From c0f00542547953b9d363b226001dc1b9111698d0 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 11:40:28 +0930
Subject: [PATCH 01/14] feat(eval): expand evaluation datasets to messages
 format and modular dataset files

---
 .gitattributes                                |    1 +
 eval/a2ui_eval/dataset.py                     |  208 ++-
 eval/a2ui_eval/strategies/format.py           |   16 +-
 eval/bin/migrate_datasets.py                  |  104 ++
 eval/bin/report_evals.py                      |  216 +--
 eval/bin/run_ci_evals.py                      |   59 +-
 eval/datasets/core_v0_9_1.yaml                | 1170 +++++++++++++++++
 eval/datasets/core_v1_0.yaml                  | 1160 ++++++++++++++++
 eval/datasets/dataset_schema.json             |   88 +-
 eval/datasets/prompts_v0_9_1.yaml             | 1001 --------------
 eval/datasets/prompts_v1_0.yaml               |  994 --------------
 eval/main.py                                  |   31 +-
 eval/tasks.py                                 |   34 +-
 eval/tests/test_dataset.py                    |   59 +-
 .../proposals/eval_suite_expansion.md         |  671 ++++++++++
 15 files changed, 3594 insertions(+), 2218 deletions(-)
 create mode 100644 .gitattributes
 create mode 100644 eval/bin/migrate_datasets.py
 create mode 100644 eval/datasets/core_v0_9_1.yaml
 create mode 100644 eval/datasets/core_v1_0.yaml
 delete mode 100644 eval/datasets/prompts_v0_9_1.yaml
 delete mode 100644 eval/datasets/prompts_v1_0.yaml
 create mode 100644 specification/proposals/eval_suite_expansion.md

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000..e341638568
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+#pattern  filter=crypt diff=crypt merge=crypt
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index b5b2b67200..3576bb9ab0 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -15,22 +15,31 @@
 """Dataset loader for A2UI evaluation."""
 
 import json
-import os
+from pathlib import Path
+from typing import Any
 
 import jsonschema
 import yaml
 from inspect_ai.dataset import MemoryDataset, Sample
+from inspect_ai.model import (
+    ChatMessageAssistant,
+    ChatMessageSystem,
+    ChatMessageTool,
+    ChatMessageUser,
+)
+from inspect_ai.tool import ToolCall
 
+from a2ui_eval.shared.utils import GIT_ROOT
 from datasets.defaults import (
     DEFAULT_CATALOG_PATH,
-    DEFAULT_WORKFLOW_DESCRIPTION,
     DEFAULT_ROLE_DESCRIPTION,
+    DEFAULT_WORKFLOW_DESCRIPTION,
     FORMAT_AGNOSTIC_ROLE_DESCRIPTION,
     FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION,
 )
-from a2ui_eval.shared.utils import GIT_ROOT
 
 SCHEMA_PATH = GIT_ROOT / "eval" / "datasets" / "dataset_schema.json"
+DATASETS_DIR = GIT_ROOT / "eval" / "datasets"
 
 
 def _version_to_dir_name(version: str) -> str:
@@ -38,74 +47,171 @@ def _version_to_dir_name(version: str) -> str:
     return "v" + version.replace(".", "_")
 
 
+def _parse_tool_calls(raw_tool_calls: list[dict[str, Any]] | None) -> list[ToolCall] | None:
+    """Converts raw tool calls into Inspect AI ToolCall objects."""
+    if not raw_tool_calls:
+        return None
+    parsed = []
+    for tc in raw_tool_calls:
+        call_id = tc.get("id", "")
+        call_type = tc.get("type", "function")
+        if "function" in tc and isinstance(tc["function"], dict):
+            func_name = tc["function"].get("name", "")
+            func_args = tc["function"].get("arguments", {})
+        else:
+            func_name = str(tc.get("function", ""))
+            func_args = tc.get("arguments", {})
+
+        if isinstance(func_args, str):
+            try:
+                func_args = json.loads(func_args)
+            except Exception:
+                pass
+        parsed.append(ToolCall(id=call_id, function=func_name, arguments=func_args, type=call_type))
+    return parsed
+
+
+def _parse_messages(item: dict[str, Any]) -> list[Any]:
+    """Parses a sample's messages list or promptText into Inspect AI ChatMessage objects."""
+    messages_raw = item.get("messages")
+    if messages_raw:
+        parsed = []
+        for m in messages_raw:
+            role = m.get("role", "user")
+            content = m.get("content", "")
+            if role == "user":
+                parsed.append(ChatMessageUser(content=content))
+            elif role == "assistant":
+                parsed.append(
+                    ChatMessageAssistant(
+                        content=content,
+                        tool_calls=_parse_tool_calls(m.get("tool_calls")),
+                    )
+                )
+            elif role == "tool":
+                parsed.append(
+                    ChatMessageTool(
+                        content=content,
+                        tool_call_id=m.get("tool_call_id", ""),
+                    )
+                )
+            elif role == "system":
+                parsed.append(ChatMessageSystem(content=content))
+            else:
+                parsed.append(ChatMessageUser(content=content))
+        return parsed
+    elif "promptText" in item:
+        return [ChatMessageUser(content=item["promptText"])]
+    else:
+        raise ValueError(f"Sample {item.get('name')} must specify 'messages' or 'promptText'.")
+
+
 def load_a2ui_dataset(
-    file_path: str,
+    file_path: str | Path | None = None,
+    dataset: str | list[str] | None = None,
     default_catalog_path: str | None = None,
     version: str | None = None,
     format_name: str | None = None,
 ) -> MemoryDataset:
-    """Loads A2UI evaluation samples from a YAML file.
+    """Loads A2UI evaluation samples from YAML dataset files.
 
     Args:
-        file_path: The path to the YAML dataset file.
-        default_catalog_path: The default catalog path to use if not specified in
-          the sample.
+        file_path: The path to a specific YAML dataset file, or None to load datasets by name.
+        dataset: Specific dataset name or list of dataset names to filter by.
+        default_catalog_path: Fallback catalog path if not specified in the sample.
         version: Optional target version string to substitute into catalog paths.
+        format_name: The output format name (e.g., 'json', 'express', 'elemental').
 
     Returns:
         A MemoryDataset containing the resolved samples.
-
-    Raises:
-        FileNotFoundError: If the dataset file does not exist.
     """
-    if not os.path.exists(file_path):
-        raise FileNotFoundError(f"Dataset file not found: {file_path}")
-
-    with open(file_path, "r", encoding="utf-8") as f:
-        data = yaml.safe_load(f)
-
     with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
         schema = json.load(f)
 
-    jsonschema.validate(instance=data, schema=schema)
+    # Normalize dataset filter
+    filter_names = None
+    if dataset:
+        if isinstance(dataset, str):
+            filter_names = [d.strip() for d in dataset.split(",") if d.strip()]
+        else:
+            filter_names = list(dataset)
+
+    # Discover files to load
+    target_files: list[Path] = []
+    if file_path:
+        p = Path(file_path)
+        if not p.is_absolute():
+            p = (DATASETS_DIR / p).resolve() if not p.exists() else p.resolve()
+        if not p.exists():
+            raise FileNotFoundError(f"Dataset file not found: {p}")
+        target_files.append(p)
+    else:
+        for yaml_file in sorted(DATASETS_DIR.glob("*.yaml")):
+            target_files.append(yaml_file)
 
     is_json = format_name is None or format_name == "json"
+    samples: list[Sample] = []
 
-    samples = []
-    for item in data:
-        catalog_path = (
-            item.get("catalog") or default_catalog_path or DEFAULT_CATALOG_PATH
-        )
-        if version and catalog_path:
-            catalog_path = catalog_path.replace(
-                "{version}", _version_to_dir_name(version)
+    for target_file in target_files:
+        with open(target_file, "r", encoding="utf-8") as f:
+            data = yaml.safe_load(f) or []
+
+        jsonschema.validate(instance=data, schema=schema)
+        inferred_dataset = target_file.stem
+
+        for item in data:
+            item_dataset = item.get("dataset") or inferred_dataset
+            if filter_names and item_dataset not in filter_names:
+                continue
+
+            catalog_path = (
+                item.get("catalog") or default_catalog_path or DEFAULT_CATALOG_PATH
+            )
+            if version and catalog_path:
+                catalog_path = catalog_path.replace(
+                    "{version}", _version_to_dir_name(version)
+                )
+
+            default_role = (
+                DEFAULT_ROLE_DESCRIPTION if is_json else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
+            )
+            default_workflow = (
+                DEFAULT_WORKFLOW_DESCRIPTION
+                if is_json
+                else FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION
+            )
+
+            protocol_role = (
+                item.get("protocol_role")
+                or item.get("role_description")
+                or default_role
+            )
+            generation_rules = (
+                item.get("generation_rules")
+                or item.get("workflow_description")
+                or default_workflow
             )
 
-        default_role = (
-            DEFAULT_ROLE_DESCRIPTION if is_json else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
-        )
-        default_workflow = (
-            DEFAULT_WORKFLOW_DESCRIPTION
-            if is_json
-            else FORMAT_AGNOSTIC_WORKFLOW_DESCRIPTION
-        )
-
-        role_description = item.get("role_description") or default_role
-        workflow_description = item.get("workflow_description") or default_workflow
-
-        samples.append(
-            Sample(
-                input=item["promptText"],
-                target=item.get("target") or item["description"],
-                metadata={
-                    "name": item.get("name"),
-                    "description": item["description"],
-                    "catalog": catalog_path,
-                    "role_description": role_description,
-                    "workflow_description": workflow_description,
-                    "allowed_surface_ids": item.get("allowed_surface_ids") or ["main"],
-                },
+            chat_messages = _parse_messages(item)
+
+            samples.append(
+                Sample(
+                    id=item.get("name"),
+                    input=chat_messages,
+                    target=item.get("target") or item["description"],
+                    metadata={
+                        "name": item.get("name"),
+                        "dataset": item_dataset,
+                        "description": item["description"],
+                        "catalog": catalog_path,
+                        "system_prompt": item.get("system_prompt", ""),
+                        "protocol_role": protocol_role,
+                        "generation_rules": generation_rules,
+                        "role_description": protocol_role,
+                        "workflow_description": generation_rules,
+                        "allowed_surface_ids": item.get("allowed_surface_ids") or ["main"],
+                    },
+                )
             )
-        )
 
     return MemoryDataset(samples=samples)
diff --git a/eval/a2ui_eval/strategies/format.py b/eval/a2ui_eval/strategies/format.py
index e1630dba5c..89f43c7953 100644
--- a/eval/a2ui_eval/strategies/format.py
+++ b/eval/a2ui_eval/strategies/format.py
@@ -76,20 +76,28 @@ async def solve(state: TaskState, generate: Generate) -> TaskState:
         catalog_config = CatalogConfig.from_path("basic_catalog", resolved_catalog_path)
         strategy = _get_strategy(format_name, version, catalog_config)
 
-        role_description = state.metadata.get("role_description", "")
-        workflow_description = state.metadata.get("workflow_description", "")
+        role_description = state.metadata.get("protocol_role") or state.metadata.get("role_description", "")
+        workflow_description = state.metadata.get("generation_rules") or state.metadata.get("workflow_description", "")
 
-        prompt = strategy.prompt_generator.generate(
+        a2ui_prompt = strategy.prompt_generator.generate(
             role_description=role_description,
             workflow_description=workflow_description,
             include_schema=True,
         )
-        state.messages.insert(0, ChatMessageSystem(content=prompt))
+
+        domain_prompt = state.metadata.get("system_prompt", "").strip()
+        if domain_prompt:
+            full_prompt = f"## Domain Instructions\n{domain_prompt}\n\n## UI Protocol Instructions\n{a2ui_prompt}"
+        else:
+            full_prompt = a2ui_prompt
+
+        state.messages.insert(0, ChatMessageSystem(content=full_prompt))
         return state
 
