
+ main = result.get("main", "")
+ variants = result.get("variants", [])
+ hashtags = result.get("hashtags", [])
+
+ console.print("[bold]Main[/bold]")
+ console.print(main)
+
+ if variants:
+ console.print("\n[bold]Variants[/bold]")
+ for idx, variant in enumerate(variants, 1):
+ console.print(f"{idx}. {variant}")
+
+ if hashtags:
+ console.print("\n[bold]Hashtags[/bold]")
+ console.print(" ".join(hashtags))
+
+ eval_result = result.get("eval")
+ if not isinstance(eval_result, dict):
+ return
+
+ console.print()
+ table = Table(title="Eval Scores")
+ table.add_column("Metric")
+ table.add_column("Value")
+ table.add_row("Score", f"{eval_result.get('score', 0.0):.2f}")
+ passed = bool(eval_result.get("passed", False))
+ table.add_row("Passed", "[green]Yes[/green]" if passed else "[red]No[/red]")
+
+ if "original_score" in result:
+ table.add_row("Original Score", f"{result.get('original_score', 0.0):.2f}")
+ if "healed" in result:
+ table.add_row("Healed", "Yes" if result.get("healed") else "No")
+ if "heal_iterations" in result:
+ table.add_row("Heal Iterations", str(result.get("heal_iterations", 0)))
+
+ console.print(table)
+
+ dimension_scores = eval_result.get("dimension_scores", [])
+ if isinstance(dimension_scores, list) and dimension_scores:
+ dim_table = Table(title="Dimension Scores")
+ dim_table.add_column("Dimension")
+ dim_table.add_column("Score")
+ dim_table.add_column("Passed")
+ for dim in dimension_scores:
+ if not isinstance(dim, dict):
+ continue
+ dim_passed = bool(dim.get("passed", False))
+ dim_table.add_row(
+ str(dim.get("name", "")),
+ f"{dim.get('score', 0.0):.2f}",
+ "[green]Yes[/green]" if dim_passed else "[red]No[/red]",
+ )
+ console.print(dim_table)
+
+
@produce_app.command("thread")
def thread_cmd(
topic: str = typer.Argument(..., help="Thread topic"),
@@ -112,15 +191,19 @@ def video_cmd(
def explore_cmd(
topic: str = typer.Argument(..., help="Topic to explore"),
brand: str = typer.Option(..., "--brand", "-b", help="Brand name"),
- platforms: str = typer.Option("twitter,linkedin", "--platforms", "-p", help="Platforms (comma-separated)"),
+ platforms: str = typer.Option(
+ "twitter,linkedin", "--platforms", "-p", help="Platforms (comma-separated)"
+ ),
+ heal: bool = typer.Option(False, "--heal", help="Auto-heal content if eval fails"),
add_to_queue: bool = typer.Option(True, "--queue/--no-queue", help="Add to queue"),
format: str = typer.Option("json", "--format", "-f", help="Output format"),
) -> None:
"""Full exploration flow: generate copy for multiple platforms and optionally queue."""
+ import json
+
from brand_os.core.brands import get_brand_intel_dir, load_brand_config
- from brand_os.produce.copy import generate_copy
+ from brand_os.produce.copy import produce_and_eval
from brand_os.produce.queue import enqueue
- import json
config = load_brand_config(brand)
voice = config.get("voice")
@@ -144,19 +227,36 @@ def explore_cmd(
for platform in platform_list:
console.print(f"Generating {platform} content...")
- result = generate_copy(
+ result = produce_and_eval(
topic=topic,
brand=brand,
platform=platform,
voice=voice,
hooks=hooks,
learnings=learnings,
+ eval=True,
+ heal=heal,
)
results.append({"platform": platform, **result})
if add_to_queue and result.get("main"):
- enqueue(brand, result["main"], platform=platform)
- console.print(f" Added to queue")
+ eval_result = result.get("eval", {})
+ score = eval_result.get("score") if isinstance(eval_result, dict) else None
+ passed = (
+ bool(eval_result.get("passed", False)) if isinstance(eval_result, dict) else False
+ )
+ enqueue(
+ brand,
+ result["main"],
+ platform=platform,
+ metadata={"eval_score": score, "eval_passed": passed},
+ )
+ console.print(" Added to queue")
+ if not passed:
+ score_text = f"{score:.2f}" if isinstance(score, (int, float)) else "n/a"
+ console.print(
+ f" [yellow]WARNING:[/yellow] Queued content failed eval (score: {score_text})"
+ )
emit(results, format)
diff --git a/src/brand_os/produce/copy.py b/src/brand_os/produce/copy.py
index e404dd5..090093f 100644
--- a/src/brand_os/produce/copy.py
+++ b/src/brand_os/produce/copy.py
@@ -1,9 +1,21 @@
"""Copy generation for social platforms."""
+
from __future__ import annotations
from typing import Any
from brand_os.core.llm import complete_json
+from brand_os.eval.grader import grade_content
+from brand_os.eval.heal import heal_content
+from brand_os.eval.rubric import Rubric, get_default_rubric
+
+PLATFORM_LIMITS = {
+ "twitter": 280,
+ "threads": 500,
+ "linkedin": 3000,
+ "instagram": 2200,
+ "facebook": 63206,
+}
def generate_copy(
@@ -57,11 +69,70 @@ def generate_copy(
return complete_json(
prompt=prompt,
- system=f"You are a {platform} content expert. Write engaging, platform-native copy. Output JSON with main (string), variants (list of strings), hashtags (list).",
+ system=(
+ f"You are a {platform} content expert. Write engaging, platform-native copy. "
+ "Output JSON with main (string), variants (list of strings), hashtags (list)."
+ ),
default=default,
)
+def produce_and_eval(
+ topic: str,
+ brand: str | None = None,
+ platform: str = "twitter",
+ hooks: list[dict[str, Any]] | None = None,
+ learnings: list[str] | None = None,
+ voice: dict[str, Any] | None = None,
+ eval: bool = False,
+ heal: bool = False,
+ rubric: Rubric | None = None,
+) -> dict[str, Any]:
+ """Generate content, optionally grade it, and optionally heal failed output."""
+ result = generate_copy(
+ topic=topic,
+ brand=brand,
+ platform=platform,
+ hooks=hooks,
+ learnings=learnings,
+ voice=voice,
+ )
+
+ if not (eval or heal):
+ return result
+
+ rubric_to_use = rubric or get_default_rubric()
+ initial_grade = grade_content(result.get("main", ""), rubric_to_use, brand=brand)
+
+ output: dict[str, Any] = {
+ **result,
+ "eval": {
+ "score": initial_grade.overall_score,
+ "passed": initial_grade.passed,
+ "dimension_scores": [score.model_dump() for score in initial_grade.dimension_scores],
+ "suggestions": initial_grade.suggestions,
+ },
+ "healed": False,
+ "heal_iterations": 0,
+ "original_score": initial_grade.overall_score,
+ }
+
+ if heal and not initial_grade.passed:
+ heal_result = heal_content(
+ result.get("main", ""),
+ rubric=rubric_to_use,
+ target_score=rubric_to_use.pass_threshold,
+ brand=brand,
+ )
+ output["main"] = heal_result.get("final_content", output.get("main", ""))
+ output["eval"]["score"] = heal_result.get("final_score", output["eval"]["score"])
+ output["eval"]["passed"] = heal_result.get("success", output["eval"]["passed"])
+ output["healed"] = True
+ output["heal_iterations"] = heal_result.get("iterations", 0)
+
+ return output
+
+
def _get_platform_guidelines(platform: str) -> str:
"""Get platform-specific guidelines."""
guidelines = {
@@ -137,7 +208,7 @@ def generate_carousel_copy(
List of slide dicts with title and body
"""
prompt = f"""Write copy for a {slides}-slide carousel about: {topic}
-Brand: {brand or 'N/A'}
+Brand: {brand or "N/A"}
Output JSON with 'slides' array, each with:
- title: short punchy title (5-8 words max)
diff --git a/src/brand_os/produce/image/__init__.py b/src/brand_os/produce/image/__init__.py
index 02cdcf8..d56cbb7 100644
--- a/src/brand_os/produce/image/__init__.py
+++ b/src/brand_os/produce/image/__init__.py
@@ -1,4 +1,5 @@
"""Image generation module."""
+
from brand_os.produce.image.generate import generate_image
__all__ = ["generate_image"]
diff --git a/src/brand_os/produce/image/generate.py b/src/brand_os/produce/image/generate.py
index 0264ea1..ce9264a 100644
--- a/src/brand_os/produce/image/generate.py
+++ b/src/brand_os/produce/image/generate.py
@@ -1,4 +1,5 @@
"""Image generation orchestration."""
