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Add AgentByte screening plugin spec
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apps/logicsrc-web/src/main.ts

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{ name: "GitHub integration", detail: "Target Profullstack repos, create issues and branches, and keep task history auditable." }
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];
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const agentByteSurfaces = [
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{ name: "CLI", detail: "`logicsrc agentbyte` for plans, sessions, scorecards, audits, and artifacts." },
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{ name: "TUI", detail: "Terminal panes for live transcripts, policy events, model use, evidence, and scorecards." },
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{ name: "SDKs", detail: "Rust, Bun, Node, Python, and curl surfaces with the same screening session API." },
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{ name: "PWA", detail: "Plan builder, candidate intake, live screening room, artifact review, and decision packet." }
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];
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document.querySelector<HTMLDivElement>("#app")!.innerHTML = `
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<main class="shell">
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<aside class="rail">
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<a class="active" href="#overview">Overview</a>
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<a href="#schemas">Schemas</a>
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<a href="/agent-swarm">Soon</a>
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<a href="/agentbyte">AgentByte</a>
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<a href="#cli">CLI</a>
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<a href="#reference">Reference</a>
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</nav>
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</div>
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</section>
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<section id="agentbyte" class="band coming-soon agentbyte">
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<div class="section-head">
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<h2>AgentByte</h2>
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<p>A coming-soon LogicSRC plugin spec for AI-era screening, assessments, interviews, and auditable agent-assisted answers.</p>
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</div>
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<div class="soon-layout">
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<article class="soon-lead">
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<span>slug: agentbyte</span>
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<h3>Screening where AI use is declared, measured, and auditable</h3>
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<p>AgentByte defines open screening sessions for humans, AI-assisted humans, autonomous agents, and human-agent pairs. Instead of pretending AI is absent, the spec records model use, tools, artifacts, scorecards, policy decisions, and evidence.</p>
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<pre><code>logicsrc agentbyte plan create \\
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--role "AI Engineer" \\
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--repo profullstack/logicsrc
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logicsrc agentbyte session audit \\
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--session ssn_123 \\
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--format markdown</code></pre>
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</article>
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<div class="soon-grid">
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${agentByteSurfaces.map((item) => `
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<article>
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<h3>${item.name}</h3>
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<p>${item.detail}</p>
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</article>
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`).join("")}
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</div>
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</div>
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</section>
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<section id="schemas" class="band two-col">
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<div>
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<div class="section-head">
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if (window.location.pathname === "/agent-swarm") {
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document.querySelector("#agent-swarm")?.scrollIntoView();
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}
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if (window.location.pathname === "/agentbyte") {
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document.querySelector("#agentbyte")?.scrollIntoView();
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}

apps/logicsrc-web/src/styles.css

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background: #eef7f3;
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}
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.agentbyte .soon-lead {
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background: #f7f3e8;
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}
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.agentbyte .soon-lead span {
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border-color: #e3c878;
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color: #735b14;
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}
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.soon-lead span {
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display: inline-block;
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margin-bottom: 0.75rem;