     return solve
 
 
+
 @solver
 def compile_format_payload(format_name: str, version: str) -> Solver:
     """Solver to compile format-specific output back to standard A2UI JSON."""
diff --git a/eval/bin/migrate_datasets.py b/eval/bin/migrate_datasets.py
new file mode 100644
index 0000000000..56da33bcd4
--- /dev/null
+++ b/eval/bin/migrate_datasets.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Migrates legacy A2UI dataset YAML files to the modern messages format."""
+
+import json
+import os
+import jsonschema
+import yaml
+
+EVAL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATASETS_DIR = os.path.join(EVAL_DIR, "datasets")
+SCHEMA_PATH = os.path.join(DATASETS_DIR, "dataset_schema.json")
+
+MIGRATIONS = {
+    "prompts_v0_9_1.yaml": "core_v0_9_1.yaml",
+    "prompts_v1_0.yaml": "core_v1_0.yaml",
+}
+
+
+def migrate_sample(item: dict, dataset_name: str) -> dict:
+    """Transforms a single legacy sample dictionary into the clean messages schema."""
+    new_item = {
+        "name": item["name"],
+        "dataset": dataset_name,
+        "description": item["description"],
+        "catalog": item.get("catalog")
+        or "specification/{version}/catalogs/basic/catalog.json",
+    }
+
+    if "messages" in item:
+        new_item["messages"] = item["messages"]
+    elif "promptText" in item:
+        new_item["messages"] = [{"role": "user", "content": item["promptText"]}]
+    else:
+        raise ValueError(
+            f"Sample {item.get('name')} has neither messages nor promptText."
+        )
+
+    if "target" in item:
+        new_item["target"] = item["target"]
+    if "system_prompt" in item:
+        new_item["system_prompt"] = item["system_prompt"]
+    if "protocol_role" in item or "role_description" in item:
+        new_item["protocol_role"] = item.get("protocol_role") or item.get(
+            "role_description"
+        )
+    if "generation_rules" in item or "workflow_description" in item:
+        new_item["generation_rules"] = item.get("generation_rules") or item.get(
+            "workflow_description"
+        )
+    if "allowed_surface_ids" in item:
+        new_item["allowed_surface_ids"] = item["allowed_surface_ids"]
+
+    return new_item
+
+
+def main():
+    with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
+        schema = json.load(f)
+
+    for old_file, new_file in MIGRATIONS.items():
+        old_path = os.path.join(DATASETS_DIR, old_file)
+        new_path = os.path.join(DATASETS_DIR, new_file)
+
+        if not os.path.exists(old_path):
+            print(f"Skipping {old_file} (not found on disk).")
+            continue
+
+        with open(old_path, "r", encoding="utf-8") as f:
+            raw_data = yaml.safe_load(f) or []
+
+        dataset_name = os.path.splitext(new_file)[0]
+        migrated_data = [migrate_sample(sample, dataset_name) for sample in raw_data]
+
+        # Validate migrated data against updated schema
+        jsonschema.validate(instance=migrated_data, schema=schema)
+
+        # Write new modular dataset file
+        with open(new_path, "w", encoding="utf-8") as f:
+            yaml.dump(migrated_data, f, sort_keys=False, allow_unicode=True)
+        print(
+            f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)} samples)."
+        )
+
+        # Delete legacy file
+        os.remove(old_path)
+        print(f"Removed legacy file: {old_file}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/eval/bin/report_evals.py b/eval/bin/report_evals.py
index c7cbc64ae2..6371d06500 100755
--- a/eval/bin/report_evals.py
+++ b/eval/bin/report_evals.py
@@ -25,17 +25,7 @@
 
 
 def extract_accuracy(log_data: dict[str, Any]) -> float:
-    """Extracts accuracy from parsed JSON log data.
-
-    Args:
-        log_data: Parsed JSON data from inspect log dump.
-
-    Returns:
-        The accuracy score as a float.
-
-    Raises:
-        ValueError: If scores or accuracy are not found or invalid.
-    """
+    """Extracts accuracy from parsed JSON log data."""
     scores = log_data.get("results", {}).get("scores", [])
     if not scores:
         raise ValueError("No scores found in log file.")
@@ -51,84 +41,90 @@ def extract_accuracy(log_data: dict[str, Any]) -> float:
 
 
 def print_results_summary(log_data: dict[str, Any]) -> None:
-    """Prints a summary of the results for each sample.
-
-    Args:
-        log_data: Parsed JSON data from inspect log dump.
-    """
+    """Prints a summary of the results for each sample grouped by dataset."""
     samples = log_data.get("samples", [])
     print("\n=== Evaluation Results Summary ===")
-    durations = []
-    for sample in samples:
-        name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
-        scores = sample.get("scores", {})
 
-        # Algorithmic validity (a2ui_scorer)
-        a2ui_score = scores.get("a2ui_scorer", {})
-        a2ui_passed = a2ui_score.get("value") == 1.0
-        a2ui_str = "PASS" if a2ui_passed else "FAIL"
-
-        # Judging results (measured_model_graded_qa)
-        qa_score = scores.get("measured_model_graded_qa", {})
-        qa_val = qa_score.get("value", "N/A")
-
-        inference_time = None
-        for event in sample["events"]:
-            if event["event"] == "model":
-                inference_time = event.get("working_time") or event.get("duration")
-                break
-        if inference_time is None:
-            inference_time = sample.get("metadata", {}).get(
-                "evaluation_duration_seconds"
+    # Group by dataset
+    datasets: dict[str, list[dict[str, Any]]] = {}
+    for sample in samples:
+        d_name = sample.get("metadata", {}).get("dataset") or "default"
+        datasets.setdefault(d_name, []).append(sample)
+
+    all_durations = []
+    dataset_stats: dict[str, dict[str, int]] = {}
+
+    for d_name, d_samples in datasets.items():
+        total = len(d_samples)
+        passed = 0
+        print(f"\n--- Dataset: {d_name} ---")
+        for sample in d_samples:
+            name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
+            scores = sample.get("scores", {})
+
+            # Algorithmic validity (a2ui_scorer)
+            a2ui_score = scores.get("a2ui_scorer", {})
+            a2ui_passed = a2ui_score.get("value") == 1.0
+            a2ui_str = "PASS" if a2ui_passed else "FAIL"
+
+            # Judging results (measured_model_graded_qa)
+            qa_score = scores.get("measured_model_graded_qa", {})
+            qa_val = qa_score.get("value", "N/A")
+
+            inference_time = None
+            for event in sample.get("events", []):
+                if event.get("event") == "model":
+                    inference_time = event.get("working_time") or event.get("duration")
+                    break
+            if inference_time is None:
+                inference_time = sample.get("metadata", {}).get("evaluation_duration_seconds")
+            inference_time_str = (
+                f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
             )
-        inference_time_str = (
-            f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
-        )
 
-        print(
-            f"{name:<25} | Algorithmic: {a2ui_str:<4} | Judging: {qa_val:<2} |"
-            f" Inference Time: {inference_time_str}"
-        )
-
-        if not a2ui_passed or qa_val != "C":
-            if not a2ui_passed:
-                print(f"  [Algorithmic Failure Reason]:")
-                expl = a2ui_score.get("explanation") or "No explanation provided."
-                for line in expl.splitlines():
-                    print(f"    {line}")
-            if qa_val != "C":
-                print(f"  [Judging Failure Reason (Grade {qa_val})]:")
-                expl = qa_score.get("explanation") or "No explanation provided."
-                for line in expl.splitlines():
-                    print(f"    {line}")
+            sample_passed = a2ui_passed and qa_val == "C"
+            if sample_passed:
+                passed += 1
 
-        if inference_time is not None:
-            durations.append(float(inference_time))
+            print(
+                f"{name:<25} | Algorithmic: {a2ui_str:<4} | Judging: {qa_val:<2} |"
+                f" Inference Time: {inference_time_str}"
+            )
 
+            if not sample_passed:
+                if not a2ui_passed:
+                    print(f"  [Algorithmic Failure Reason]:")
+                    expl = a2ui_score.get("explanation") or "No explanation provided."
+                    for line in str(expl).splitlines():
+                        print(f"    {line}")
+                if qa_val != "C":
+                    print(f"  [Judging Failure Reason (Grade {qa_val})]:")
+                    expl = qa_score.get("explanation") or "No explanation provided."
+                    for line in str(expl).splitlines():
+                        print(f"    {line}")
+
+            if inference_time is not None:
+                all_durations.append(float(inference_time))
+
+        dataset_stats[d_name] = {"passed": passed, "total": total}
+
+    print("\n==================================")
+    print("Dataset Summary:")
+    for d_name, stats in dataset_stats.items():
+        pct = (stats["passed"] / stats["total"] * 100) if stats["total"] else 0.0
+        print(f"  {d_name:<16}: {stats['passed']}/{stats['total']} passed ({pct:.2f}%)")
+
+    if all_durations:
+        avg_duration = statistics.mean(all_durations)
+        med_duration = statistics.median(all_durations)
+        print(f"Inference Time - Average: {avg_duration:.2f}s | Median: {med_duration:.2f}s")
     print("==================================")
-    if durations:
-        avg_duration = statistics.mean(durations)
-        med_duration = statistics.median(durations)
-        print(
-            f"Inference Time - Average: {avg_duration:.2f}s | Median:"
-            f" {med_duration:.2f}s"
-        )
-        print("==================================")
 
 
 def generate_markdown_summary(
     log_data: dict[str, Any], accuracy_percentage: float, threshold: float
 ) -> str:
-    """Generates a markdown summary of the evaluation results.
-
-    Args:
-        log_data: Parsed JSON data from inspect log dump.
-        accuracy_percentage: The calculated accuracy percentage.
-        threshold: The threshold to compare against.
-
-    Returns:
-        A string containing the markdown summary.
-    """
+    """Generates a markdown summary of the evaluation results."""
     eval_spec = log_data.get("eval", {}) or {}
     task_name = eval_spec.get("task", "Unknown Task")
     model_name = eval_spec.get("model", "Unknown Model")
@@ -144,23 +140,56 @@ def generate_markdown_summary(
         f" `{threshold:.2f}%`)"
     )
     lines.append("")
+
+    samples = log_data.get("samples", []) or []
+
+    # Dataset performance summary table
+    datasets: dict[str, list[dict[str, Any]]] = {}
+    for sample in samples:
+        d_name = sample.get("metadata", {}).get("dataset") or "default"
+        datasets.setdefault(d_name, []).append(sample)
+
+    if any(k != "default" for k in datasets.keys()):
+        lines.append("#### Dataset Performance Summary")
+        lines.append("| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |")
+        lines.append("| :--- | :---: | :---: | :---: | :---: |")
+
+        for d_name, d_samples in datasets.items():
+            total = len(d_samples)
+            a2ui_pass_cnt = sum(
+                1 for s in d_samples if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
+            )
+            qa_pass_cnt = sum(
+                1 for s in d_samples if s.get("scores", {}).get("measured_model_graded_qa", {}).get("value") == "C"
+            )
+            both_pass_cnt = sum(
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
+                and s.get("scores", {}).get("measured_model_graded_qa", {}).get("value") == "C"
+            )
+            a2ui_pct = (a2ui_pass_cnt / total * 100) if total else 0.0
+            qa_pct = (qa_pass_cnt / total * 100) if total else 0.0
+            both_pct = (both_pass_cnt / total * 100) if total else 0.0
+            lines.append(
+                f"| `{d_name}` | {total} | {a2ui_pct:.1f}% | {qa_pct:.1f}% | {both_pct:.1f}% |"
+            )
+        lines.append("")
+
     lines.append("#### Sample Results")
     lines.append("| Sample / Task | Algorithmic | Judging | Inference Time | Status |")
     lines.append("| :--- | :---: | :---: | :---: | :---: |")
 
-    samples = log_data.get("samples", []) or []
     failures = []
 
     for sample in samples:
         name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
         scores = sample.get("scores", {}) or {}
 
-        # Algorithmic validity (a2ui_scorer)
         a2ui_score = scores.get("a2ui_scorer", {}) or {}
         a2ui_passed = a2ui_score.get("value") == 1.0
         a2ui_str = "PASS" if a2ui_passed else "FAIL"
 
-        # Judging results (measured_model_graded_qa)
         qa_score = scores.get("measured_model_graded_qa", {}) or {}
         qa_val = qa_score.get("value", "N/A")
 
@@ -169,7 +198,6 @@ def generate_markdown_summary(
             f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
         )
 
-        # Overall sample status
         sample_passed = a2ui_passed and qa_val == "C"
         sample_status_str = "PASS" if sample_passed else "FAIL"
 
@@ -213,14 +241,7 @@ def generate_markdown_summary(
 
 
 def load_log_data(log_path: str) -> dict[str, Any]:
-    """Runs inspect log dump to get JSON and parses it.
-
-    Args:
-        log_path: Path to the .eval log file.
-
-    Returns:
-        Parsed JSON data as a dictionary.
-    """
+    """Runs inspect log dump to get JSON and parses it."""
     dump_cmd = ["uv", "run", "inspect", "log", "dump", log_path]
     dump_output = subprocess.check_output(dump_cmd, text=True)
     result: dict[str, Any] = json.loads(dump_output)
@@ -242,22 +263,11 @@ def main() -> None:
 
     try:
         log_data = load_log_data(args.log)
-
-        # Print summary of results per sample
         print_results_summary(log_data)
-
-        try:
-            accuracy = extract_accuracy(log_data)
-            percentage = accuracy * 100
-            print(f"Pass percentage: {percentage:.2f}%")
-            sys.exit(0)
-        except ValueError as e:
-            print(f"Error: {e}")
-            sys.exit(1)
-
-    except subprocess.CalledProcessError as e:
-        print(f"Error running inspect log dump: {e}")
-        sys.exit(e.returncode)
+        accuracy = extract_accuracy(log_data)
+        percentage = accuracy * 100
+        print(f"Pass percentage: {percentage:.2f}%")
+        sys.exit(0)
     except Exception as e:
         print(f"Error processing log file: {e}")
         sys.exit(1)
diff --git a/eval/bin/run_ci_evals.py b/eval/bin/run_ci_evals.py
index f259128f49..3c476d4695 100755
--- a/eval/bin/run_ci_evals.py
+++ b/eval/bin/run_ci_evals.py
@@ -18,40 +18,29 @@
 import argparse
 import datetime
 import glob
-import json
 import os
 import shutil
 import subprocess
 import sys
 