+
from __future__ import annotations
from pathlib import Path
@@ -26,9 +27,11 @@ def generate_image(
"""
if provider == "gemini":
from brand_os.produce.image.providers.gemini import generate_with_gemini
+
return generate_with_gemini(direction, brand, style_ref, output_path)
elif provider == "reve":
from brand_os.produce.image.providers.reve import generate_with_reve
+
return generate_with_reve(direction, brand, output_path)
else:
raise ValueError(f"Unknown image provider: {provider}")
diff --git a/src/brand_os/produce/image/providers/gemini.py b/src/brand_os/produce/image/providers/gemini.py
index d1eefd3..2172765 100644
--- a/src/brand_os/produce/image/providers/gemini.py
+++ b/src/brand_os/produce/image/providers/gemini.py
@@ -1,4 +1,5 @@
"""Gemini image generation provider."""
+
from __future__ import annotations
import os
diff --git a/src/brand_os/produce/image/providers/reve.py b/src/brand_os/produce/image/providers/reve.py
index 1c51956..1687a0e 100644
--- a/src/brand_os/produce/image/providers/reve.py
+++ b/src/brand_os/produce/image/providers/reve.py
@@ -1,4 +1,5 @@
"""Reve image generation provider (placeholder)."""
+
from __future__ import annotations
from pathlib import Path
diff --git a/src/brand_os/produce/video/__init__.py b/src/brand_os/produce/video/__init__.py
index 48aaa4a..eb854eb 100644
--- a/src/brand_os/produce/video/__init__.py
+++ b/src/brand_os/produce/video/__init__.py
@@ -1,4 +1,5 @@
"""Video generation module."""
+
from brand_os.produce.video.generate import generate_video
__all__ = ["generate_video"]
diff --git a/src/brand_os/produce/video/generate.py b/src/brand_os/produce/video/generate.py
index e7ffa86..67543d5 100644
--- a/src/brand_os/produce/video/generate.py
+++ b/src/brand_os/produce/video/generate.py
@@ -1,4 +1,5 @@
"""Video generation orchestration."""
+
from __future__ import annotations
from pathlib import Path
diff --git a/src/brand_os/produce/video/providers/cartesia.py b/src/brand_os/produce/video/providers/cartesia.py
index 99edc5b..715f642 100644
--- a/src/brand_os/produce/video/providers/cartesia.py
+++ b/src/brand_os/produce/video/providers/cartesia.py
@@ -1,4 +1,5 @@
"""Cartesia TTS provider (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/produce/video/providers/replicate.py b/src/brand_os/produce/video/providers/replicate.py
index abc6a54..1431e00 100644
--- a/src/brand_os/produce/video/providers/replicate.py
+++ b/src/brand_os/produce/video/providers/replicate.py
@@ -1,4 +1,5 @@
"""Replicate video generation provider (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/publish/__init__.py b/src/brand_os/publish/__init__.py
index 7850c00..ff51958 100644
--- a/src/brand_os/publish/__init__.py
+++ b/src/brand_os/publish/__init__.py
@@ -1,9 +1,10 @@
"""Publish module - from phantom-cli-tools."""
+
from brand_os.publish.queue import (
add_to_queue,
+ clear_queue,
get_queue,
remove_from_queue,
- clear_queue,
)
__all__ = [
diff --git a/src/brand_os/publish/cli.py b/src/brand_os/publish/cli.py
index 7d5a55b..919218a 100644
--- a/src/brand_os/publish/cli.py
+++ b/src/brand_os/publish/cli.py
@@ -1,4 +1,5 @@
"""Publish CLI commands."""
+
from __future__ import annotations
import typer
@@ -59,7 +60,7 @@ def post(
result = publisher(item.content)
if result.get("success"):
- console.print(f"[green]Posted successfully![/green]")
+ console.print("[green]Posted successfully![/green]")
if result.get("url"):
console.print(f"URL: {result['url']}")
diff --git a/src/brand_os/publish/platforms/__init__.py b/src/brand_os/publish/platforms/__init__.py
index bdb5670..c98e58a 100644
--- a/src/brand_os/publish/platforms/__init__.py
+++ b/src/brand_os/publish/platforms/__init__.py
@@ -1,4 +1,5 @@
"""Social platform publishers."""
+
from typing import Any
# Platform publisher registry
@@ -23,12 +24,14 @@ def list_platforms() -> list[str]:
# Import and register publishers
try:
from brand_os.publish.platforms.twitter import post_tweet
+
register_publisher("twitter", post_tweet)
except ImportError:
pass
try:
from brand_os.publish.platforms.linkedin import post_linkedin
+
register_publisher("linkedin", post_linkedin)
except ImportError:
pass
diff --git a/src/brand_os/publish/platforms/facebook.py b/src/brand_os/publish/platforms/facebook.py
index cc59199..0b89c6f 100644
--- a/src/brand_os/publish/platforms/facebook.py
+++ b/src/brand_os/publish/platforms/facebook.py
@@ -1,4 +1,5 @@
"""Facebook publishing (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/publish/platforms/instagram.py b/src/brand_os/publish/platforms/instagram.py
index 8dcae50..ab07482 100644
--- a/src/brand_os/publish/platforms/instagram.py
+++ b/src/brand_os/publish/platforms/instagram.py
@@ -1,4 +1,5 @@
"""Instagram publishing (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/publish/platforms/linkedin.py b/src/brand_os/publish/platforms/linkedin.py
index fc2666c..cc9d0e9 100644
--- a/src/brand_os/publish/platforms/linkedin.py
+++ b/src/brand_os/publish/platforms/linkedin.py
@@ -1,4 +1,5 @@
"""LinkedIn publishing."""
+
from __future__ import annotations
import os
@@ -23,7 +24,9 @@ def post_linkedin(
Result dict with post_id or error
"""
# Get credentials from env if not provided
- access_token = credentials.get("access_token") if credentials else os.getenv("LINKEDIN_ACCESS_TOKEN")
+ access_token = (
+ credentials.get("access_token") if credentials else os.getenv("LINKEDIN_ACCESS_TOKEN")
+ )
person_id = credentials.get("person_id") if credentials else os.getenv("LINKEDIN_PERSON_ID")
if not access_token:
diff --git a/src/brand_os/publish/platforms/threads.py b/src/brand_os/publish/platforms/threads.py
index d8c05b5..d9de69b 100644
--- a/src/brand_os/publish/platforms/threads.py
+++ b/src/brand_os/publish/platforms/threads.py
@@ -1,4 +1,5 @@
"""Threads publishing (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/publish/platforms/twitter.py b/src/brand_os/publish/platforms/twitter.py
index 7a1010e..88cef41 100644
--- a/src/brand_os/publish/platforms/twitter.py
+++ b/src/brand_os/publish/platforms/twitter.py
@@ -1,4 +1,5 @@
"""Twitter/X publishing."""
+
from __future__ import annotations
import os
diff --git a/src/brand_os/publish/platforms/youtube.py b/src/brand_os/publish/platforms/youtube.py
index 985e9b4..1a3dc38 100644
--- a/src/brand_os/publish/platforms/youtube.py
+++ b/src/brand_os/publish/platforms/youtube.py
@@ -1,4 +1,5 @@
"""YouTube publishing (placeholder)."""
+
from __future__ import annotations
from typing import Any
diff --git a/src/brand_os/publish/queue.py b/src/brand_os/publish/queue.py
index 4e7ca67..a296420 100644
--- a/src/brand_os/publish/queue.py
+++ b/src/brand_os/publish/queue.py
@@ -1,4 +1,5 @@
"""Content queue management."""
+
from __future__ import annotations
import json
diff --git a/src/brand_os/publish/rate_limit.py b/src/brand_os/publish/rate_limit.py
index f5231be..72b1c28 100644
--- a/src/brand_os/publish/rate_limit.py
+++ b/src/brand_os/publish/rate_limit.py
@@ -1,15 +1,15 @@
"""Rate limiting for social publishing."""