docs/agent-screening.md

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# AgentByte Plugin Spec
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Status: coming soon
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Slug: `agentbyte`
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AgentByte is a working-title LogicSRC plugin spec for interoperable screening workflows in the AI era. It is intended for candidate screening, contractor qualification, agent capability checks, team upskilling, and marketplace trust.
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The spec assumes both humans and AI agents may participate. AI assistance is not treated as cheating by default; it is declared, measured, constrained, and audited by policy.
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## Goals
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- Turn job descriptions, project needs, and role requirements into structured screening plans.
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- Support human, AI-assisted human, autonomous agent, and hybrid candidate modes.
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- Record what tools, models, prompts, files, repos, and external resources were used.
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- Produce portable scorecards, evidence, transcripts, artifacts, and replayable audit logs.
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- Let products implement screening without owning the standard.
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## Core Objects
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```txt
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screening_profile
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screening_plan
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screening_session
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screening_question
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screening_challenge
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screening_answer
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screening_artifact
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screening_scorecard
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screening_policy
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screening_proctor_event
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screening_model_use
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screening_decision
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```
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## Candidate Modes
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```txt
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human_only
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human_ai_assisted
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agent_only
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human_agent_pair
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team
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```
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## Policy Fields
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```txt
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ai_allowed
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allowed_models
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blocked_models
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allowed_tools
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blocked_tools
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internet_allowed
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repo_access
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time_limit_seconds
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recording_required
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human_attestation_required
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agent_attestation_required
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evidence_required
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```
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## CLI Spec
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Command namespace:
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```bash
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logicsrc agentbyte <command>
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```
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Required commands:
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```txt
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plan create
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plan get
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session start
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session submit
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session score
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session audit
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policy validate
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artifact attach
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export scorecard
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```
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Examples:
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```bash
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logicsrc agentbyte plan create --role "AI Engineer" --repo profullstack/logicsrc
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logicsrc agentbyte session start --plan plan_123 --candidate did:agent.qa
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logicsrc agentbyte session submit --session ssn_123 --artifact ./patch.diff
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logicsrc agentbyte session audit --session ssn_123 --format markdown
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```
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## TUI Spec
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Required panes:
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```txt
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plans
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sessions
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live transcript
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questions/challenges
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artifacts
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model/tool use
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scorecard
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audit events
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policy violations
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```
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Required actions:
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```txt
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start session
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pause session
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attach artifact
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request clarification
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mark evidence
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score criterion
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flag policy event
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export scorecard
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```
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## SDK Spec
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All SDKs should expose the same conceptual API:
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```txt
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createPlan(input)
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getPlan(id)
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startSession(planId, candidate)
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submitAnswer(sessionId, answer)
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attachArtifact(sessionId, artifact)
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recordModelUse(sessionId, modelUse)
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scoreSession(sessionId, scorecard)
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exportAudit(sessionId)
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```
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### Rust
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Package target: `agentbyte`
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```rust
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let client = ScreeningClient::new(config);
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let plan = client.create_plan(plan_input).await?;
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let session = client.start_session(&plan.id, candidate).await?;
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```
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### Bun
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Package target: `@profullstack/agentbyte-bun`
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```ts
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const client = new ScreeningClient({ apiKey });
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const plan = await client.createPlan(input);
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const session = await client.startSession(plan.id, candidate);
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```
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### Node
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Package target: `@profullstack/agentbyte-node`
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```ts
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import { ScreeningClient } from "@profullstack/agentbyte-node";
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const client = new ScreeningClient({ apiKey });
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await client.recordModelUse(sessionId, modelUse);
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```
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### Python
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Package target: `profullstack-agentbyte`
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```python
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client = ScreeningClient(api_key=api_key)
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plan = client.create_plan(input)
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session = client.start_session(plan["id"], candidate)
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```
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### curl
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HTTP surface:
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```bash
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curl -X POST "$LOGICSRC_API/agentbyte/plans" \
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-H "authorization: Bearer $TOKEN" \
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-H "content-type: application/json" \
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-d @plan.json
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```
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Required endpoints:
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```txt
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POST /agentbyte/plans
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GET /agentbyte/plans/:id
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POST /agentbyte/sessions
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GET /agentbyte/sessions/:id
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POST /agentbyte/sessions/:id/answers
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POST /agentbyte/sessions/:id/artifacts
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POST /agentbyte/sessions/:id/model-use
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POST /agentbyte/sessions/:id/score
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GET /agentbyte/sessions/:id/audit
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```
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## PWA Spec
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Required views:
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```txt
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plan builder
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candidate intake
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live screening room
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challenge workspace
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model/tool-use disclosure
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artifact review
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scorecard editor
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audit timeline
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decision packet
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```
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Required states:
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```txt
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draft
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scheduled
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active
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paused
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submitted
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scoring
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reviewed
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decided
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disputed
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archived
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```
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## MCP Spec
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Resources:
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```txt
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agentbyte://plans
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agentbyte://sessions
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agentbyte://policies
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agentbyte://scorecards
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agentbyte://audits/{session_id}
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```
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Tools:
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```txt
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create_agentbyte_plan
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start_agentbyte_session
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submit_agentbyte_answer
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attach_agentbyte_artifact
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record_agentbyte_model_use
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score_agentbyte_session
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export_agentbyte_audit
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```
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Prompts:
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```txt
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create-agentbyte-plan
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review-agentbyte-artifacts
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summarize-agentbyte-audit
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draft-agentbyte-decision
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```
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## Minimum Audit Event Shape
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```json
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{
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"type": "logicsrc.event",
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"version": "0.1",
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"event_id": "evt_screening_123",
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"event_type": "screening.model_use.recorded",
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"resource_type": "screening_session",
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"resource_id": "ssn_123",
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"actor_did": "candidate.example",
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"created_at": "2026-01-01T00:00:00.000Z",
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"metadata": {
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"model": "provider/model",
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"purpose": "answer_assistance",
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"policy_allowed": true
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}
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}
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```

docs/plugins.md

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- uGig: job import, gig publishing, candidate/agent linking, bid sync, marketplace publishing, and reputation sync.
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- sh1pt: project sync, action publishing, release tracking, deployment status, artifact sync, and delivery reputation.
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Coming soon plugin specs:
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- AgentByte: candidate, contractor, and agent capability screening for AI-era workflows. See `docs/agent-screening.md`.
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Runtime requirements:
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- Validate plugin manifests.

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