-from report_evals import extract_accuracy, print_results_summary, load_log_data, generate_markdown_summary
+from report_evals import (
+    extract_accuracy,
+    generate_markdown_summary,
+    load_log_data,
+    print_results_summary,
+)
+
 # Automatically override Inspect AI's connection rate-limiter limit to prevent queuing delays in latency measurements
 os.environ["INSPECT_MAX_CONNECTIONS"] = "50"
 
 
 def check_threshold(percentage: float, threshold: float) -> bool:
-    """Compares percentage with threshold.
-
-    Args:
-        percentage: The calculated pass percentage.
-        threshold: The threshold to compare against.
-
-    Returns:
-        True if percentage >= threshold, False otherwise.
-    """
+    """Compares percentage with threshold."""
     return percentage >= threshold
 
 
 def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
-    """Builds the command line arguments for main.py.
-
-    Args:
-        args: Parsed command line arguments.
-        seed: The random seed to use.
-
-    Returns:
-        A list of strings representing the command.
-    """
+    """Builds the command line arguments for main.py."""
     cmd = [
         "uv",
         "run",
@@ -68,13 +57,29 @@ def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
         "--grading-model",
         args.grading_model,
     ]
-    if args.max_samples != 0:
+    if getattr(args, "dataset", None):
+        cmd.extend(["--dataset", args.dataset])
+    if getattr(args, "datasets", None):
+        cmd.extend(["--datasets", args.datasets])
+    if getattr(args, "max_samples", None) and args.max_samples != 0:
         cmd.extend(["--limit", str(args.max_samples)])
     return cmd
 
 
 def main() -> None:
     parser = argparse.ArgumentParser(description="Run A2UI evals for CI.")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help="Evaluate only a specific dataset name",
+    )
+    parser.add_argument(
+        "--datasets",
+        type=str,
+        default=None,
+        help="Comma-separated list of datasets to evaluate",
+    )
     parser.add_argument(
         "--max-samples",
         type=int,
@@ -104,7 +109,6 @@ def main() -> None:
     )
     args = parser.parse_args()
 
-    # Find eval root (directory above bin)
     script_dir = os.path.dirname(os.path.abspath(__file__))
     eval_root = os.path.dirname(script_dir)
 
@@ -143,9 +147,8 @@ def main() -> None:
 
             accuracy = extract_accuracy(log_data)
             percentage = accuracy * 100
-            print(f"Pass percentage: {percentage}%")
+            print(f"Pass percentage: {percentage:.2f}%")
 
-            # Generate and accumulate markdown summary
             try:
                 summary_md = generate_markdown_summary(
                     log_data, percentage, args.threshold
@@ -156,20 +159,16 @@ def main() -> None:
 
             if not check_threshold(percentage, args.threshold):
                 print(
-                    f"Error: Pass percentage {percentage}% is less than threshold"
-                    f" {args.threshold}%"
+                    f"Error: Pass percentage {percentage:.2f}% is less than threshold"
+                    f" {args.threshold:.2f}%"
                 )
                 all_passed = False
             else:
                 print("Pass percentage check passed.")
-        except ValueError as e:
-            print(f"Error extracting metrics: {e}")
-            all_passed = False
         except Exception as e:
             print(f"Error processing log file: {e}")
             all_passed = False
 
-    # Write consolidated summary if we have summaries
     if summaries:
         summary_path = os.path.join(eval_root, "eval_summary.md")
         try:
diff --git a/eval/datasets/core_v0_9_1.yaml b/eval/datasets/core_v0_9_1.yaml
new file mode 100644
index 0000000000..6c81f65c63
--- /dev/null
+++ b/eval/datasets/core_v0_9_1.yaml
@@ -0,0 +1,1170 @@
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diff --git a/eval/datasets/core_v1_0.yaml b/eval/datasets/core_v1_0.yaml
new file mode 100644
index 0000000000..327b78b3a5
--- /dev/null
+++ b/eval/datasets/core_v1_0.yaml
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diff --git a/eval/datasets/dataset_schema.json b/eval/datasets/dataset_schema.json
index 933bfef01f..95bbcd228f 100644
--- a/eval/datasets/dataset_schema.json
+++ b/eval/datasets/dataset_schema.json
@@ -1,45 +1,109 @@
 {
   "$schema": "http://json-schema.org/draft-07/schema#",
-  "description": "Schema for A2UI evaluation datasets.",
+  "title": "A2UI Evaluation Dataset Schema",
+  "description": "Schema for A2UI evaluation datasets and data points.",
   "type": "array",
   "items": {
     "type": "object",
     "properties": {
       "name": {
         "type": "string",
-        "description": "A unique identifier for this evaluation sample."
+        "description": "Unique identifier for this evaluation sample."
+      },
+      "dataset": {
+        "type": "string",
+        "description": "Name of the dataset this data point belongs to. If omitted, inferred from the YAML file name."
       },
       "description": {
         "type": "string",
-        "description": "A human-readable description of what this sample tests."
+        "description": "Human-readable explanation of what this sample tests."
+      },
+      "catalog": {
+        "type": "string",
+        "description": "Path to a custom catalog JSON file. Supports the '{version}' placeholder."
+      },
+      "messages": {
+        "type": "array",
+        "description": "Ordered chat completion messages preceding and including the final UI generation request.",
+        "items": {
+          "type": "object",
+          "properties": {
+            "role": {
+              "type": "string",
+              "enum": ["user", "assistant", "system", "tool"],
+              "description": "The role of the message sender."
+            },
+            "content": {
+              "type": "string",
+              "description": "Text content of the message."
+            },
+            "tool_calls": {
+              "type": "array",
+              "description": "List of tool calls made by the assistant in this turn.",
+              "items": {
+                "type": "object",
+                "properties": {
+                  "id": { "type": "string" },
+                  "type": { "type": "string", "enum": ["function"] },
+                  "function": {
+                    "type": "object",
+                    "properties": {
+                      "name": { "type": "string" },
+                      "arguments": { "type": "string" }
+                    },
+                    "required": ["name", "arguments"]
+                  }
+                },
+                "required": ["id", "function"]
+              }
+            },
+            "tool_call_id": {
+              "type": "string",
+              "description": "Identifier linking a tool response turn to its preceding assistant tool call."
+            }
+          },
+          "required": ["role"]
+        }
       },
       "promptText": {
         "type": "string",
-        "description": "The user input prompt that is sent to the LLM."
+        "description": "Legacy single-turn user prompt (deprecated; automatically migrated to messages)."
       },
       "target": {
         "type": "string",
-        "description": "The expected outcome or grading criteria for the model's generated output. Used by the LLM-as-a-judge scorer. If omitted, defaults to the value of `description`."
+        "description": "Expected outcome and rubric criteria for LLM-as-a-judge scoring. If omitted, defaults to description."
       },
-      "catalog": {
+      "system_prompt": {
+        "type": "string",
+        "description": "Domain-specific system prompt unrelated to A2UI (e.g., application persona, business rules). Merged with the A2UI SDK system prompt during solver execution."
+      },
+      "protocol_role": {
         "type": "string",
-        "description": "Path to the catalog json file. Corresponds to the catalog file path loaded into the A2uiSchemaManager. If omitted, the default is loaded from eval/datasets/defaults.py. Supports `{version}` placeholder which dynamically resolves to the target version directory name (e.g. 'v0_9_1' or 'v1_0')."
+        "description": "A2UI protocol role instruction passed to the SDK prompt generator."
       },
       "role_description": {
         "type": "string",
-        "description": "Corresponds to the `role_description` parameter of `A2uiSchemaManager.generate_system_prompt()`. If omitted, the default is loaded from eval/datasets/defaults.py."
+        "description": "Legacy alias for protocol_role."
+      },
+      "generation_rules": {
+        "type": "string",
+        "description": "A2UI protocol workflow rules appended to the SDK workflow prompt."
       },
       "workflow_description": {
         "type": "string",
-        "description": "Corresponds to the `workflow_description` parameter of `A2uiSchemaManager.generate_system_prompt()`. If omitted, the default is loaded from eval/datasets/defaults.py."
+        "description": "Legacy alias for generation_rules."
       },
       "allowed_surface_ids": {
         "type": "array",
-        "items": {"type": "string"},
-        "description": "An optional list of allowed surface IDs. The first item is used as the default fallback."
+        "items": { "type": "string" },
+        "description": "Allowed surface IDs for the output."
       }
     },
-    "required": ["name", "description", "promptText"],
+    "required": ["name", "description", "catalog"],
+    "anyOf": [
+      { "required": ["messages"] },
+      { "required": ["promptText"] }
+    ],
     "additionalProperties": false
   }
 }
diff --git a/eval/datasets/prompts_v0_9_1.yaml b/eval/datasets/prompts_v0_9_1.yaml
deleted file mode 100644
index 264461aab2..0000000000
--- a/eval/datasets/prompts_v0_9_1.yaml
+++ /dev/null
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diff --git a/eval/datasets/prompts_v1_0.yaml b/eval/datasets/prompts_v1_0.yaml
deleted file mode 100644
index 29126ceb74..0000000000
--- a/eval/datasets/prompts_v1_0.yaml
+++ /dev/null
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diff --git a/eval/main.py b/eval/main.py
index 6265653110..66da843564 100644
--- a/eval/main.py
+++ b/eval/main.py
@@ -14,7 +14,6 @@
 
 import os
 import sys
-import traceback
 import argparse
 from inspect_ai import eval_set
 from tasks import a2ui_v0_9_1_eval, a2ui_v1_0_eval
@@ -31,6 +30,18 @@ def main() -> None:
         action="store_true",
         help="Run a quick sanity check (2 samples, gemini-3.1-flash-lite, 0 retry)",
     )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help="Evaluate only a specific dataset (e.g. 'customer_a_data' or 'core_v0_9_1')",
+    )
+    parser.add_argument(
+        "--datasets",
+        type=str,
+        default=None,
+        help="Comma-separated list of datasets to evaluate",
+    )
     parser.add_argument(
         "--model",
         type=str,
@@ -92,6 +103,11 @@ def main() -> None:
     retry_attempts = 0 if args.sanity else args.max_retries
     sample_shuffle = None if args.sanity else args.sample_shuffle
 
+    # Resolve dataset filters
+    selected_dataset = args.dataset
+    if args.datasets:
+        selected_dataset = args.datasets
+
     # Parse and validate strategies
     selected_strategies = []
     raw_strategies = args.strategies if args.strategies else ["direct", "subagent_tool"]
@@ -110,11 +126,19 @@ def main() -> None:
             )
         if strat in ["express", "elemental"]:
             tasks.append(
-                a2ui_v1_0_eval(strategy=strat, grading_model=args.grading_model)
+                a2ui_v1_0_eval(
+                    strategy=strat,
+                    grading_model=args.grading_model,
+                    dataset=selected_dataset,
+                )
             )
         else:
             tasks.append(
-                a2ui_v0_9_1_eval(strategy=strat, grading_model=args.grading_model)
+                a2ui_v0_9_1_eval(
+                    strategy=strat,
+                    grading_model=args.grading_model,
+                    dataset=selected_dataset,
+                )
             )
 
     eval_set_kwargs = {
@@ -124,6 +148,7 @@ def main() -> None:
         "retry_attempts": retry_attempts,
         "limit": limit,
         "sample_shuffle": sample_shuffle,
+        "log_dir_allow_dirty": True,
     }
     model_args = {}
     if args.thinking_budget is not None:
diff --git a/eval/tasks.py b/eval/tasks.py
index c7c3b0bbc8..798599e513 100644
--- a/eval/tasks.py
+++ b/eval/tasks.py
@@ -30,8 +30,8 @@
 
 # Paths relative to the eval directory where we run inspect
 CURRENT_DIR = Path(__file__).resolve().parent
-DATASET_V0_9_1_PATH = (CURRENT_DIR / "datasets/prompts_v0_9_1.yaml").resolve()
-DATASET_V1_0_PATH = (CURRENT_DIR / "datasets/prompts_v1_0.yaml").resolve()
+DATASET_V0_9_1_PATH = (CURRENT_DIR / "datasets/core_v0_9_1.yaml").resolve()
+DATASET_V1_0_PATH = (CURRENT_DIR / "datasets/core_v1_0.yaml").resolve()
 
 
 @scorer(metrics=[])
@@ -65,6 +65,7 @@ def a2ui_v0_9_1_eval(
     list_models: bool = False,
     grading_model: str = "google/gemini-3.5-flash",
     strategy: str = "direct",
+    dataset: str | list[str] | None = None,
 ) -> Task:
     """Evaluation task for A2UI v0.9.1 protocol generation.
 
@@ -72,6 +73,7 @@ def a2ui_v0_9_1_eval(
         list_models: Whether to list available Gemini models and exit.
         grading_model: The model to use for LLM-as-a-judge grading.
         strategy: The evaluation strategy to use (e.g., 'direct').
+        dataset: Specific dataset name or list of dataset names.
 
     Returns:
         An Inspect Task object configured for A2UI v0.9.1 evaluation.
@@ -92,21 +94,24 @@ def a2ui_v0_9_1_eval(
             scorer=[dummy_scorer()],
         )
 
-    active_dataset_path = DATASET_V0_9_1_PATH
     active_version = "0.9.1"
     default_catalog_path = "specification/v0_9_1/catalogs/basic/catalog.json"
-
     format_name = strategy if strategy in ["express", "elemental"] else "json"
 
-    dataset = load_a2ui_dataset(
-        str(active_dataset_path),
+    file_path = None
+    if not dataset and DATASET_V0_9_1_PATH.exists():
+        file_path = str(DATASET_V0_9_1_PATH)
+
+    dataset_obj = load_a2ui_dataset(
+        file_path=file_path,
+        dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
         format_name=format_name,
     )
 
     return Task(
-        dataset=dataset,
+        dataset=dataset_obj,
         solver=get_solver(strategy, version=active_version),
         scorer=[
             a2ui_scorer(version=active_version),
@@ -122,6 +127,7 @@ def a2ui_v1_0_eval(
     list_models: bool = False,
     grading_model: str = "google/gemini-3.5-flash",
     strategy: str = "express",
+    dataset: str | list[str] | None = None,
 ) -> Task:
     """Evaluation task for A2UI v1.0 protocol generation.
 