+
from __future__ import annotations
import json
-from datetime import timedelta
+from datetime import datetime, timedelta
from pathlib import Path
from typing import Any
from brand_os.core.config import utc_now
from brand_os.core.storage import data_dir
-
# Default rate limits per platform (posts per hour)
DEFAULT_LIMITS = {
"twitter": 10,
@@ -61,10 +61,7 @@ def can_post(platform: str, brand: str | None = None) -> bool:
window_start = utc_now() - timedelta(hours=1)
posts = state[key].get("posts", [])
- recent_posts = [
- p for p in posts
- if datetime.fromisoformat(p) > window_start
- ]
+ recent_posts = [p for p in posts if datetime.fromisoformat(p) > window_start]
limit = DEFAULT_LIMITS.get(platform, 10)
return len(recent_posts) < limit
@@ -87,10 +84,7 @@ def record_post(platform: str, brand: str | None = None) -> None:
# Clean up old entries (keep last 24 hours)
cutoff = utc_now() - timedelta(hours=24)
- state[key]["posts"] = [
- p for p in state[key]["posts"]
- if datetime.fromisoformat(p) > cutoff
- ]
+ state[key]["posts"] = [p for p in state[key]["posts"] if datetime.fromisoformat(p) > cutoff]
save_rate_state(state)
@@ -117,10 +111,9 @@ def get_wait_time(platform: str, brand: str | None = None) -> int:
window_start = utc_now() - timedelta(hours=1)
posts = state[key].get("posts", [])
- recent_posts = sorted([
- datetime.fromisoformat(p) for p in posts
- if datetime.fromisoformat(p) > window_start
- ])
+ recent_posts = sorted(
+ [datetime.fromisoformat(p) for p in posts if datetime.fromisoformat(p) > window_start]
+ )
if not recent_posts:
return 0
diff --git a/src/brand_os/server/api.py b/src/brand_os/server/api.py
index 12d60eb..2bf8ef7 100644
--- a/src/brand_os/server/api.py
+++ b/src/brand_os/server/api.py
@@ -1,4 +1,5 @@
"""FastAPI server (optional)."""
+
from __future__ import annotations
diff --git a/src/brand_os/server/mcp.py b/src/brand_os/server/mcp.py
index 5128341..b6a2fd3 100644
--- a/src/brand_os/server/mcp.py
+++ b/src/brand_os/server/mcp.py
@@ -1,4 +1,5 @@
"""MCP server (optional)."""
+
from __future__ import annotations
diff --git a/src/brand_os/signals/__init__.py b/src/brand_os/signals/__init__.py
index 07bf9ce..75b4a12 100644
--- a/src/brand_os/signals/__init__.py
+++ b/src/brand_os/signals/__init__.py
@@ -1,6 +1,7 @@
"""Signals module - from brandOS."""
-from brand_os.signals.relevance import filter_signals, score_relevance
+
from brand_os.signals.history import append_signals, query_signals
+from brand_os.signals.relevance import filter_signals, score_relevance
__all__ = [
"filter_signals",
diff --git a/src/brand_os/signals/cli.py b/src/brand_os/signals/cli.py
index a20bf60..b1dc333 100644
--- a/src/brand_os/signals/cli.py
+++ b/src/brand_os/signals/cli.py
@@ -1,4 +1,5 @@
"""Signals CLI commands."""
+
from __future__ import annotations
import typer
@@ -115,6 +116,7 @@ def discover_subreddits(
brandos signals discover-subreddits --industry "B2B SaaS" --query "automation"
"""
from rich.table import Table
+
from brand_os.signals.sources.reddit_discover import SubredditDiscovery
discovery = SubredditDiscovery()
@@ -122,6 +124,7 @@ def discover_subreddits(
if brand:
# Load brand config and discover
from brand_os.core.config import load_brand_config
+
config = load_brand_config(brand) or {}
console.print(f"[bold]Discovering subreddits for brand: {brand}[/bold]\n")
@@ -160,7 +163,9 @@ def discover_subreddits(
f"{sub.subscribers:,}",
f"{sub.active_users:,}" if sub.active_users else "-",
f"{sub.relevance_score:.2f}" if sub.relevance_score else "-",
- (sub.description or sub.title)[:50] + "..." if len(sub.description or sub.title) > 50 else (sub.description or sub.title),
+ (sub.description or sub.title)[:50] + "..."
+ if len(sub.description or sub.title) > 50
+ else (sub.description or sub.title),
)
console.print(table)
diff --git a/src/brand_os/signals/history.py b/src/brand_os/signals/history.py
index 1a0c0a2..c8ec225 100644
--- a/src/brand_os/signals/history.py
+++ b/src/brand_os/signals/history.py
@@ -1,4 +1,5 @@
"""Signal history storage (JSONL-based)."""
+
from __future__ import annotations
import json
@@ -126,8 +127,7 @@ def query_signals(
# Text query filter
if query_lower:
text = " ".join(
- str(signal.get(f, ""))
- for f in ["headline", "title", "text", "summary"]
+ str(signal.get(f, "")) for f in ["headline", "title", "text", "summary"]
).lower()
if query_lower not in text:
continue
diff --git a/src/brand_os/signals/providers/__init__.py b/src/brand_os/signals/providers/__init__.py
index 73559ad..8efae8d 100644
--- a/src/brand_os/signals/providers/__init__.py
+++ b/src/brand_os/signals/providers/__init__.py
@@ -1,4 +1,5 @@
"""Signal providers."""
+
from brand_os.signals.providers.google_news import fetch_google_news
from brand_os.signals.providers.web import fetch_web_signals
diff --git a/src/brand_os/signals/providers/file.py b/src/brand_os/signals/providers/file.py
index 32f1223..ea2b15e 100644
--- a/src/brand_os/signals/providers/file.py
+++ b/src/brand_os/signals/providers/file.py
@@ -1,4 +1,5 @@
"""File-based signal provider."""
+
from __future__ import annotations
import json
diff --git a/src/brand_os/signals/providers/google_news.py b/src/brand_os/signals/providers/google_news.py
index ac211ea..d9b96c4 100644
--- a/src/brand_os/signals/providers/google_news.py
+++ b/src/brand_os/signals/providers/google_news.py
@@ -1,4 +1,5 @@
"""Google News signal provider."""
+
from __future__ import annotations
from typing import Any
@@ -35,7 +36,7 @@ def fetch_google_news(
response = client.get(url)
response.raise_for_status()
xml_content = response.text
- except httpx.HTTPError as e:
+ except httpx.HTTPError:
return []
# Parse RSS XML
diff --git a/src/brand_os/signals/providers/web.py b/src/brand_os/signals/providers/web.py
index 1112477..aa7e4d8 100644
--- a/src/brand_os/signals/providers/web.py
+++ b/src/brand_os/signals/providers/web.py
@@ -1,4 +1,5 @@
"""Generic web signal provider."""
+
from __future__ import annotations
from typing import Any
@@ -61,6 +62,7 @@ def _extract_title(html: str) -> str | None:
match = re.search(r"]*>(.*?)", html, re.IGNORECASE | re.DOTALL)
if match:
import html as html_module
+
return html_module.unescape(match.group(1)).strip()
return None
@@ -82,9 +84,11 @@ def _extract_links(html: str, base_url: str) -> list[dict[str, str]]:
# Skip anchors and javascript
if full_url.startswith(("http://", "https://")):
- links.append({
- "url": full_url,
- "text": text[:100] if text else "",
- })
+ links.append(
+ {
+ "url": full_url,
+ "text": text[:100] if text else "",
+ }
+ )
return links
diff --git a/src/brand_os/signals/relevance.py b/src/brand_os/signals/relevance.py
index 53c61f5..fc7418c 100644
--- a/src/brand_os/signals/relevance.py
+++ b/src/brand_os/signals/relevance.py
@@ -1,8 +1,8 @@
"""Signal relevance filtering."""
+
from __future__ import annotations
from typing import Any
-import re
def score_relevance(
diff --git a/src/brand_os/signals/schema.py b/src/brand_os/signals/schema.py
index b4c2a71..c380195 100644
--- a/src/brand_os/signals/schema.py
+++ b/src/brand_os/signals/schema.py
@@ -3,7 +3,7 @@
from __future__ import annotations
from datetime import datetime
-from enum import Enum
+from enum import StrEnum
from typing import Any
from uuid import uuid4
@@ -12,7 +12,7 @@
from brand_os.core.config import utc_now
-class SignalSource(str, Enum):
+class SignalSource(StrEnum):
"""Known signal sources."""
NEWS = "news"
@@ -28,7 +28,7 @@ class SignalSource(str, Enum):
CUSTOM = "custom"
-class SignalType(str, Enum):
+class SignalType(StrEnum):
"""Types of signals."""
NEWS = "news"
@@ -43,7 +43,7 @@ class SignalType(str, Enum):
ALERT = "alert"
-class Urgency(str, Enum):
+class Urgency(StrEnum):
"""Signal urgency levels."""
LOW = "low"
diff --git a/src/brand_os/signals/sources/__init__.py b/src/brand_os/signals/sources/__init__.py
index a5d5acc..0ccba3d 100644
--- a/src/brand_os/signals/sources/__init__.py
+++ b/src/brand_os/signals/sources/__init__.py
@@ -1,8 +1,11 @@
"""Signal sources for data ingestion."""
-from brand_os.signals.sources.rss import RSSSource
from brand_os.signals.sources.reddit import RedditSource, get_subreddits_for_brand
-from brand_os.signals.sources.reddit_discover import SubredditDiscovery, discover_subreddits_for_brand
+from brand_os.signals.sources.reddit_discover import (
+ SubredditDiscovery,
+ discover_subreddits_for_brand,
+)
+from brand_os.signals.sources.rss import RSSSource
__all__ = [
"RSSSource",
diff --git a/src/brand_os/signals/sources/reddit.py b/src/brand_os/signals/sources/reddit.py
index 3b4ff22..0313c8c 100644
--- a/src/brand_os/signals/sources/reddit.py
+++ b/src/brand_os/signals/sources/reddit.py
@@ -10,13 +10,10 @@
import json
import logging
import urllib.request
-from datetime import datetime
-from typing import Any
-
-logger = logging.getLogger(__name__)
from brand_os.signals.schema import Signal, SignalSource, SignalType, Urgency
+logger = logging.getLogger(__name__)
# Common subreddits by category for auto-suggestion
SUBREDDIT_CATEGORIES = {
@@ -146,10 +143,7 @@ def _fetch_subreddit(self, subreddit: str, limit: int = 25) -> list[dict]:
"""Fetch posts from a subreddit using public JSON API."""
url = f"https://www.reddit.com/r/{subreddit}/.json?limit={limit}"
- req = urllib.request.Request(
- url,
- headers={"User-Agent": self.user_agent}
- )
+ req = urllib.request.Request(url, headers={"User-Agent": self.user_agent})
with urllib.request.urlopen(req, timeout=self.timeout) as response:
data = json.loads(response.read().decode())
@@ -234,6 +228,7 @@ def get_subreddits_for_brand(brand_config: dict, use_discovery: bool = True) ->
if has_context:
try:
from brand_os.signals.sources.reddit_discover import discover_subreddits_for_brand
+
discovered = discover_subreddits_for_brand(brand_config)
if discovered:
return discovered[:10] # Limit to top 10
diff --git a/src/brand_os/signals/sources/reddit_discover.py b/src/brand_os/signals/sources/reddit_discover.py
index 8aeaafb..cc4c451 100644
--- a/src/brand_os/signals/sources/reddit_discover.py
+++ b/src/brand_os/signals/sources/reddit_discover.py
@@ -13,19 +13,17 @@
import urllib.parse
import urllib.request
from dataclasses import dataclass
-from datetime import datetime
-from typing import Any
from brand_os.core.config import utc_now
+from brand_os.core.llm import complete_json
logger = logging.getLogger(__name__)
-from brand_os.core.llm import complete_json
-
@dataclass
class SubredditInfo:
"""Discovered subreddit with metadata."""
+
name: str
title: str
description: str
@@ -199,10 +197,7 @@ def discover_for_brand(
sub.relevance_score = self._score_subreddit(sub, keywords or [])
# Filter: minimum subscribers, not dead
- results = [
- s for s in results
- if s.subscribers >= 1000 and s.active_users >= 10
- ]
+ results = [s for s in results if s.subscribers >= 1000 and s.active_users >= 10]
# Sort by relevance
results.sort(key=lambda x: x.relevance_score, reverse=True)
@@ -254,6 +249,7 @@ def _score_subreddit(self, sub: SubredditInfo, keywords: list[str]) -> float:
# Subscriber score (log scale)
if sub.subscribers > 0:
import math
+
score += min(0.3, math.log10(sub.subscribers) / 20)
# Activity score
@@ -279,10 +275,7 @@ def _score_subreddit(self, sub: SubredditInfo, keywords: list[str]) -> float:
def _fetch_json(self, url: str) -> dict:
"""Fetch JSON from URL."""
- req = urllib.request.Request(
- url,
- headers={"User-Agent": self.user_agent}
- )
+ req = urllib.request.Request(url, headers={"User-Agent": self.user_agent})
with urllib.request.urlopen(req, timeout=self.timeout) as response:
return json.loads(response.read().decode())
diff --git a/src/brand_os/signals/sources/rss.py b/src/brand_os/signals/sources/rss.py
index 9048f40..d21db7a 100644
--- a/src/brand_os/signals/sources/rss.py
+++ b/src/brand_os/signals/sources/rss.py
@@ -7,15 +7,13 @@
import hashlib
import logging
-from datetime import datetime
-from typing import Any
import httpx
-logger = logging.getLogger(__name__)
-
from brand_os.signals.schema import Signal, SignalSource, SignalType, Urgency
+logger = logging.getLogger(__name__)
+
class RSSSource:
"""Fetch signals from RSS/Atom feeds.
@@ -80,11 +78,13 @@ def _parse_feed(self, xml: str) -> list[dict]:
import re
# Try RSS format first
- item_pattern = re.compile(r'- (.*?)
', re.DOTALL)
- title_pattern = re.compile(r'(?:<!\[CDATA\[)?(.*?)(?:\]\]>)?', re.DOTALL)
- link_pattern = re.compile(r'(?:)?', re.DOTALL)
- desc_pattern = re.compile(r'(?:)?', re.DOTALL)
- pubdate_pattern = re.compile(r'(.*?)', re.DOTALL)
+ item_pattern = re.compile(r"- (.*?)
", re.DOTALL)
+ title_pattern = re.compile(r"(?:<!\[CDATA\[)?(.*?)(?:\]\]>)?", re.DOTALL)
+ link_pattern = re.compile(r"(?:)?", re.DOTALL)
+ desc_pattern = re.compile(
+ r"(?:)?", re.DOTALL
+ )
+ pubdate_pattern = re.compile(r"(.*?)", re.DOTALL)
for match in item_pattern.finditer(xml):
item_xml = match.group(1)
@@ -94,19 +94,23 @@ def _parse_feed(self, xml: str) -> list[dict]:
desc_match = desc_pattern.search(item_xml)
pubdate_match = pubdate_pattern.search(item_xml)
- items.append({
- "title": title_match.group(1).strip() if title_match else "",
- "link": link_match.group(1).strip() if link_match else "",
- "description": desc_match.group(1).strip() if desc_match else "",
- "pubDate": pubdate_match.group(1).strip() if pubdate_match else "",
- })
+ items.append(
+ {
+ "title": title_match.group(1).strip() if title_match else "",
+ "link": link_match.group(1).strip() if link_match else "",
+ "description": desc_match.group(1).strip() if desc_match else "",
+ "pubDate": pubdate_match.group(1).strip() if pubdate_match else "",
+ }
+ )
# Try Atom format if no RSS items found
if not items:
- entry_pattern = re.compile(r'(.*?)', re.DOTALL)
+ entry_pattern = re.compile(r"(.*?)", re.DOTALL)
atom_link_pattern = re.compile(r']*href=["\']([^"\']+)["\']', re.DOTALL)
- content_pattern = re.compile(r'<(?:content|summary)[^>]*>(.*?)', re.DOTALL)
- updated_pattern = re.compile(r'(.*?)', re.DOTALL)
+ content_pattern = re.compile(
+ r"<(?:content|summary)[^>]*>(.*?)", re.DOTALL
+ )
+ updated_pattern = re.compile(r"(.*?)", re.DOTALL)
for match in entry_pattern.finditer(xml):
entry_xml = match.group(1)
@@ -116,12 +120,14 @@ def _parse_feed(self, xml: str) -> list[dict]:
content_match = content_pattern.search(entry_xml)
updated_match = updated_pattern.search(entry_xml)
- items.append({
- "title": title_match.group(1).strip() if title_match else "",
- "link": link_match.group(1).strip() if link_match else "",
- "description": content_match.group(1).strip() if content_match else "",
- "pubDate": updated_match.group(1).strip() if updated_match else "",
- })
+ items.append(
+ {
+ "title": title_match.group(1).strip() if title_match else "",
+ "link": link_match.group(1).strip() if link_match else "",
+ "description": content_match.group(1).strip() if content_match else "",
+ "pubDate": updated_match.group(1).strip() if updated_match else "",
+ }
+ )
return items
@@ -134,7 +140,8 @@ def _item_to_signal(self, item: dict, brand: str, feed_url: str) -> Signal:
# Strip HTML tags from description
import re
- description = re.sub(r'<[^>]+>', '', item.get("description", ""))[:1000]
+
+ description = re.sub(r"<[^>]+>", "", item.get("description", ""))[:1000]
return Signal(
id=content_hash,
diff --git a/src/brand_os/workflows/approval.py b/src/brand_os/workflows/approval.py
index 8bae436..8ea36c8 100644
--- a/src/brand_os/workflows/approval.py
+++ b/src/brand_os/workflows/approval.py
@@ -6,7 +6,10 @@
from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable
+import logging
+from typing import Any
+
+import httpx
from brand_os.core.config import utc_now
from brand_os.core.decision import (
@@ -16,6 +19,8 @@
get_decision_log,
)
+logger = logging.getLogger(__name__)
+
# Try to import statemachine, fall back to manual implementation
try:
from statemachine import State, StateMachine
@@ -110,9 +115,21 @@ def _save(self) -> None:
get_decision_log().update(self.decision)
def _notify(self, message: str) -> None:
- """Send notification (placeholder for Slack/email integration)."""