@@ -129,6 +135,7 @@ def a2ui_v1_0_eval(
         list_models: Whether to list available Gemini models and exit.
         grading_model: The model to use for LLM-as-a-judge grading.
         strategy: The evaluation strategy to use (e.g., 'express').
+        dataset: Specific dataset name or list of dataset names.
 
     Returns:
         An Inspect Task object configured for A2UI v1.0 evaluation.
@@ -149,21 +156,24 @@ def a2ui_v1_0_eval(
             scorer=[dummy_scorer()],
         )
 
-    active_dataset_path = DATASET_V1_0_PATH
     active_version = "1.0"
     default_catalog_path = "specification/v1_0/catalogs/basic/catalog.json"
-
     format_name = strategy if strategy in ["express", "elemental"] else "json"
 
-    dataset = load_a2ui_dataset(
-        str(active_dataset_path),
+    file_path = None
+    if not dataset and DATASET_V1_0_PATH.exists():
+        file_path = str(DATASET_V1_0_PATH)
+
+    dataset_obj = load_a2ui_dataset(
+        file_path=file_path,
+        dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
         format_name=format_name,
     )
 
     return Task(
-        dataset=dataset,
+        dataset=dataset_obj,
         solver=get_solver(strategy, version=active_version),
         scorer=[
             a2ui_scorer(version=active_version),
diff --git a/eval/tests/test_dataset.py b/eval/tests/test_dataset.py
index 790a6bb989..a737bdaa6e 100644
--- a/eval/tests/test_dataset.py
+++ b/eval/tests/test_dataset.py
@@ -12,27 +12,31 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 from pathlib import Path
 import pytest
+from inspect_ai.model import ChatMessageUser, ChatMessageAssistant, ChatMessageTool
 from a2ui_eval.dataset import load_a2ui_dataset
 
 
 def test_load_a2ui_dataset(tmp_path: Path) -> None:
-    # Create a dummy YAML file
     d = tmp_path / "sub"
     d.mkdir()
     p = d / "dummy_prompts.yaml"
     p.write_text("""
 - name: testPrompt
   description: A test prompt.
-  promptText: "Test input"
+  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  messages:
+    - role: user
+      content: "Test input"
 """)
 
-    dataset = load_a2ui_dataset(str(p))
+    dataset = load_a2ui_dataset(file_path=str(p))
 
     assert len(dataset) == 1
-    assert dataset[0].input == "Test input"
+    assert len(dataset[0].input) == 1
+    assert isinstance(dataset[0].input[0], ChatMessageUser)
+    assert dataset[0].input[0].content == "Test input"
     assert dataset[0].target == "A test prompt."
     assert dataset[0].metadata is not None
     assert dataset[0].metadata["name"] == "testPrompt"
@@ -40,7 +44,7 @@ def test_load_a2ui_dataset(tmp_path: Path) -> None:
 
 def test_load_a2ui_dataset_file_not_found() -> None:
     with pytest.raises(FileNotFoundError):
-        load_a2ui_dataset("non_existent_file.yaml")
+        load_a2ui_dataset(file_path="non_existent_file.yaml")
 
 
 def test_load_a2ui_dataset_with_version(tmp_path: Path) -> None:
@@ -50,12 +54,51 @@ def test_load_a2ui_dataset_with_version(tmp_path: Path) -> None:
     p.write_text("""
 - name: testPrompt
   description: A test prompt.
-  promptText: "Test input"
   catalog: "path/{version}/catalog.json"
+  messages:
+    - role: user
+      content: "Test input"
 """)
 
-    dataset = load_a2ui_dataset(str(p), version="0.9.1")
+    dataset = load_a2ui_dataset(file_path=str(p), version="0.9.1")
 