- # TODO: Implement notification via slack-sdk or email
- pass
+ """Send notification via Slack webhook."""
+ import os
+
+ webhook_url = os.getenv("SLACK_WEBHOOK_URL")
+ if not webhook_url:
+ logger.info("Approval notification: %s", message)
+ return
+ try:
+ httpx.post(
+ webhook_url,
+ json={"text": f"[BrandOS] {message}"},
+ timeout=10,
+ )
+ except Exception:
+ pass # Best-effort notification
else:
# Fallback implementation without statemachine dependency
@@ -135,9 +152,7 @@ def __init__(self, decision: Decision):
def _transition(self, new_status: DecisionStatus) -> None:
valid = self.VALID_TRANSITIONS.get(self.decision.status, [])
if new_status not in valid:
- raise ValueError(
- f"Invalid transition: {self.decision.status} -> {new_status}"
- )
+ raise ValueError(f"Invalid transition: {self.decision.status} -> {new_status}")
self.decision.status = new_status
self.decision.updated_at = utc_now()
get_decision_log().update(self.decision)
diff --git a/tests/eval/test_grader.py b/tests/eval/test_grader.py
new file mode 100644
index 0000000..9c9c990
--- /dev/null
+++ b/tests/eval/test_grader.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+
+def _extract_dimension_block(prompt: str, *, dim_name: str, weight: float, threshold: float) -> str:
+ lines = prompt.splitlines()
+ header = f"- **{dim_name}** (weight: {weight}, threshold: {threshold})"
+ start = lines.index(header)
+
+ end = len(lines)
+ for i in range(start + 1, len(lines)):
+ if lines[i].startswith("- **"):
+ end = i
+ break
+
+ return "\n".join(lines[start:end])
+
+
+def test_grade_content_prompt_renders_score_anchors_only_when_present(monkeypatch) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(
+ name="with_anchors",
+ description="Has anchors",
+ weight=1.0,
+ threshold=0.7,
+ criteria=["A", "B"],
+ score_anchors={1: "Bad", 3: "OK", 5: "Great"},
+ ),
+ RubricDimension(
+ name="without_anchors",
+ description="No anchors",
+ weight=2.0,
+ threshold=0.8,
+ criteria=["C"],
+ score_anchors={},
+ ),
+ ],
+ )
+
+ grader.grade_content("hello", rubric=rubric)
+
+ prompt = captured["prompt"]
+
+ with_block = _extract_dimension_block(prompt, dim_name="with_anchors", weight=1.0, threshold=0.7)
+ assert " Score anchors:" in with_block
+ assert " 1 = Bad" in with_block
+ assert " 3 = OK" in with_block
+ assert " 5 = Great" in with_block
+
+ without_block = _extract_dimension_block(
+ prompt, dim_name="without_anchors", weight=2.0, threshold=0.8
+ )
+ assert "Score anchors:" not in without_block
+
diff --git a/tests/eval/test_grader_exemplars.py b/tests/eval/test_grader_exemplars.py
new file mode 100644
index 0000000..a1d5fcc
--- /dev/null
+++ b/tests/eval/test_grader_exemplars.py
@@ -0,0 +1,360 @@
+from __future__ import annotations
+
+
+def test_load_voice_exemplars_parses_well_formed_examples(
+ monkeypatch, tmp_path
+) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+ voice_guide.write_text(
+ """## Examples
+
+### Good Example
+> We don't just ship features - we ship outcomes.
+
+### What to Avoid
+> Our industry-leading platform leverages cutting-edge AI.
+""",
+ encoding="utf-8",
+ )
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ result = grader.load_voice_exemplars("acme")
+
+ assert result is not None
+ assert result.good_examples == ["We don't just ship features - we ship outcomes."]
+ assert result.bad_examples == [
+ "Our industry-leading platform leverages cutting-edge AI."
+ ]
+
+
+def test_load_voice_exemplars_template_returns_none() -> None:
+ from brand_os.eval import grader
+
+ assert grader.load_voice_exemplars("_template") is None
+
+
+def test_load_voice_exemplars_missing_file_returns_none(monkeypatch, tmp_path) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ brand_dir.mkdir(parents=True)
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ assert grader.load_voice_exemplars("acme") is None
+
+
+def test_load_voice_exemplars_empty_file_returns_none(monkeypatch, tmp_path) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+ voice_guide.write_text("\n \n", encoding="utf-8")
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ assert grader.load_voice_exemplars("acme") is None
+
+
+def test_load_voice_exemplars_no_blockquotes_returns_none(monkeypatch, tmp_path) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+ voice_guide.write_text(
+ """## Examples
+
+### Good Example
+Plain text that is not a blockquote.
+
+### What to Avoid
+Also plain text.
+""",
+ encoding="utf-8",
+ )
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ assert grader.load_voice_exemplars("acme") is None
+
+
+def test_load_voice_exemplars_malformed_markdown_does_not_leak_sections(
+ monkeypatch, tmp_path
+) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+ voice_guide.write_text(
+ """## Examples
+
+### Good Example
+> On-brand example.
+
+###Bad Example
+> This should not be parsed as good.
+""",
+ encoding="utf-8",
+ )
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ result = grader.load_voice_exemplars("acme")
+
+ assert result is not None
+ assert result.good_examples == ["On-brand example."]
+ assert result.bad_examples == []
+
+
+def test_load_voice_exemplars_enforces_max_counts_and_length(
+ monkeypatch, tmp_path
+) -> None:
+ from brand_os.eval import grader
+
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+
+ long_good = "G" * 350
+ long_bad = "B" * 350
+ voice_guide.write_text(
+ f"""## Examples
+
+### Good Examples
+> Good 1
+
+> {long_good}
+
+> Good 3
+
+> Good 4
+
+### Bad Examples
+> Bad 1
+
+> {long_bad}
+
+> Bad 3
+
+> Bad 4
+""",
+ encoding="utf-8",
+ )
+
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ result = grader.load_voice_exemplars("acme")
+
+ assert result is not None
+ assert len(result.good_examples) == 3
+ assert len(result.bad_examples) == 3
+ assert all(len(example) <= 300 for example in result.good_examples)
+ assert all(len(example) <= 300 for example in result.bad_examples)
+ assert result.good_examples[1] == "G" * 300
+ assert result.bad_examples[1] == "B" * 300
+
+
+def test_build_voice_context_includes_exemplars_when_provided(monkeypatch) -> None:
+ from brand_os.eval import grader
+
+ monkeypatch.setattr(
+ grader,
+ "load_brand_config",
+ lambda _brand: {"voice": {"tone": "bold", "vocabulary": "technical"}},
+ )
+
+ context = grader._build_voice_context(
+ "acme",
+ exemplars=grader.VoiceExemplars(
+ good_examples=["We ship outcomes, not just features."],
+ bad_examples=["Our industry-leading platform synergizes workflows."],
+ raw_text="raw",
+ ),
+ )
+
+ assert "## Brand Voice Definition (acme)" in context
+ assert "### On-Brand Examples" in context
+ assert "> We ship outcomes, not just features." in context
+ assert "### Off-Brand Examples" in context
+ assert "> Our industry-leading platform synergizes workflows." in context
+
+
+def test_build_voice_context_respects_1500_char_total_budget(monkeypatch) -> None:
+ from brand_os.eval import grader
+
+ monkeypatch.setattr(
+ grader,
+ "load_brand_config",
+ lambda _brand: {
+ "voice": {
+ "tone": "T" * 1200,
+ "vocabulary": "technical",
+ "rules": ["Never use passive voice"],
+ }
+ },
+ )
+
+ context = grader._build_voice_context(
+ "acme",
+ exemplars=grader.VoiceExemplars(
+ good_examples=["G" * 1200],
+ bad_examples=["B" * 1200],
+ raw_text="raw",
+ ),
+ )
+
+ assert len(context) == 1500
+ assert context.startswith("## Brand Voice Definition (acme)\n- Tone: ")
+
+
+def test_grade_content_prompt_renders_voice_exemplars_when_present(
+ monkeypatch, tmp_path
+) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ captured: dict[str, str] = {}
+ brand_dir = tmp_path / "acme"
+ voice_guide = brand_dir / "references" / "voice-guide.md"
+ voice_guide.parent.mkdir(parents=True)
+ voice_guide.write_text(
+ """## Examples
+
+### Good Example
+> We ship outcomes, not just features.