     assert len(dataset) == 1
     assert dataset[0].metadata is not None
     assert dataset[0].metadata["catalog"] == "path/v0_9_1/catalog.json"
+
+
+def test_load_a2ui_dataset_multi_turn(tmp_path: Path) -> None:
+    d = tmp_path / "sub"
+    d.mkdir()
+    p = d / "dummy_multi_turn.yaml"
+    p.write_text("""
+- name: multiTurnPrompt
+  dataset: customer_data
+  description: A multi turn prompt with tool calls.
+  catalog: "specification/v0_9_1/catalogs/basic/catalog.json"
+  system_prompt: "Domain prompt"
+  messages:
+    - role: user
+      content: "Lookup account 123"
+    - role: assistant
+      content: "Checking account"
+      tool_calls:
+        - id: call_1
+          type: function
+          function:
+            name: lookup
+            arguments: '{"id": "123"}'
+    - role: tool
+      tool_call_id: call_1
+      content: '{"balance": 500}'
+    - role: user
+      content: "Render my balance card"
+""")
+
+    dataset = load_a2ui_dataset(file_path=str(p), dataset="customer_data")
+    assert len(dataset) == 1
+    assert len(dataset[0].input) == 4
+    assert isinstance(dataset[0].input[0], ChatMessageUser)
+    assert isinstance(dataset[0].input[1], ChatMessageAssistant)
+    assert isinstance(dataset[0].input[2], ChatMessageTool)
+    assert dataset[0].metadata["system_prompt"] == "Domain prompt"
diff --git a/specification/proposals/eval_suite_expansion.md b/specification/proposals/eval_suite_expansion.md
new file mode 100644
index 0000000000..78b1cb5ff9
--- /dev/null
+++ b/specification/proposals/eval_suite_expansion.md
@@ -0,0 +1,671 @@
+# Evaluation dataset expansion and agentic data points design
+
+_Status: Draft_
+
+_Author: A2UI Team_
+
+_Created: 2026-07-23_
+
+## Background
+
+The A2UI evaluation framework located in [eval/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval) tests whether Large Language Models generate valid and contextually appropriate A2UI JSON payloads. The current framework runs Inspect AI tasks ([eval/tasks.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tasks.py)) against prompt datasets stored in YAML format. Datasets are encrypted at rest using Transcrypt to prevent data contamination from web crawlers.
+
+While the existing system tests basic generation well, real-world agentic applications present scenarios that the current setup cannot evaluate:
+
+1. **Monolithic dataset files**: All evaluation samples reside in monolithic YAML files. There is no mechanism to isolate data points into separate modular datasets (such as `core_v0_9_1`, `customer_a_data`, or `banking_app`), execute only a specific dataset, or view separate reporting tables per dataset.
+2. **Disconnected single-string prompts**: The existing schema ([eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json)) uses a standalone `promptText` string. Real agentic applications pass a structured `messages` list containing prior user queries, assistant responses, and tool calls with execution outputs before the model generates UI.
+3. **Domain system prompts and custom catalogs**: In production applications, models receive domain-specific system prompts (such as customer support rules, flight booking domain logic, or medical guidelines) that are separate from A2UI protocol instructions. Applications also define custom component catalogs with specialized UI elements. The current eval engine only injects A2UI protocol instructions without merging custom domain system prompts or supporting multi-turn chat history.
+4. **Contributor onboarding**: Contributors and external teams creating new data points need a clear, step-by-step workflow covering decryption, schema verification, local evaluation execution, and safe encrypted commits.
+
+This proposal outlines the technical design for expanding the evaluation framework to support separate modular datasets, standard LLM chat completion message formats (`messages`), custom catalogs with merged domain system prompts, and a contributor guide. All existing data points are cleanly migrated into modular dataset files under `eval/datasets/`, eliminating legacy monolithic files and obsolete field aliases.
+
+---
+
+## Architecture overview
+
+The expanded evaluation pipeline adds dataset discovery, standard LLM `messages` array parsing, system prompt merging, and dataset-aware reporting to Inspect AI.
+
+```mermaid
+flowchart TD
+    subgraph Data Layer [Encrypted YAML Datasets]
+        CoreDataset["Core v0.9.1 Dataset<br>(core_v0_9_1.yaml)"]
+        CustDataset["Customer A Dataset<br>(customer_a_data.yaml)"]
+        CustomDatasetN["Custom Datasets<br>(*.yaml)"]
+    end
+
+    subgraph Loader [Dataset Loader & Schema Validator]
+        Validator["JSON Schema Validator<br>(dataset_schema.json)"]
+        DatasetResolver["Dataset Resolver<br>(filter by --dataset / --datasets)"]
+    end
+
+    subgraph Engine [Inspect AI Evaluation Engine]
+        PromptMerger["System Prompt Merger<br>(Domain Prompt + A2UI SDK Schema/Rules)"]
+        MessageBuilder["Chat Messages Builder<br>(User + Assistant + Tool Calls in messages)"]
+        Inference["Model Inference<br>(Final UI Generation Turn)"]
+    end
+
+    subgraph Scoring [Evaluation & Scoring]
+        AlgScorer["a2ui_scorer<br>(Catalog Schema & Tree Integrity)"]
+        JudgeScorer["measured_model_graded_qa<br>(LLM Judge on Target Criteria)"]
+    end
+
+    subgraph Reporting [Dataset-Aware Reporting]
+        ConsoleSummary["CLI Output Table<br>(Grouped by Dataset)"]
+        MarkdownReport["eval_summary.md<br>(Per-Dataset Breakdown)"]
+    end
+
+    CoreDataset --> Validator
+    CustDataset --> Validator
+    CustomDatasetN --> Validator
+    Validator --> DatasetResolver
+    DatasetResolver --> PromptMerger
+    DatasetResolver --> MessageBuilder
+    PromptMerger --> Inference
+    MessageBuilder --> Inference
+    Inference --> AlgScorer
+    Inference --> JudgeScorer
+    AlgScorer --> ConsoleSummary
+    JudgeScorer --> ConsoleSummary
+    ConsoleSummary --> MarkdownReport
+```
+
+---
+
+## 1. Separate datasets of data points
+
+### Directory organization
+
+Dataset files will be organized under [eval/datasets/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) as separate modular YAML files. The legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) are migrated directly into named dataset files:
+
+```
+eval/datasets/
+├── .gitattributes                  # Transcrypt filter rules
+├── dataset_schema.json             # Updated JSON schema for data points
+├── defaults.py                     # Default prompt templates and catalog paths
+├── core_v0_9_1.yaml                # Migrated core dataset for v0.9.1 protocol
+├── core_v1_0.yaml                  # Migrated core dataset for v1.0 protocol
+├── customer_a_data.yaml            # Customer A specific agentic scenarios
+└── flight_booking.yaml             # Domain-specific multi-turn dataset
+```
+
+Each YAML file in `eval/datasets/` represents a distinct evaluation dataset named after its filename (e.g. `customer_a_data.yaml` defines the `customer_a_data` dataset, and `core_v0_9_1.yaml` defines the `core_v0_9_1` dataset). Alternatively, a sample can specify an explicit `dataset` property in its metadata.
+
+### Dataset resolution and CLI execution
+
+The CLI runner in [eval/main.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/main.py) and the CI runner in [eval/bin/run_ci_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/run_ci_evals.py) will support filtering by dataset:
+
+```bash
+# Run all datasets (default)
+uv run main.py
+
+# Run only a specific dataset
+uv run main.py --dataset customer_a_data
+
+# Run multiple specific datasets
+uv run main.py --datasets customer_a_data,core_v0_9_1
+
+# Run a quick sanity check on a specific dataset
+uv run main.py --dataset customer_a_data --sanity
+```
+
+#### CLI argument specifications
+
+* `--dataset <name>`: Evaluates only samples belonging to the named dataset.
+* `--datasets <name1,name2>`: Evaluates samples belonging to any of the listed datasets (comma-separated).
+* When no dataset flag is supplied, the framework defaults to evaluating all active datasets or the default core datasets.
+
+### Dataset-aware reporting and summary tables
+
+The evaluation reporting tools in [eval/bin/report_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/report_evals.py) will group and calculate metrics per dataset:
+
+#### Console output format
+
+```text
+=== Evaluation Results Summary ===
+
+--- Dataset: customer_a_data (Pass Rate: 100.00%) ---
+customer_a_flight_select  | Algorithmic: PASS | Judging: C  | Inference Time: 3.20s
+customer_a_seat_picker    | Algorithmic: PASS | Judging: C  | Inference Time: 2.80s
+
+--- Dataset: core_v0_9_1 (Pass Rate: 90.00%) ---
+contact_form              | Algorithmic: PASS | Judging: C  | Inference Time: 1.90s
+data_binding_list         | Algorithmic: PASS | Judging: P  | Inference Time: 2.10s
+  [Judging Failure Reason (Grade P)]:
+    Minor cosmetic variation in column label.
+
+==================================
+Dataset Summary:
+  customer_a_data : 2/2 passed (100.00%)
+  core_v0_9_1     : 9/10 passed (90.00%)
+Overall Pass Rate: 91.67% (Threshold: 90.00%)
+Inference Time - Average: 2.50s | Median: 2.45s
+==================================
+```
+
+#### Markdown summary format (`eval_summary.md`)
+
+The generated markdown summary file will include a top-level dataset summary table followed by detailed per-dataset breakdowns:
+
+```markdown
+### Evaluation Summary: a2ui_v0_9_1_eval
+- **Status**: PASS
+- **Model**: `google/gemini-3.5-flash`
+- **Overall Pass Percentage**: `91.67%` (Threshold: `90.00%`)
+
+#### Dataset Performance Summary
+| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |
+| :--- | :---: | :---: | :---: | :---: |
+| `customer_a_data` | 2 | 100.0% | 100.0% | 100.0% |
+| `core_v0_9_1` | 10 | 100.0% | 90.0% | 90.0% |
+
+#### Detailed Dataset Results: customer_a_data
+| Sample / Task | Algorithmic | Judging | Inference Time | Status |
+| :--- | :---: | :---: | :---: | :---: |
+| customer_a_flight_select | PASS | C | 3.20s | PASS |
+| customer_a_seat_picker | PASS | C | 2.80s | PASS |
+```
+
+---
+
+## 2. Standard LLM chat schema and field reference guide
+
+To align the evaluation format with industry-standard LLM chat completion APIs (such as OpenAI Chat Completions, Anthropic Messages API, and Inspect AI's internal `state.messages`), conversational turns and user prompts are consolidated under the familiar **`messages`** array. Every data point explicitly declares its required **`catalog`** path.
+
+### Standalone field reference
+
+| Field Name | Type | Required? | Clear Explanation & Purpose |
+| :--- | :--- | :--- | :--- |
+| **`name`** | string | **Yes** | Unique identifier for this evaluation sample (e.g. `customer_a_login_form`). Used in logs and result tables. |
+| **`description`** | string | **Yes** | Human-readable explanation of what this test case verifies. |
+| **`catalog`** | string | **Yes** | **Path to the component catalog JSON file** defining available UI widgets and validation functions. Supports the `{version}` placeholder (e.g. `specification/{version}/catalogs/basic/catalog.json`). Required on every data point so solvers and scorers explicitly know which component schema and rules apply. |
+| **`messages`** | array of message objects | **Yes** | **The conversation messages.** An ordered list of chat turns preceding and including the final UI request. Each item specifies a `role` (`user`, `assistant`, `system`, or `tool`), `content`, and optional `tool_calls` or `tool_call_id`. |
+| **`target`** | string | No | **The primary grading criteria and expected UI outcome.** Used by the LLM-as-a-judge (`measured_model_graded_qa`) to evaluate whether the generated output meets requirements. If omitted, defaults to the value of `description`. |
+| **`dataset`** | string | No | The name of the dataset module this sample belongs to. If omitted, the loader infers it from the YAML filename. |
+| **`system_prompt`** | string | No | **Domain-specific system instructions** representing the host application (e.g., application persona, customer service rules, or API constraints). The eval engine merges this domain prompt with the A2UI protocol instructions. |
+| **`protocol_role`** | string | No | **The A2UI persona instruction passed to the SDK prompt generator.** Specifies the high-level role for UI generation (Default: *"You are an AI assistant. Based on the following request, generate a stream of JSON messages that conform to the provided JSON Schemas."*). Most contributors can leave this omitted. |
+| **`generation_rules`** | string | No | **Technical protocol constraints appended to the SDK workflow prompt.** Defines formatting rules such as required surface ID naming, component root ID constraints, or adjacency list rules. Most contributors can leave this omitted to use standard protocol defaults. |
+| **`allowed_surface_ids`** | array of strings | No | List of valid surface identifiers the model is allowed to target (Default: `["main"]`). |
+
+---
+
+### Detailed breakdown of key concepts
+
+#### 1. Why `catalog` is required
+Every A2UI evaluation requires a component catalog. Making `catalog` mandatory removes ambiguity for contributors, solver prompt generators, and validators. A contributor always explicitly declares the catalog path (such as `specification/{version}/catalogs/basic/catalog.json` for standard protocol widgets or a domain catalog path like `specification/{version}/catalogs/customer_a/catalog.json`).
+
+#### 2. Why `messages` is the preferred terminology
+Across the LLM ecosystem (OpenAI, Anthropic, LiteLLM, Ollama, and Inspect AI), `messages` is the universal term for chat interaction sequences:
+* **Direct framework alignment**: Inspect AI stores conversation turns in `state.messages`. Using `messages` in our YAML schema creates a 1:1 conceptual mapping.
+* **Avoids ambiguity**: The word "context" is overloaded in AI (often referring to RAG retrieval chunks, context windows, or A2UI action event contexts like `action.event.context`). `messages` unambiguously refers to the chat history.
+* **Consistent single-turn and multi-turn authoring**:
+  * **Single-turn prompt**: `messages` contains a single user turn (`role: user`, `content: "Create a login form"`).
+  * **Multi-turn conversation**: `messages` contains prior user, assistant, and tool turns, ending with the user turn that prompts the UI response.
+
+#### 3. `target` vs `description`
+* **`description`** is a high-level summary of what the test case does, used for human documentation and readability in test manifests.
+* **`target`** is the detailed rubric evaluated by the grading model. It specifies the exact expectations for the UI, such as required component types (e.g. "Must contain a Card with a TextField and a primary Button"), data bindings (e.g. "TextField must bind to `/auth/email`"), and interactive actions.
+
+#### 4. `system_prompt` vs `protocol_role`
+* **`system_prompt`** represents the **domain application persona**. In a real application, an agent has domain rules unrelated to A2UI (for example: *"You are the Acme Airlines Agent. Always confirm passenger baggage before checkout."*).
+* **`protocol_role`** represents the **A2UI protocol prompt**. This is the standard SDK instruction telling the LLM that it must output structured JSON conforming to the A2UI schema.
+* The eval engine merges both: domain instructions are presented first, followed by protocol instructions.
+
+#### 5. `generation_rules`
+* This field controls the technical A2UI rules injected into the prompt's `## Workflow Description:` section.
+* Standard protocol rules (e.g. *"Construct the UI on surfaceId 'main'"* and *"Ensure there is one root component with id 'root'"*) are injected by default.
+* If a test case needs custom protocol behavior (for example, rendering to a surface named `'sidebar'`), the contributor specifies those extra rules in `generation_rules`.
+
+---
+
+### JSON schema specification
+
+The schema in [eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json) validates the clean dataset structure with `catalog` required:
+
+```json
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "A2UI Evaluation Dataset Schema",
+  "description": "Schema for A2UI evaluation datasets and data points.",
+  "type": "array",
+  "items": {
+    "type": "object",
+    "properties": {
+      "name": {
+        "type": "string",
+        "description": "Unique identifier for this evaluation sample."
+      },
+      "dataset": {
+        "type": "string",
+        "description": "Name of the dataset this data point belongs to. If omitted, inferred from the YAML file name."
+      },
+      "description": {
+        "type": "string",
+        "description": "Human-readable explanation of what this sample tests."
+      },
+      "catalog": {
+        "type": "string",
+        "description": "Path to a custom catalog JSON file. Supports the '{version}' placeholder."
+      },
+      "messages": {
+        "type": "array",
+        "description": "Ordered chat completion messages preceding and including the final UI generation request.",
+        "items": {
+          "type": "object",
+          "properties": {
+            "role": {
+              "type": "string",
+              "enum": ["user", "assistant", "system", "tool"],
+              "description": "The role of the message sender."
+            },
+            "content": {
+              "type": "string",
+              "description": "Text content of the message."
+            },
+            "tool_calls": {
+              "type": "array",
+              "description": "List of tool calls made by the assistant in this turn.",
+              "items": {
+                "type": "object",
+                "properties": {
+                  "id": { "type": "string" },
+                  "type": { "type": "string", "enum": ["function"] },
+                  "function": {
+                    "type": "object",
+                    "properties": {
+                      "name": { "type": "string" },
+                      "arguments": { "type": "string" }
+                    },
+                    "required": ["name", "arguments"]
+                  }
+                },
+                "required": ["id", "function"]
+              }
+            },
+            "tool_call_id": {
+              "type": "string",
+              "description": "Identifier linking a tool response turn to its preceding assistant tool call."
+            }
+          },
+          "required": ["role"]
+        }
+      },
+      "target": {
+        "type": "string",
+        "description": "Expected outcome and rubric criteria for LLM-as-a-judge scoring. If omitted, defaults to description."
+      },
+      "system_prompt": {
+        "type": "string",
+        "description": "Domain-specific system prompt unrelated to A2UI (e.g., application persona, business rules). Merged with the A2UI SDK system prompt during solver execution."
+      },
+      "protocol_role": {
+        "type": "string",
+        "description": "A2UI protocol role instruction passed to the SDK prompt generator."
+      },
+      "generation_rules": {
+        "type": "string",
+        "description": "A2UI protocol workflow rules appended to the SDK workflow prompt."
+      },
+      "allowed_surface_ids": {
+        "type": "array",
+        "items": { "type": "string" },
+        "description": "Allowed surface IDs for the output."
+      }
+    },
+    "required": ["name", "description", "catalog", "messages"],
+    "additionalProperties": false
+  }
+}
+```
+
+### Example multi-turn agentic data point (YAML)
+
+Below is an example of an agentic data point representing a flight booking assistant that interacts via tools before rendering a custom A2UI interface:
+
+```yaml
+- name: customer_a_flight_confirmation
+  dataset: customer_a_data
+  description: Multi-turn flight booking agent renders confirmation card with custom catalog.
+  catalog: "specification/{version}/catalogs/customer_a/catalog.json"
+  system_prompt: |
+    You are the Acme Airlines Booking Agent. Assist users with flight reservations.
+    Always confirm passenger details and baggage selections before finalizing bookings.
+  messages:
+    - role: user
+      content: "I want to book flight AC-402 for Jane Doe."
+    - role: assistant
+      content: "Let me check the flight details and available seats for flight AC-402."
+      tool_calls:
+        - id: call_flight_lookup_001
+          type: function
+          function:
+            name: lookup_flight
+            arguments: '{"flight_number": "AC-402"}'
+    - role: tool
+      tool_call_id: call_flight_lookup_001
+      content: '{"status": "available", "flight": "AC-402", "origin": "SFO", "dest": "JFK", "departure": "2026-08-15T08:00:00Z", "price": 350.00}'
+    - role: assistant
+      content: "Flight AC-402 from SFO to JFK is available for $350.00. Would you like to select a seat and confirm?"
+    - role: user
+      content: "Yes, please show me the booking confirmation UI with a button to confirm and an option to add baggage."
+  target: |
+    The response must generate valid A2UI JSON containing:
+    1. A Card or Column root container.
+    2. Text components displaying the flight number (AC-402), route (SFO to JFK), and price ($350.00).
+    3. A CheckBox component bound to '/booking/addBaggage' or similar path.
+    4. A primary Button labeled 'Confirm Booking' with an event action.
+```
+
+### Example single-turn data point (YAML)
+
+Below is an example of a migrated single-turn core data point:
+
+```yaml
+- name: contact_form
+  dataset: core_v0_9_1
+  description: Renders a contact form with email and submit button.
+  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  messages:
+    - role: user
+      content: "Create a contact form with an email text field and submit button."
+  target: |
+    The response must contain a Card or Column with a TextField bound to '/contact/email' and a primary Button.
+```
+
+---
+
+## 3. Eval engine execution mechanics
+
+When executing an evaluation sample, the solver in [eval/a2ui_eval/strategies/format.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/strategies/format.py) coordinates prompt merging, chat message list assembly, inference, and scoring.
+
+### Step 1: System prompt generation and merging
+
+The solver merges the domain-specific system prompt with the protocol instructions generated by the A2UI SDK:
+
+1. **SDK prompt generation**: The A2UI SDK `TransportPromptGenerator` loads the required `catalog` and generates protocol rules, JSON schemas, component catalog definitions, and output tag requirements (`<a2ui-json>`).
+2. **Domain prompt merging**: If the data point includes a `system_prompt`, the solver combines them into a unified system instruction:
+
+```python
+def merge_system_prompts(domain_prompt: str, a2ui_prompt: str) -> str:
+    """Merges domain-specific rules with A2UI protocol instructions."""
+    parts = []
+    if domain_prompt.strip():
+        parts.append(f"## Domain Instructions\n{domain_prompt.strip()}")
+    if a2ui_prompt.strip():
+        parts.append(f"## UI Protocol Instructions\n{a2ui_prompt.strip()}")
+    return "\n\n".join(parts)
+```
+
+3. The merged prompt is inserted as the first message (`ChatMessageSystem`) in Inspect AI's `state.messages`.
+
+### Step 2: Chat messages assembly
+
+The solver populates `state.messages` directly from the sample's `messages` array:
+
+* **User turns**: Converted to `ChatMessageUser(content=turn["content"])`.
+* **Assistant turns**: Converted to `ChatMessageAssistant(content=turn.get("content", ""), tool_calls=turn.get("tool_calls"))`.
+* **Tool turns**: Converted to `ChatMessageTool(content=turn["content"], tool_call_id=turn["tool_call_id"])`.
+
+The final turn in `messages` triggers the assistant's UI generation response.
+
+### Step 3: Model inference
+
+The solver invokes `measured_generate()`, passing the assembled message list to the model. The model generates its completion on the final turn.
+
+### Step 4: Multi-stage scoring
+
+1. **Programmatic validation ([a2ui_scorer](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py))**:
+   * Resolves the required `catalog` path specified in the data point.
+   * Parses JSON from `<a2ui-json>` tags.
+   * Runs the catalog validator ([A2uiValidator](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/agent_sdks/python/a2ui_agent/README.md)) to verify schema adherence, parent-child references, and root component presence.
+2. **LLM judge scoring ([measured_model_graded_qa](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py))**:
+   * Evaluates the generated UI against the `target` criteria using the grading model (e.g. `google/gemini-3.5-flash`).
+   * Produces a grade of `C` (Correct), `P` (Partial Credit), or `I` (Incorrect).
+
+---
+
+## 4. Contributor guide: checking in data points and encryption
+
+This step-by-step guide is for contributors adding or updating data points in the A2UI evaluation framework.
+
+### Why data points are encrypted
+
+Evaluation datasets are encrypted at rest in Git using **Transcrypt**. This prevents public search engines and automated LLM training web crawlers from indexing our test prompts and answers, avoiding artificial score inflation from model memorization.
+
+### Step 1: Initial decryption setup
+
+Before modifying or viewing datasets, initialize Transcrypt with the repository evaluation password:
+
+```bash
+cd eval
+bin/transcrypt -p <PASSWORD>
+```
+
+*(Request the password from an A2UI maintainer if you do not have it.)*
+
+Once decrypted, all YAML files in `eval/datasets/` appear as regular plaintext files on your local filesystem.
+
+### Step 2: Create or update a dataset file
+
+1. Create a new YAML file under `eval/datasets/<dataset_name>.yaml` (for example, `eval/datasets/customer_a_data.yaml`).
+2. Add your evaluation samples following the standard chat completion format with a required `catalog`:
+
+```yaml
+- name: customer_a_login_form
+  dataset: customer_a_data
+  description: Tests generation of a secure login card with email and password fields.
+  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  system_prompt: "You are the Customer A Authentication Agent. Help users sign in."
+  messages:
+    - role: user
+      content: "Create a login form with email, password, and a submit button."
+  target: |
+    The response must contain:
+    1. A Card or Column root component.
+    2. A TextField for email bound to '/auth/email'.
+    3. A TextField for password bound to '/auth/password'.
+    4. A primary Button labeled 'Sign In'.
+```
+
+### Step 3: Validate your dataset against the schema
+
+Run the automated dataset schema test before running evaluations:
+
+```bash
+uv run python -m pytest tests/test_dataset.py
+```
+
+You can also validate specific dataset files directly:
+
+```bash
+uv run python -m a2ui_eval.dataset --validate datasets/customer_a_data.yaml
+```
+
+If your YAML file violates the schema (e.g. missing required fields like `catalog` or unknown properties), the validator will output the exact line and error.
+
+### Step 4: Run a local evaluation test
+
+Test how your new data points perform against Gemini models:
+
+```bash
+# Set your API key
+export GEMINI_API_KEY="your_api_key"
+
+# Run a quick sanity check on your new dataset (2 samples, flash-lite)
+uv run main.py --dataset customer_a_data --sanity
+
+# Run the full evaluation on your dataset
+uv run main.py --dataset customer_a_data
+```
+
+### Step 5: Inspect results in the log viewer
+
+Start Inspect AI's interactive web viewer to examine model outputs, merged system prompts, and validator rationales:
+
+```bash
+uv run inspect view start
+```
+
+Open `http://localhost:7575` in your browser to view the trace of each sample.
+
+### Step 6: Commit and push changes
+
+Because `.gitattributes` configures Transcrypt filters for `*.yaml` files in the `eval/datasets/` directory, Git will automatically encrypt the files when staging:
+
+```bash
+git add eval/datasets/customer_a_data.yaml
+git commit -m "feat(eval): add customer_a_data evaluation dataset"
+git push origin your-branch
+```
+
+To verify that files are encrypted before pushing, run:
+
+```bash
+git diff --cached
+```
+
+If Transcrypt is active, the staged diff will show encrypted cipher text.
+
+---
+
+## 5. Migration path for existing data points
+
+To guarantee a smooth transition from legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) to modular dataset files without breaking ongoing developer workflows or CI runs, the framework provides an automated migration script and an in-memory runtime fallback.
+
+### Automated migration script (`eval/bin/migrate_datasets.py`)
+
+A dedicated migration script automates the transformation of existing datasets into the modern `messages` format with the required `catalog` field:
+
+```python
+#!/usr/bin/env python3
+"""Migrates legacy A2UI dataset YAML files to the modern messages format."""
+
+import os
+import yaml
+import jsonschema
+import json
+
+EVAL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATASETS_DIR = os.path.join(EVAL_DIR, "datasets")
+SCHEMA_PATH = os.path.join(DATASETS_DIR, "dataset_schema.json")
+
+MIGRATIONS = {
+    "prompts_v0_9_1.yaml": "core_v0_9_1.yaml",
+    "prompts_v1_0.yaml": "core_v1_0.yaml",
+}
+
+def migrate_sample(item: dict, dataset_name: str) -> dict:
+    """Transforms a single legacy sample dictionary into the clean messages schema."""
+    new_item = {
+        "name": item["name"],
+        "dataset": dataset_name,
+        "description": item["description"],
+        "catalog": item.get("catalog") or "specification/{version}/catalogs/basic/catalog.json",
+    }
+
+    # Transform promptText to messages array
+    if "messages" in item:
+        new_item["messages"] = item["messages"]
+    elif "promptText" in item:
+        new_item["messages"] = [{"role": "user", "content": item["promptText"]}]
+    else:
+        raise ValueError(f"Sample {item.get('name')} has neither messages nor promptText.")
+
+    # Copy standard fields
+    if "target" in item:
+        new_item["target"] = item["target"]
+    if "system_prompt" in item:
+        new_item["system_prompt"] = item["system_prompt"]
+    if "protocol_role" in item or "role_description" in item:
+        new_item["protocol_role"] = item.get("protocol_role") or item.get("role_description")
+    if "generation_rules" in item or "workflow_description" in item:
+        new_item["generation_rules"] = item.get("generation_rules") or item.get("workflow_description")
+    if "allowed_surface_ids" in item:
+        new_item["allowed_surface_ids"] = item["allowed_surface_ids"]
+
+    return new_item
+
+def main():
+    with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
+        schema = json.load(f)
+
+    for old_file, new_file in MIGRATIONS.items():
+        old_path = os.path.join(DATASETS_DIR, old_file)
+        new_path = os.path.join(DATASETS_DIR, new_file)
+
+        if not os.path.exists(old_path):
+            print(f"Skipping {old_file} (not found on disk).")
+            continue
+
+        with open(old_path, "r", encoding="utf-8") as f:
+            raw_data = yaml.safe_load(f) or []
+
+        dataset_name = os.path.splitext(new_file)[0]
+        migrated_data = [migrate_sample(sample, dataset_name) for sample in raw_data]
+
+        # Validate migrated data against updated schema
+        jsonschema.validate(instance=migrated_data, schema=schema)
+
+        # Write new modular dataset file
+        with open(new_path, "w", encoding="utf-8") as f:
+            yaml.dump(migrated_data, f, sort_keys=False, allow_unicode=True)
+        print(f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)} samples).")
+
+        # Delete legacy file
+        os.remove(old_path)
+        print(f"Removed legacy file: {old_file}")
+
+if __name__ == "__main__":
+    main()
+```
+
+### In-memory runtime fallback (loader safety)
+
+To prevent failures if a developer branch or scratch file still contains legacy fields during the transition period, the dataset loader in [eval/a2ui_eval/dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/dataset.py) includes a runtime normalizer:
+
+1. If a sample contains `promptText` instead of `messages`, the loader automatically constructs `messages = [ChatMessageUser(content=item["promptText"])]` in memory.
+2. If `catalog` is missing from a legacy sample during loading, the loader defaults to `specification/{version}/catalogs/basic/catalog.json`.
+3. If a sample contains `role_description` or `workflow_description`, the loader automatically maps them to `protocol_role` and `generation_rules`.
+
+### Step-by-step migration workflow for maintainers
+
+1. **Decrypt datasets**: Initialize Transcrypt locally if not already unlocked:
+   ```bash
+   cd eval
+   bin/transcrypt -p <PASSWORD>
+   ```
+2. **Run migration script**: Execute the automated migration script:
+   ```bash
+   uv run python bin/migrate_datasets.py
+   ```
+3. **Verify dataset schema compliance**: Run the dataset test suite:
+   ```bash
+   uv run python -m pytest tests/test_dataset.py
+   ```
+4. **Run sanity check evaluation**: Execute a quick eval run to confirm model inference and scoring work as expected:
+   ```bash
+   uv run main.py --sanity
+   ```
+5. **Stage and commit changes**: Git will automatically encrypt the new modular files via `.gitattributes`:
+   ```bash
+   git add eval/datasets/core_v0_9_1.yaml eval/datasets/core_v1_0.yaml
+   git rm eval/datasets/prompts_v0_9_1.yaml eval/datasets/prompts_v1_0.yaml
+   git commit -m "feat(eval): migrate dataset files to messages format"
+   ```
+
+---
+
+## Code changes roadmap
+
+| File Path | Nature of Change | Purpose |
+| :--- | :--- | :--- |
+| [eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json) | Update | Require `catalog`, use `messages` for chat turns, and include `dataset`, `system_prompt`, `protocol_role`, and `generation_rules` fields. |
+| [eval/datasets/core_v0_9_1.yaml](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) | Create | Migrated v0.9.1 data points using the `messages` format and explicit `catalog`. |
+| [eval/datasets/core_v1_0.yaml](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) | Create | Migrated v1.0 data points using the `messages` format and explicit `catalog`. |
+| [eval/a2ui_eval/dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/dataset.py) | Update | Add dataset discovery, `messages` array conversion to Inspect AI `ChatMessage` objects, required `catalog` enforcement, and validation CLI. |
+| [eval/a2ui_eval/strategies/format.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/strategies/format.py) | Update | Implement domain system prompt merging and `messages` chat history prepending. |
+| [eval/a2ui_eval/scorers.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py) | Update | Use explicit `catalog` path in `a2ui_scorer`. |
+| [eval/tasks.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tasks.py) | Update | Point to migrated datasets and accept `dataset` and `datasets` filtering parameters. |
+| [eval/main.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/main.py) | Update | Add `--dataset` and `--datasets` CLI flags. |
+| [eval/bin/report_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/report_evals.py) | Update | Add per-dataset grouping in console summaries and markdown reports. |
+| [eval/bin/run_ci_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/run_ci_evals.py) | Update | Pass dataset filtering arguments to `main.py`. |
+| [eval/tests/test_dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tests/test_dataset.py) | Update | Add unit tests for dataset loading, `messages` chat turn parsing, required `catalog` check, and schema validation. |