+
+### What to Avoid
+> Our industry-leading platform synergizes workflows.
+""",
+ encoding="utf-8",
+ )
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [
+ {"name": "brand_voice", "score": 1.0, "feedback": "ok", "passed": True}
+ ],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+ monkeypatch.setattr(
+ grader,
+ "load_brand_config",
+ lambda _brand: {"voice": {"tone": "bold", "vocabulary": "technical"}},
+ )
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0)
+ ],
+ )
+
+ grader.grade_content("hello", rubric=rubric, brand="acme")
+
+ prompt = captured["prompt"]
+ assert "## Brand Voice Definition (acme)" in prompt
+ assert "### On-Brand Examples" in prompt
+ assert "> We ship outcomes, not just features." in prompt
+ assert "### Off-Brand Examples" in prompt
+ assert "> Our industry-leading platform synergizes workflows." in prompt
+
+
+def test_grade_content_prompt_omits_exemplar_sections_when_absent(monkeypatch) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [
+ {"name": "brand_voice", "score": 1.0, "feedback": "ok", "passed": True}
+ ],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+ monkeypatch.setattr(
+ grader,
+ "load_brand_config",
+ lambda _brand: {"voice": {"tone": "bold", "vocabulary": "technical"}},
+ )
+ monkeypatch.setattr(grader, "load_voice_exemplars", lambda _brand: None)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0)
+ ],
+ )
+
+ grader.grade_content("hello", rubric=rubric, brand="acme")
+
+ prompt = captured["prompt"]
+ assert "## Brand Voice Definition (acme)" in prompt
+ assert "### On-Brand Examples" not in prompt
+ assert "### Off-Brand Examples" not in prompt
+
+
+def test_grade_content_backward_compatible_without_voice_guide(
+ monkeypatch, tmp_path
+) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ captured: dict[str, str] = {}
+ brand_dir = tmp_path / "acme"
+ brand_dir.mkdir(parents=True)
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [
+ {"name": "brand_voice", "score": 1.0, "feedback": "ok", "passed": True}
+ ],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+ monkeypatch.setattr(
+ grader,
+ "load_brand_config",
+ lambda _brand: {"voice": {"tone": "bold", "vocabulary": "technical"}},
+ )
+ monkeypatch.setattr(grader, "get_brand_dir", lambda _brand: brand_dir)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0)
+ ],
+ )
+
+ grader.grade_content("hello", rubric=rubric, brand="acme")
+
+ prompt = captured["prompt"]
+ assert "## Brand Voice Definition (acme)" in prompt
+ assert "### On-Brand Examples" not in prompt
+ assert "### Off-Brand Examples" not in prompt
diff --git a/tests/eval/test_heal.py b/tests/eval/test_heal.py
new file mode 100644
index 0000000..42440ce
--- /dev/null
+++ b/tests/eval/test_heal.py
@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+
+def test_improve_content_includes_target_anchor_for_failed_dimensions(monkeypatch) -> None:
+ from brand_os.eval import grader, heal
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete(*, prompt: str, system: str) -> str:
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return "IMPROVED"
+
+ monkeypatch.setattr(heal, "complete", _fake_complete)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(
+ name="brand_voice",
+ description="Matches brand voice",
+ score_anchors={1: "Bad", 3: "OK", 5: "Great: indistinguishable from exemplars"},
+ ),
+ RubricDimension(
+ name="clarity",
+ description="Clear writing",
+ score_anchors={5: "Crystal clear"},
+ ),
+ RubricDimension(
+ name="engagement",
+ description="Engaging content",
+ score_anchors={},
+ ),
+ ],
+ )
+
+ grade = grader.GradeResult(
+ overall_score=0.4,
+ passed=False,
+ dimension_scores=[
+ grader.DimensionScore(
+ name="brand_voice",
+ score=0.4,
+ feedback="Too generic",
+ passed=False,
+ ),
+ grader.DimensionScore(
+ name="clarity",
+ score=0.9,
+ feedback="Fine",
+ passed=True,
+ ),
+ grader.DimensionScore(
+ name="engagement",
+ score=0.3,
+ feedback="No hook",
+ passed=False,
+ ),
+ ],
+ red_flags_found=[],
+ summary="Needs improvement",
+ suggestions=[],
+ )
+
+ out = heal._improve_content("hello", grade, rubric=rubric)
+ assert out == "IMPROVED"
+
+ prompt = captured["prompt"]
+
+ assert "### What good looks like for brand_voice" in prompt
+ assert "Great: indistinguishable from exemplars" in prompt
+
+ # Passed dimensions should not get anchor guidance.
+ assert "### What good looks like for clarity" not in prompt
+
+ # Failed dimensions with no anchors should not get anchor guidance.
+ assert "### What good looks like for engagement" not in prompt
+
+
+def test_improve_content_formats_voice_exemplars_for_healing_prompt(monkeypatch) -> None:
+ from brand_os.eval import grader, heal
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete(*, prompt: str, system: str) -> str:
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return "IMPROVED"
+
+ monkeypatch.setattr(heal, "complete", _fake_complete)
+ monkeypatch.setattr(
+ heal,
+ "load_voice_exemplars",
+ lambda _brand: grader.VoiceExemplars(
+ good_examples=["We ship outcomes, not outputs."],
+ bad_examples=["Our synergistic platform leverages AI."],
+ raw_text="",
+ ),
+ )
+ monkeypatch.setattr(
+ heal,
+ "_build_voice_context",
+ lambda _brand, _exemplars: (
+ "## Brand Voice Definition (acme)\n"
+ "### On-Brand Examples\n"
+ "> We ship outcomes, not outputs.\n"
+ "### Off-Brand Examples\n"
+ "> Our synergistic platform leverages AI."