From 48cfd060e1487bdd96221d03aa5e1687760ecf4b Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 11:42:06 +0930
Subject: [PATCH 02/14] feat(eval): add customer_a_data multi-turn dataset and
 tool response function mapping

---
 eval/a2ui_eval/dataset.py          |  28 +++----
 eval/datasets/customer_a_data.yaml | 129 +++++++++++++++++++++++++++++
 2 files changed, 141 insertions(+), 16 deletions(-)
 create mode 100644 eval/datasets/customer_a_data.yaml

diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index 3576bb9ab0..5651406f4e 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -76,23 +76,32 @@ def _parse_messages(item: dict[str, Any]) -> list[Any]:
     messages_raw = item.get("messages")
     if messages_raw:
         parsed = []
+        call_id_to_func: dict[str, str] = {}
         for m in messages_raw:
             role = m.get("role", "user")
             content = m.get("content", "")
             if role == "user":
                 parsed.append(ChatMessageUser(content=content))
             elif role == "assistant":
+                tool_calls = _parse_tool_calls(m.get("tool_calls"))
+                if tool_calls:
+                    for tc in tool_calls:
+                        if tc.id and tc.function:
+                            call_id_to_func[tc.id] = tc.function
                 parsed.append(
                     ChatMessageAssistant(
                         content=content,
-                        tool_calls=_parse_tool_calls(m.get("tool_calls")),
+                        tool_calls=tool_calls,
                     )
                 )
             elif role == "tool":
+                call_id = m.get("tool_call_id", "")
+                func_name = m.get("function") or m.get("name") or call_id_to_func.get(call_id, "tool_response")
                 parsed.append(
                     ChatMessageTool(
                         content=content,
-                        tool_call_id=m.get("tool_call_id", ""),
+                        tool_call_id=call_id,
+                        function=func_name,
                     )
                 )
             elif role == "system":
@@ -113,22 +122,10 @@ def load_a2ui_dataset(
     version: str | None = None,
     format_name: str | None = None,
 ) -> MemoryDataset:
-    """Loads A2UI evaluation samples from YAML dataset files.
-
-    Args:
-        file_path: The path to a specific YAML dataset file, or None to load datasets by name.
-        dataset: Specific dataset name or list of dataset names to filter by.
-        default_catalog_path: Fallback catalog path if not specified in the sample.
-        version: Optional target version string to substitute into catalog paths.
-        format_name: The output format name (e.g., 'json', 'express', 'elemental').
-
-    Returns:
-        A MemoryDataset containing the resolved samples.
-    """
+    """Loads A2UI evaluation samples from YAML dataset files."""
     with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
         schema = json.load(f)
 
-    # Normalize dataset filter
     filter_names = None
     if dataset:
         if isinstance(dataset, str):
@@ -136,7 +133,6 @@ def load_a2ui_dataset(
         else:
             filter_names = list(dataset)
 
-    # Discover files to load
     target_files: list[Path] = []
     if file_path:
         p = Path(file_path)
diff --git a/eval/datasets/customer_a_data.yaml b/eval/datasets/customer_a_data.yaml
new file mode 100644
index 0000000000..854c09f56c
--- /dev/null
+++ b/eval/datasets/customer_a_data.yaml
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From f7904e294609939e916b78a88aaa710600d7ff53 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 11:49:44 +0930
Subject: [PATCH 03/14] feat(eval): rename dataset to
 multi_turn_conversation_dataset and add 24-turn triage and 32-turn rebooking
 scenarios

---
 eval/datasets/customer_a_data.yaml            | 129 ------
 .../multi_turn_conversation_dataset.yaml      | 407 ++++++++++++++++++
 2 files changed, 407 insertions(+), 129 deletions(-)
 delete mode 100644 eval/datasets/customer_a_data.yaml
 create mode 100644 eval/datasets/multi_turn_conversation_dataset.yaml

diff --git a/eval/datasets/customer_a_data.yaml b/eval/datasets/customer_a_data.yaml
deleted file mode 100644
index 854c09f56c..0000000000
--- a/eval/datasets/customer_a_data.yaml
+++ /dev/null
@@ -1,129 +0,0 @@
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diff --git a/eval/datasets/multi_turn_conversation_dataset.yaml b/eval/datasets/multi_turn_conversation_dataset.yaml
new file mode 100644
index 0000000000..7be74b7e2b
--- /dev/null
+++ b/eval/datasets/multi_turn_conversation_dataset.yaml
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From 97a68ee68b761f4a68cb711fbddb044f73ac87ea Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 11:50:46 +0930
Subject: [PATCH 04/14] feat(skills): add a2ui-add-eval-datapoint skill

---
 .../skills/a2ui-add-eval-datapoint/SKILL.md   | 70 +++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 .agents/skills/a2ui-add-eval-datapoint/SKILL.md

diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
new file mode 100644
index 0000000000..d6ed994540
--- /dev/null
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -0,0 +1,70 @@
+---
+name: a2ui-add-eval-datapoint
+description: Step-by-step workflow for adding and verifying new evaluation data points in the A2UI evaluation suite.
+---
+
+# Adding and verifying A2UI evaluation data points
+
+Use this skill when adding new data points or datasets to the A2UI evaluation framework in [eval/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval).
+
+## References
+
+For full technical specifications, consult:
+- [eval/README.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/README.md): Evaluation quickstart and Transcrypt setup.
+- [eval/DESIGN.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/DESIGN.md): Evaluation architecture and grading details.
+- [specification/proposals/eval_suite_expansion.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/specification/proposals/eval_suite_expansion.md): Full dataset schema and field reference guide.
+
+---
+
+## Step-by-step workflow
+
+### 1. Unlock Transcrypt
+Datasets are encrypted at rest in Git. Unlock Transcrypt locally before editing:
+```bash
+cd eval
+bin/transcrypt -p <PASSWORD>
+```
+
+### 2. Add or update dataset YAML
+Create or edit a YAML file in [eval/datasets/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) (e.g. `eval/datasets/my_dataset.yaml`):
+
+```yaml
+- name: sample_name
+  dataset: my_dataset
+  description: Brief summary of what this sample tests.
+  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  system_prompt: "Optional domain-specific system prompt."
+  messages:
+    - role: user
+      content: "Create a contact form..."
+  target: "Expected UI outcome and rubric criteria for LLM judge."
+```
+
+### 3. Validate schema compliance
+Run the automated schema test:
+```bash
+cd eval
+uv run python -m pytest tests/test_dataset.py
+```
+
+### 4. Run an evaluation test on the new data point
+Always run a test on the newly added dataset to verify model inference and scoring:
+```bash
+cd eval
+# Quick check (flash-lite model)
+uv run main.py --dataset my_dataset --sanity
+
+# Full evaluation check
+uv run main.py --dataset my_dataset
+```
+
+### 5. Inspect results and commit
+Explore model outputs and validator rationales in Inspect AI's web viewer:
+```bash
+uv run inspect view start
+```
+Stage and commit changes (Git transparently encrypts YAML files via Transcrypt filters):
+```bash
+git add eval/datasets/my_dataset.yaml
+git commit -m "feat(eval): add my_dataset evaluation data points"
+```

From c78da0eef5d84a125eb597aad1af180ba0d35332 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 11:55:24 +0930
Subject: [PATCH 05/14] docs(eval): update README, DESIGN, and
 add-eval-datapoint skill with schema rules and encrypted commit workflow

---
 .../skills/a2ui-add-eval-datapoint/SKILL.md   | 65 +++++++++----
 eval/DESIGN.md                                |  8 +-
 eval/README.md                                | 96 +++++++++----------
 3 files changed, 92 insertions(+), 77 deletions(-)

diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
index d6ed994540..6c45bc07ef 100644
--- a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -5,66 +5,91 @@ description: Step-by-step workflow for adding and verifying new evaluation data
 
 # Adding and verifying A2UI evaluation data points
 
-Use this skill when adding new data points or datasets to the A2UI evaluation framework in [eval/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval).
+Use this skill when adding new data points or datasets to the A2UI evaluation framework in `eval/`.
 
 ## References
 
-For full technical specifications, consult:
-- [eval/README.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/README.md): Evaluation quickstart and Transcrypt setup.
-- [eval/DESIGN.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/DESIGN.md): Evaluation architecture and grading details.
-- [specification/proposals/eval_suite_expansion.md](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/specification/proposals/eval_suite_expansion.md): Full dataset schema and field reference guide.
+For full architecture and setup details, consult:
+- `eval/README.md`: Evaluation quickstart, Transcrypt setup, and CLI flags.
+- `eval/DESIGN.md`: Architecture, multi-stage scorers, and encryption-at-rest design.
+- `eval/datasets/dataset_schema.json`: Formal JSON Schema defining required fields and structural rules for evaluation data points.
 
 ---
 
 ## Step-by-step workflow
 
 ### 1. Unlock Transcrypt
-Datasets are encrypted at rest in Git. Unlock Transcrypt locally before editing:
+Datasets in `eval/datasets/` are encrypted at rest. Unlock Transcrypt locally before authoring:
 ```bash
 cd eval
 bin/transcrypt -p <PASSWORD>
 ```
 
-### 2. Add or update dataset YAML
-Create or edit a YAML file in [eval/datasets/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) (e.g. `eval/datasets/my_dataset.yaml`):
+### 2. Author the data point
+Create or update a dataset file in `eval/datasets/<dataset_name>.yaml` (e.g. `eval/datasets/my_dataset.yaml`).
+
+Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_schema.json`.
 
 ```yaml
-- name: sample_name
+- name: sample_unique_identifier
   dataset: my_dataset
-  description: Brief summary of what this sample tests.
+  description: Clear summary of what this scenario tests.
   catalog: "specification/{version}/catalogs/basic/catalog.json"
-  system_prompt: "Optional domain-specific system prompt."
+  system_prompt: |
+    Optional domain-specific system instructions (e.g. company policies, triage rules).
   messages:
     - role: user
-      content: "Create a contact form..."
-  target: "Expected UI outcome and rubric criteria for LLM judge."
+      content: "Initial user request..."
+    - role: assistant
+      content: "Assistant response..."
+      tool_calls:
+        - id: call_001
+          type: function
+          function:
+            name: query_database
+            arguments: '{"key": "value"}'
+    - role: tool
+      tool_call_id: call_001
+      function: query_database
+      content: '{"result": "data"}'
+    - role: user
+      content: "Render the UI card with the retrieved data..."
+  target: |
+    Expected UI outcome and scoring criteria for the LLM judge.
 ```
 
 ### 3. Validate schema compliance
-Run the automated schema test:
+Verify that all data points satisfy `eval/datasets/dataset_schema.json`:
 ```bash
 cd eval
 uv run python -m pytest tests/test_dataset.py
 ```
 
-### 4. Run an evaluation test on the new data point
-Always run a test on the newly added dataset to verify model inference and scoring:
+### 4. Run an evaluation on the new data point
+Always execute an evaluation run on the newly added dataset to verify model inference and scoring:
 ```bash
 cd eval
-# Quick check (flash-lite model)
+# Quick validation on gemini-3.1-flash-lite
 uv run main.py --dataset my_dataset --sanity
 
 # Full evaluation check
 uv run main.py --dataset my_dataset
 ```
 
-### 5. Inspect results and commit
-Explore model outputs and validator rationales in Inspect AI's web viewer:
+View the interactive traces and judging rationales:
 ```bash
 uv run inspect view start
 ```
-Stage and commit changes (Git transparently encrypts YAML files via Transcrypt filters):
+
+### 5. Verify encryption and commit
+When staging changes, Git applies the Transcrypt clean filter. Confirm that the staged file is encrypted ciphertext before committing:
 ```bash
+# Stage the new dataset file
 git add eval/datasets/my_dataset.yaml
+
+# Verify staged content is encrypted ciphertext (not plaintext)
+git diff --cached eval/datasets/my_dataset.yaml
+
+# Commit the encrypted data point
 git commit -m "feat(eval): add my_dataset evaluation data points"
 ```
diff --git a/eval/DESIGN.md b/eval/DESIGN.md
index c5375599ba..a63e9bb09c 100644
--- a/eval/DESIGN.md
+++ b/eval/DESIGN.md
@@ -14,12 +14,12 @@ The framework adopts the three-pillar architecture of Inspect AI—Tasks, Solver
 
 | Inspect Component | A2UI Implementation Detail                                                 | Primary Responsibility                                            |
 | :---------------- | :------------------------------------------------------------------------- | :---------------------------------------------------------------- |
-| Dataset           | Encrypted archives resolved into EvalSet JSON.                             | Providing structured prompts and ground-truth UI targets.         |
-| Solver            | Uses `A2uiSchemaManager` to inject system prompt, then generates response. | Orchestrating the model's attempt to generate a valid UI payload. |
-| Scorer            | A hybrid of SDK-based validation and LLM-as-a-judge.                       | Determining the accuracy and usability of the generated JSON.     |
+| Dataset           | Encrypted YAML files conforming to `datasets/dataset_schema.json`.         | Providing structured `messages` turns, catalogs, and UI targets.  |
+| Solver            | Uses `A2uiSchemaManager` to inject system prompt and protocol rules.       | Orchestrating the model's attempt to generate a valid UI payload. |
+| Scorer            | Algorithmic `a2ui_scorer` validation and `measured_model_graded_qa` judge. | Determining technical validity and semantic intent quality.       |
 | Model Provider    | Support for Gemini, OpenAI, Anthropic, and local vLLM.                     | Standardizing API interactions across diverse model families.     |
 
-The interaction flow begins with the resolution of a dataset sample. A solver then prepares the environment, using `A2uiSchemaManager` to provide the model with the correct protocol schemas and a "catalog" of available A2UI components—a critical constraint of the A2UI security model. The model generates a response, which is then parsed and corrected using the SDK's parser tools, and finally passed through a series of Scorers to assess its technical validity and semantic alignment with the user's intent.
+The interaction flow begins with loading a dataset sample. Each sample specifies a `catalog` path, optional domain `system_prompt`, and a list of `messages` representing the conversation history (including user requests, assistant responses, and tool calls). The solver merges the domain system prompt with A2UI protocol instructions and passes the conversational context to the model. The generated UI payload is parsed and evaluated by `a2ui_scorer` for schema validity and `measured_model_graded_qa` for semantic quality.
 
 ### **Integration with A2UI Python SDK**
 
diff --git a/eval/README.md b/eval/README.md
index 93a3ac7812..41de17665c 100644
--- a/eval/README.md
+++ b/eval/README.md
@@ -1,101 +1,91 @@
 # A2UI Evaluation Framework
 
-This folder contains evaluation tests (aka evals) for the A2UI project.
-An evaluation test verifies that a prompt produces expected results conforming to the A2UI schema and semantic rules.
+This folder contains evaluation tests (aka evals) for the A2UI project using the [Inspect AI](https://inspect.aisi.org.uk/) framework.
+An evaluation test verifies that a prompt or conversational history produces expected UI results conforming to the A2UI schema and semantic rules.
 
 ## Design
 
-For a detailed overview of the design, secrets management, and contamination prevention, see the [DESIGN.md](DESIGN.md) file.
+For a detailed overview of the evaluation architecture, multi-stage scoring, and secret management, see [DESIGN.md](DESIGN.md).
+
+## Datasets and Schema
+
+Evaluation data points live in `datasets/*.yaml` files (e.g., `datasets/core_v0_9_1.yaml`, `datasets/core_v1_0.yaml`, `datasets/multi_turn_conversation_dataset.yaml`).
+
+Every dataset file must conform to the JSON schema defined in `datasets/dataset_schema.json`.
+
+### Dataset Structure
+Each sample in a dataset YAML file defines:
+- **`name`** (required): Unique identifier for the sample.
+- **`description`** (required): Human-readable summary of the test scenario.
+- **`catalog`** (required): Relative path to the component catalog (e.g., `"specification/{version}/catalogs/basic/catalog.json"`).
+- **`dataset`** (optional): Logical dataset grouping name (defaults to file basename).
+- **`system_prompt`** (optional): Domain-specific system instructions (e.g. clinical triage rules or travel policies).
+- **`messages`** (required): Chat conversation turns (`user`, `assistant` with optional `tool_calls`, `tool` returns, and `system`).
+- **`target`** (optional): Expected UI outcome description and grading criteria for the LLM judge.
 
 ## Running Evaluations
 
-To run the evaluations, you need to use the Inspect AI CLI via `uv`. Make sure you are in this directory (`evals/eval`).
+Make sure you are in the `eval/` directory.
 