+ ),
+ )
+
+ grade = grader.GradeResult(
+ overall_score=0.3,
+ passed=False,
+ dimension_scores=[
+ grader.DimensionScore(
+ name="brand_voice",
+ score=0.3,
+ feedback="Off voice",
+ passed=False,
+ )
+ ],
+ red_flags_found=[],
+ summary="Needs improvement",
+ suggestions=[],
+ )
+
+ out = heal._improve_content("hello", grade, brand="acme")
+ assert out == "IMPROVED"
+
+ prompt = captured["prompt"]
+ assert "### Voice Reference (target style)" in prompt
+ assert "### Voice Anti-Pattern (avoid this style)" in prompt
+ assert "We ship outcomes, not outputs." in prompt
+ assert "Our synergistic platform leverages AI." in prompt
+ assert "### On-Brand Examples" not in prompt
+ assert "### Off-Brand Examples" not in prompt
diff --git a/tests/eval/test_rubric.py b/tests/eval/test_rubric.py
new file mode 100644
index 0000000..872ec8d
--- /dev/null
+++ b/tests/eval/test_rubric.py
@@ -0,0 +1,45 @@
+from brand_os.eval.rubric import parse_rubric
+
+
+def test_parse_rubric_without_score_anchors_defaults_empty() -> None:
+ rubric = parse_rubric(
+ {
+ "name": "test",
+ "dimensions": [
+ {
+ "name": "brand_voice",
+ "description": "Does it match the brand voice?",
+ "weight": 1.0,
+ "threshold": 0.7,
+ "criteria": ["Consistent tone"],
+ }
+ ],
+ }
+ )
+
+ assert len(rubric.dimensions) == 1
+ assert rubric.dimensions[0].name == "brand_voice"
+ assert rubric.dimensions[0].score_anchors == {}
+
+
+def test_parse_rubric_with_score_anchors_preserved() -> None:
+ rubric = parse_rubric(
+ {
+ "name": "test",
+ "dimensions": [
+ {
+ "name": "brand_voice",
+ "description": "Does it match the brand voice?",
+ "score_anchors": {
+ "1": "Bad",
+ 3: "OK",
+ "5": "Great",
+ },
+ }
+ ],
+ }
+ )
+
+ assert len(rubric.dimensions) == 1
+ assert rubric.dimensions[0].name == "brand_voice"
+ assert rubric.dimensions[0].score_anchors == {1: "Bad", 3: "OK", 5: "Great"}
diff --git a/tests/produce/test_copy.py b/tests/produce/test_copy.py
new file mode 100644
index 0000000..e11c857
--- /dev/null
+++ b/tests/produce/test_copy.py
@@ -0,0 +1,249 @@
+from __future__ import annotations
+
+
+def test_produce_and_eval_without_eval_or_heal_matches_generate_copy_without_extra_calls(monkeypatch) -> None:
+ from brand_os.produce import copy
+
+ expected = {
+ "main": "Baseline copy.",
+ "variants": ["Variant A", "Variant B"],
+ "hashtags": ["#AI", "#Trends"],
+ "platform": "twitter",
+ }
+
+ def _fake_generate_copy(
+ topic: str,
+ brand: str | None = None,
+ platform: str = "twitter",
+ hooks=None,
+ learnings=None,
+ voice=None,
+ ):
+ assert topic == "AI trends"
+ assert brand == "acme"
+ assert platform == "twitter"
+ return expected
+
+ def _fail_grade_content(*args, **kwargs):
+ raise AssertionError("grade_content should not be called when eval=False and heal=False")
+
+ def _fail_heal_content(*args, **kwargs):
+ raise AssertionError("heal_content should not be called when eval=False and heal=False")
+
+ monkeypatch.setattr(copy, "generate_copy", _fake_generate_copy)
+ monkeypatch.setattr(copy, "grade_content", _fail_grade_content)
+ monkeypatch.setattr(copy, "heal_content", _fail_heal_content)
+
+ result = copy.produce_and_eval(topic="AI trends", brand="acme", platform="twitter")
+
+ assert result == expected
+
+
+def test_produce_and_eval_with_eval_true_returns_scores(monkeypatch) -> None:
+ from brand_os.eval.grader import DimensionScore, GradeResult
+ from brand_os.produce import copy
+
+ def _fake_generate_copy(
+ topic: str,
+ brand: str | None = None,
+ platform: str = "twitter",
+ hooks=None,
+ learnings=None,
+ voice=None,
+ ):
+ assert topic == "AI trends"
+ assert brand == "acme"
+ assert platform == "twitter"
+ return {
+ "main": "Launch smarter workflows with AI.",
+ "variants": ["Variant A", "Variant B"],
+ "hashtags": ["#AI", "#Automation"],
+ "platform": "twitter",
+ }
+
+ def _fake_grade_content(content: str, rubric=None, brand: str | None = None):
+ assert content == "Launch smarter workflows with AI."
+ assert brand == "acme"
+ return GradeResult(
+ overall_score=0.85,
+ passed=True,
+ dimension_scores=[
+ DimensionScore(
+ name="clarity",
+ score=0.9,
+ feedback="Clear message.",
+ passed=True,
+ )
+ ],
+ red_flags_found=[],
+ summary="Strong",
+ suggestions=["Add one concrete stat"],
+ )
+
+ monkeypatch.setattr(copy, "generate_copy", _fake_generate_copy)
+ monkeypatch.setattr(copy, "grade_content", _fake_grade_content)
+
+ result = copy.produce_and_eval(topic="AI trends", brand="acme", platform="twitter", eval=True)
+
+ assert result["main"] == "Launch smarter workflows with AI."
+ assert result["variants"] == ["Variant A", "Variant B"]
+ assert result["hashtags"] == ["#AI", "#Automation"]
+ assert result["platform"] == "twitter"
+
+ assert result["eval"]["score"] == 0.85
+ assert result["eval"]["passed"] is True
+ assert result["eval"]["dimension_scores"] == [
+ {
+ "name": "clarity",
+ "score": 0.9,
+ "feedback": "Clear message.",
+ "passed": True,
+ }
+ ]
+ assert result["eval"]["suggestions"] == ["Add one concrete stat"]
+
+ assert result["healed"] is False
+ assert result["heal_iterations"] == 0
+ assert result["original_score"] == 0.85
+
+
+def test_produce_and_eval_with_heal_true_runs_heal_when_initial_grade_fails(monkeypatch) -> None:
+ from brand_os.eval.grader import DimensionScore, GradeResult
+ from brand_os.produce import copy
+
+ heal_called = False
+
+ def _fake_generate_copy(
+ topic: str,
+ brand: str | None = None,
+ platform: str = "twitter",
+ hooks=None,
+ learnings=None,
+ voice=None,
+ ):
+ assert topic == "AI trends"
+ assert brand == "acme"
+ assert platform == "twitter"
+ return {
+ "main": "Initial draft.",
+ "variants": ["Variant A"],
+ "hashtags": ["#AI"],
+ "platform": "twitter",
+ }
+
+ def _fake_grade_content(content: str, rubric=None, brand: str | None = None):
+ assert content == "Initial draft."
+ assert brand == "acme"
+ assert rubric is not None
+ return GradeResult(
+ overall_score=0.55,
+ passed=False,
+ dimension_scores=[
+ DimensionScore(
+ name="clarity",
+ score=0.5,
+ feedback="Too vague.",
+ passed=False,
+ )
+ ],
+ red_flags_found=[],
+ summary="Needs improvement",
+ suggestions=["Be more specific"],
+ )
+
+ def _fake_heal_content(content: str, rubric=None, target_score: float = 0.8, brand: str | None = None):
+ nonlocal heal_called
+ heal_called = True
+ assert content == "Initial draft."
+ assert rubric is not None
+ assert target_score == rubric.pass_threshold
+ assert brand == "acme"
+ return {
+ "final_content": "Healed draft with specifics.",
+ "final_score": 0.82,
+ "success": True,
+ "iterations": 2,
+ }
+
+ monkeypatch.setattr(copy, "generate_copy", _fake_generate_copy)
+ monkeypatch.setattr(copy, "grade_content", _fake_grade_content)
+ monkeypatch.setattr(copy, "heal_content", _fake_heal_content)
+
+ result = copy.produce_and_eval(topic="AI trends", brand="acme", platform="twitter", heal=True)
+
+ assert heal_called is True
+ assert result["main"] == "Healed draft with specifics."
+ assert result["eval"]["score"] == 0.82
+ assert result["eval"]["passed"] is True
+ assert result["healed"] is True
+ assert result["heal_iterations"] == 2
+ assert result["original_score"] == 0.55
+
+
+def test_produce_and_eval_with_heal_true_skips_heal_when_initial_grade_passes(monkeypatch) -> None:
+ from brand_os.eval.grader import DimensionScore, GradeResult
+ from brand_os.produce import copy
+
+ heal_called = False
+
+ def _fake_generate_copy(
+ topic: str,
+ brand: str | None = None,
+ platform: str = "twitter",
+ hooks=None,
+ learnings=None,
+ voice=None,
+ ):
+ assert topic == "AI trends"
+ assert brand == "acme"
+ assert platform == "twitter"
+ return {
+ "main": "Passing draft.",
+ "variants": ["Variant A"],
+ "hashtags": ["#AI"],
+ "platform": "twitter",
+ }
+
+ def _fake_grade_content(content: str, rubric=None, brand: str | None = None):
+ assert content == "Passing draft."
+ assert brand == "acme"
+ assert rubric is not None
+ return GradeResult(
+ overall_score=0.91,
+ passed=True,
+ dimension_scores=[
+ DimensionScore(
+ name="clarity",
+ score=0.95,
+ feedback="Clear and specific.",
+ passed=True,
+ )
+ ],
+ red_flags_found=[],
+ summary="Strong",
+ suggestions=["Optional: add a CTA"],
+ )
+
+ def _fake_heal_content(content: str, rubric=None, target_score: float = 0.8, brand: str | None = None):
+ nonlocal heal_called
+ heal_called = True
+ return {
+ "final_content": "Should not be used",
+ "final_score": 0.99,
+ "success": True,
+ "iterations": 1,
+ }
+
+ monkeypatch.setattr(copy, "generate_copy", _fake_generate_copy)
+ monkeypatch.setattr(copy, "grade_content", _fake_grade_content)
+ monkeypatch.setattr(copy, "heal_content", _fake_heal_content)
+
+ result = copy.produce_and_eval(topic="AI trends", brand="acme", platform="twitter", heal=True)
+
+ assert heal_called is False
+ assert result["main"] == "Passing draft."
+ assert result["eval"]["score"] == 0.91
+ assert result["eval"]["passed"] is True
+ assert result["healed"] is False
+ assert result["heal_iterations"] == 0
+ assert result["original_score"] == 0.91
diff --git a/tests/test_grader_voice_context.py b/tests/test_grader_voice_context.py
new file mode 100644
index 0000000..3c9b7fd
--- /dev/null
+++ b/tests/test_grader_voice_context.py
@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+
+def test_build_voice_context_populated_brand(monkeypatch) -> None:
+ from brand_os.eval import grader
+
+ def _fake_load_brand_config(brand: str):
+ assert brand == "acme"
+ return {
+ "voice": {
+ "tone": "bold",
+ "vocabulary": "technical",
+ "patterns": ["Start with a provocative question", "Use data points"],
+ "rules": ["Never use passive voice", "Keep sentences under 20 words"],
+ "avoid_phrases": ["industry-leading", "synergy", "leverage"],
+ }
+ }
+
+ monkeypatch.setattr(grader, "load_brand_config", _fake_load_brand_config)
+
+ got = grader._build_voice_context("acme")
+ expected = "\n".join(
+ [
+ "## Brand Voice Definition (acme)",
+ "- Tone: bold",
+ "- Vocabulary: technical",
+ "- Speech patterns: Start with a provocative question, Use data points",
+ "- Rules: Never use passive voice, Keep sentences under 20 words",
+ "- Avoid phrases: industry-leading, synergy, leverage",
+ "",
+ "Evaluate the brand_voice dimension against these specific guidelines.",
+ ]
+ )
+
+ assert got == expected
+
+
+def test_build_voice_context_missing_or_empty_brand(monkeypatch) -> None:
+ from brand_os.eval import grader
+
+ def _fake_load_brand_config(brand: str):
+ if brand == "does-not-exist":
+ raise FileNotFoundError("brand not found")
+ if brand == "empty-voice":
+ return {"voice": {}}
+ raise AssertionError(f"unexpected brand: {brand}")
+
+ monkeypatch.setattr(grader, "load_brand_config", _fake_load_brand_config)
+
+ # Missing/blank brand should short-circuit without loading config.
+ assert grader._build_voice_context("") == ""
+ assert grader._build_voice_context(" ") == ""
+ assert grader._build_voice_context(None) == "" # type: ignore[arg-type]
+
+ # Invalid brand should not raise and should return empty.
+ assert grader._build_voice_context("does-not-exist") == ""
+
+ # Empty/minimal voice config should return empty.
+ assert grader._build_voice_context("empty-voice") == ""
+
+
+def test_grade_content_with_brand_includes_voice_context(monkeypatch) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ def _fake_load_brand_config(brand: str):
+ assert brand == "acme"
+ return {
+ "voice": {
+ "tone": "bold",
+ "vocabulary": "technical",
+ "patterns": ["Start with a provocative question", "Use data points"],
+ "rules": ["Never use passive voice", "Keep sentences under 20 words"],
+ "avoid_phrases": ["industry-leading", "synergy", "leverage"],
+ }
+ }
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [
+ {"name": "brand_voice", "score": 1.0, "feedback": "ok", "passed": True}
+ ],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "load_brand_config", _fake_load_brand_config)
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0)
+ ],
+ )
+
+ result = grader.grade_content("hello", rubric=rubric, brand="acme")
+ assert result.summary == "ok"
+
+ prompt = captured["prompt"]
+ assert "## Brand Voice Definition (acme)" in prompt
+ assert "- Tone: bold" in prompt
+ assert "- Vocabulary: technical" in prompt
+ assert "Evaluate the brand_voice dimension against these specific guidelines." in prompt
+
+
+def test_grade_content_without_brand_is_backward_compatible(monkeypatch) -> None:
+ from brand_os.eval import grader
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ def _fake_load_brand_config(_brand: str):
+ raise AssertionError("load_brand_config should not be called when brand is omitted")
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete_json(*, prompt: str, system: str, default: dict):
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return {
+ "dimension_scores": [
+ {"name": "brand_voice", "score": 1.0, "feedback": "ok", "passed": True}
+ ],
+ "red_flags_found": [],
+ "summary": "ok",
+ "suggestions": [],
+ }
+
+ monkeypatch.setattr(grader, "load_brand_config", _fake_load_brand_config)
+ monkeypatch.setattr(grader, "complete_json", _fake_complete_json)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0)
+ ],
+ )
+
+ result = grader.grade_content("hello", rubric=rubric)
+ assert result.summary == "ok"
+
+ expected_prompt = "\n".join(
+ [
+ "Grade this content against the rubric.",
+ "",
+ "## Content",
+ "hello",
+ "",
+ "## Rubric",
+ "Name: test",
+ "",
+ "### Dimensions",
+ "- **brand_voice** (weight: 1.0, threshold: 0.7)",
+ " Matches brand voice",
+ ]
+ )
+
+ assert captured["system"] == grader.GRADER_SYSTEM
+ assert captured["prompt"] == expected_prompt
+ assert "## Brand Voice Definition" not in captured["prompt"]
diff --git a/tests/test_heal_voice_context.py b/tests/test_heal_voice_context.py
new file mode 100644
index 0000000..4941ae2
--- /dev/null
+++ b/tests/test_heal_voice_context.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+
+def test_heal_content_with_brand_includes_voice_context_in_improvement_prompt(monkeypatch) -> None:
+ from brand_os.eval import grader, heal
+ from brand_os.eval.rubric import Rubric, RubricDimension
+
+ def _fake_load_brand_config(brand: str):
+ assert brand == "acme"
+ return {
+ "voice": {
+ "tone": "bold",
+ "vocabulary": "technical",
+ "patterns": ["Start with a provocative question", "Use data points"],
+ "rules": ["Never use passive voice", "Keep sentences under 20 words"],
+ "avoid_phrases": ["industry-leading", "synergy", "leverage"],
+ }
+ }
+
+ monkeypatch.setattr(grader, "load_brand_config", _fake_load_brand_config)
+
+ def _fake_grade_content(content: str, rubric: Rubric | None = None, brand: str | None = None):
+ assert brand == "acme"
+ return grader.GradeResult(
+ overall_score=0.4,
+ passed=False,
+ dimension_scores=[
+ grader.DimensionScore(
+ name="brand_voice",
+ score=0.4,
+ feedback="Too generic; not bold or technical enough.",
+ passed=False,
+ )
+ ],
+ red_flags_found=[],
+ summary="Needs voice fixes",
+ suggestions=[],
+ )
+
+ monkeypatch.setattr(heal, "grade_content", _fake_grade_content)
+
+ captured: dict[str, str] = {}
+
+ def _fake_complete(*, prompt: str, system: str) -> str:
+ captured["prompt"] = prompt
+ captured["system"] = system
+ return "IMPROVED"
+
+ monkeypatch.setattr(heal, "complete", _fake_complete)
+
+ rubric = Rubric(
+ name="test",
+ dimensions=[
+ RubricDimension(name="brand_voice", description="Matches brand voice", weight=1.0),
+ ],
+ )
+
+ heal.heal_content(
+ "hello",
+ rubric=rubric,
+ max_iterations=1,
+ target_score=0.8,
+ brand="acme",
+ )
+
+ prompt = captured["prompt"]
+ assert "## Brand Voice Definition (acme)" in prompt
+ assert "- Tone: bold" in prompt
+ assert "- Vocabulary: technical" in prompt
+ assert "Evaluate the brand_voice dimension against these specific guidelines." in prompt
+ assert "- brand_voice: Too generic; not bold or technical enough." in prompt
+
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..ddb1f86
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,5049 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+ "python_full_version < '3.12' and sys_platform == 'win32'",
+ "python_full_version >= '3.12'",
+ "python_full_version < '3.12' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "adagio"
+version = "0.2.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "triad" },
+]
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+]
+
+[[package]]
+name = "ag-ui-protocol"
+version = "0.1.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "pydantic" },
+]
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+[[package]]
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "propcache" },
+ { name = "yarl" },
+]
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