 ### Prerequisites
 
 1. **Set your Gemini API key**:
-
    ```bash
    export GEMINI_API_KEY="your_api_key"
    ```
 
 2. **Decrypt Datasets (First Time Setup)**:
-   The evaluation datasets are encrypted at rest in the repository to prevent base model contamination. To decrypt them in your repo for evaluation, you need to initialize Transcrypt with the shared password. From the `evals/eval` directory, run:
-
+   The evaluation datasets are encrypted at rest in the repository to prevent base model contamination. To decrypt them locally, initialize Transcrypt with the shared password:
    ```bash
    bin/transcrypt -p <PASSWORD>
    ```
 
-You can request the password from any member of the A2UI team (it's not really a secret, but it's also not going on Github in plaintext).
-
-After this one time setup, you will have local plaintext access to the decrypted datasets in the `datasets/` directory, and they will be encrypted and decrypted transparently by git.
-
-### Upgrading transcrypt
-
-If you pull updates that change the encryption settings (such as transitioning from MD5 to PBKDF2), you may encounter decryption errors during `git pull` or see OpenSSL deprecation warnings.
-
-To upgrade your local transcrypt configuration to the latest settings:
-
-1. Run the upgrade command:
-
-   ```bash
-   bin/transcrypt --upgrade
-   ```
-
-   This updates the local filter scripts in your `.git` directory while preserving your saved password.
+   After this setup, git transparently encrypts files on `git add` and decrypts them on checkout.
 
-2. Force Git to re-decrypt the files:
-   ```bash
-   git checkout HEAD -- $(git ls-crypt)
-   ```
-   This runs the files through the newly upgraded smudge filter, decrypting them.
-
-## Dimensions
-
-Evaluations run across the following dimensions:
+### Executing Evals
 
-- Strategies: Different ways of orchestrating A2UI generation
-- Samples: Data in `dataset/`
+To run all datasets:
+```bash
+uv run main.py
+```
 
-### Run Evals
+To run a specific dataset or multiple datasets:
+```bash
+# Run a single dataset
+uv run main.py --dataset multi_turn_conversation_dataset
 
-To run the evaluations:
+# Run multiple datasets
+uv run main.py --datasets core_v0_9_1,multi_turn_conversation_dataset
+```
 
+To test across different inference formats (`direct` JSON, `express` XML tags, `elemental` DSL):
 ```bash
-uv run main.py
+uv run main.py --dataset multi_turn_conversation_dataset --strategies direct,express,elemental
 ```
 
-For a quick 2-sample validation using `gemini-3.1-flash-lite`, use the sanity flag:
-
+For a quick 2-sample validation using `gemini-3.1-flash-lite`:
 ```bash
 uv run main.py --sanity
 ```
 
 ## Viewing Evaluation Results
 
-Inspect AI provides a web-based log viewer to explore the results of your evaluations.
-
-To start the log viewer:
+Inspect AI provides a web-based log viewer to explore interactive traces and judge rationales:
 
 ```bash
 uv run inspect view start
 ```
 
-This will start a local web server (usually at `http://localhost:7575`) and open the viewer in your browser. It will automatically load logs from the `logs/` directory.
-
-## Listing Available Models
-
-To list the available Gemini models supported by your API key:
+This starts a local web server (usually at `http://localhost:7575`).
 
+To print a console summary or markdown table from an eval log file:
 ```bash
-uv run inspect eval tasks.py -T list_models=True --model google/gemini-3-flash-preview
+uv run python bin/report_evals.py logs/<log_filename>.eval
 ```
 
-(the `--model` flag is required even though it is ignored)
-
-## Running Unit Tests
+## Running Unit Tests & Schema Validation
 
-To run the unit tests for the evaluation framework (dataset loader, solvers, scorers):
+To run the unit tests and validate all dataset files against `datasets/dataset_schema.json`:
 
 ```bash
 uv run python -m pytest

From 0d31b6610c8936b58562eee97a76d7220b9e9c3c Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:18:20 +0930
Subject: [PATCH 06/14] fix(eval): apply pyink formatting and clean up relative
 markdown links

---
 .../skills/a2ui-add-eval-datapoint/SKILL.md   |   19 +-
 eval/README.md                                |    9 +
 eval/a2ui_eval/dataset.py                     |   28 +-
 eval/a2ui_eval/strategies/format.py           |   14 +-
 eval/bin/migrate_datasets.py                  |    8 +-
 eval/bin/report_evals.py                      |   33 +-
 eval/datasets/core_v0_9_1.yaml                | 2355 +++++++++--------
 eval/datasets/core_v1_0.yaml                  | 2335 ++++++++--------
 eval/datasets/dataset_schema.json             |   15 +-
 eval/main.py                                  |    4 +-
 .../proposals/eval_suite_expansion.md         |  175 +-
 11 files changed, 2547 insertions(+), 2448 deletions(-)

diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
index 6c45bc07ef..2b78d11e4c 100644
--- a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -10,6 +10,7 @@ Use this skill when adding new data points or datasets to the A2UI evaluation fr
 ## References
 
 For full architecture and setup details, consult:
+
 - `eval/README.md`: Evaluation quickstart, Transcrypt setup, and CLI flags.
 - `eval/DESIGN.md`: Architecture, multi-stage scorers, and encryption-at-rest design.
 - `eval/datasets/dataset_schema.json`: Formal JSON Schema defining required fields and structural rules for evaluation data points.
@@ -19,13 +20,16 @@ For full architecture and setup details, consult:
 ## Step-by-step workflow
 
 ### 1. Unlock Transcrypt
+
 Datasets in `eval/datasets/` are encrypted at rest. Unlock Transcrypt locally before authoring:
+
 ```bash
 cd eval
 bin/transcrypt -p <PASSWORD>
 ```
 
 ### 2. Author the data point
+
 Create or update a dataset file in `eval/datasets/<dataset_name>.yaml` (e.g. `eval/datasets/my_dataset.yaml`).
 
 Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_schema.json`.
@@ -34,14 +38,14 @@ Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_s
 - name: sample_unique_identifier
   dataset: my_dataset
   description: Clear summary of what this scenario tests.
-  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
   system_prompt: |
     Optional domain-specific system instructions (e.g. company policies, triage rules).
   messages:
     - role: user
-      content: "Initial user request..."
+      content: 'Initial user request...'
     - role: assistant
-      content: "Assistant response..."
+      content: 'Assistant response...'
       tool_calls:
         - id: call_001
           type: function
@@ -53,20 +57,24 @@ Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_s
       function: query_database
       content: '{"result": "data"}'
     - role: user
-      content: "Render the UI card with the retrieved data..."
+      content: 'Render the UI card with the retrieved data...'
   target: |
     Expected UI outcome and scoring criteria for the LLM judge.
 ```
 
 ### 3. Validate schema compliance
+
 Verify that all data points satisfy `eval/datasets/dataset_schema.json`:
+
 ```bash
 cd eval
 uv run python -m pytest tests/test_dataset.py
 ```
 
 ### 4. Run an evaluation on the new data point
+
 Always execute an evaluation run on the newly added dataset to verify model inference and scoring:
+
 ```bash
 cd eval
 # Quick validation on gemini-3.1-flash-lite
@@ -77,12 +85,15 @@ uv run main.py --dataset my_dataset
 ```
 
 View the interactive traces and judging rationales:
+
 ```bash
 uv run inspect view start
 ```
 
 ### 5. Verify encryption and commit
+
 When staging changes, Git applies the Transcrypt clean filter. Confirm that the staged file is encrypted ciphertext before committing:
+
 ```bash
 # Stage the new dataset file
 git add eval/datasets/my_dataset.yaml
diff --git a/eval/README.md b/eval/README.md
index 41de17665c..9fedcd5e7d 100644
--- a/eval/README.md
+++ b/eval/README.md
@@ -14,7 +14,9 @@ Evaluation data points live in `datasets/*.yaml` files (e.g., `datasets/core_v0_
 Every dataset file must conform to the JSON schema defined in `datasets/dataset_schema.json`.
 
 ### Dataset Structure
+
 Each sample in a dataset YAML file defines:
+
 - **`name`** (required): Unique identifier for the sample.
 - **`description`** (required): Human-readable summary of the test scenario.
 - **`catalog`** (required): Relative path to the component catalog (e.g., `"specification/{version}/catalogs/basic/catalog.json"`).
@@ -30,12 +32,14 @@ Make sure you are in the `eval/` directory.
 ### Prerequisites
 
 1. **Set your Gemini API key**:
+
    ```bash
    export GEMINI_API_KEY="your_api_key"
    ```
 
 2. **Decrypt Datasets (First Time Setup)**:
    The evaluation datasets are encrypted at rest in the repository to prevent base model contamination. To decrypt them locally, initialize Transcrypt with the shared password:
+
    ```bash
    bin/transcrypt -p <PASSWORD>
    ```
@@ -45,11 +49,13 @@ Make sure you are in the `eval/` directory.
 ### Executing Evals
 
 To run all datasets:
+
 ```bash
 uv run main.py
 ```
 
 To run a specific dataset or multiple datasets:
+
 ```bash
 # Run a single dataset
 uv run main.py --dataset multi_turn_conversation_dataset
@@ -59,11 +65,13 @@ uv run main.py --datasets core_v0_9_1,multi_turn_conversation_dataset
 ```
 
 To test across different inference formats (`direct` JSON, `express` XML tags, `elemental` DSL):
+
 ```bash
 uv run main.py --dataset multi_turn_conversation_dataset --strategies direct,express,elemental
 ```
 
 For a quick 2-sample validation using `gemini-3.1-flash-lite`:
+
 ```bash
 uv run main.py --sanity
 ```
@@ -79,6 +87,7 @@ uv run inspect view start
 This starts a local web server (usually at `http://localhost:7575`).
 
 To print a console summary or markdown table from an eval log file:
+
 ```bash
 uv run python bin/report_evals.py logs/<log_filename>.eval
 ```
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index 5651406f4e..63748c07e4 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -47,7 +47,9 @@ def _version_to_dir_name(version: str) -> str:
     return "v" + version.replace(".", "_")
 
 
-def _parse_tool_calls(raw_tool_calls: list[dict[str, Any]] | None) -> list[ToolCall] | None:
+def _parse_tool_calls(
+    raw_tool_calls: list[dict[str, Any]] | None,
+) -> list[ToolCall] | None:
     """Converts raw tool calls into Inspect AI ToolCall objects."""
     if not raw_tool_calls:
         return None
@@ -67,7 +69,11 @@ def _parse_tool_calls(raw_tool_calls: list[dict[str, Any]] | None) -> list[ToolC
                 func_args = json.loads(func_args)
             except Exception:
                 pass
-        parsed.append(ToolCall(id=call_id, function=func_name, arguments=func_args, type=call_type))
+        parsed.append(
+            ToolCall(
+                id=call_id, function=func_name, arguments=func_args, type=call_type
+            )
+        )
     return parsed
 
 
@@ -96,7 +102,11 @@ def _parse_messages(item: dict[str, Any]) -> list[Any]:
                 )
             elif role == "tool":
                 call_id = m.get("tool_call_id", "")
-                func_name = m.get("function") or m.get("name") or call_id_to_func.get(call_id, "tool_response")
+                func_name = (
+                    m.get("function")
+                    or m.get("name")
+                    or call_id_to_func.get(call_id, "tool_response")
+                )
                 parsed.append(
                     ChatMessageTool(
                         content=content,
@@ -112,7 +122,9 @@ def _parse_messages(item: dict[str, Any]) -> list[Any]:
     elif "promptText" in item:
         return [ChatMessageUser(content=item["promptText"])]
     else:
-        raise ValueError(f"Sample {item.get('name')} must specify 'messages' or 'promptText'.")
+        raise ValueError(
+            f"Sample {item.get('name')} must specify 'messages' or 'promptText'."
+        )
 
 
 def load_a2ui_dataset(
@@ -169,7 +181,9 @@ def load_a2ui_dataset(
                 )
 
             default_role = (
-                DEFAULT_ROLE_DESCRIPTION if is_json else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
+                DEFAULT_ROLE_DESCRIPTION
+                if is_json
+                else FORMAT_AGNOSTIC_ROLE_DESCRIPTION
             )
             default_workflow = (
                 DEFAULT_WORKFLOW_DESCRIPTION
@@ -205,7 +219,9 @@ def load_a2ui_dataset(
                         "generation_rules": generation_rules,
                         "role_description": protocol_role,
                         "workflow_description": generation_rules,
-                        "allowed_surface_ids": item.get("allowed_surface_ids") or ["main"],
+                        "allowed_surface_ids": (
+                            item.get("allowed_surface_ids") or ["main"]
+                        ),
                     },
                 )
             )
diff --git a/eval/a2ui_eval/strategies/format.py b/eval/a2ui_eval/strategies/format.py
index 89f43c7953..01ff65e503 100644
--- a/eval/a2ui_eval/strategies/format.py
+++ b/eval/a2ui_eval/strategies/format.py
@@ -76,8 +76,12 @@ async def solve(state: TaskState, generate: Generate) -> TaskState:
         catalog_config = CatalogConfig.from_path("basic_catalog", resolved_catalog_path)
         strategy = _get_strategy(format_name, version, catalog_config)
 
-        role_description = state.metadata.get("protocol_role") or state.metadata.get("role_description", "")
-        workflow_description = state.metadata.get("generation_rules") or state.metadata.get("workflow_description", "")
+        role_description = state.metadata.get("protocol_role") or state.metadata.get(
+            "role_description", ""
+        )
+        workflow_description = state.metadata.get(
+            "generation_rules"
+        ) or state.metadata.get("workflow_description", "")
 
         a2ui_prompt = strategy.prompt_generator.generate(
             role_description=role_description,
@@ -87,7 +91,10 @@ async def solve(state: TaskState, generate: Generate) -> TaskState:
 
         domain_prompt = state.metadata.get("system_prompt", "").strip()
         if domain_prompt:
-            full_prompt = f"## Domain Instructions\n{domain_prompt}\n\n## UI Protocol Instructions\n{a2ui_prompt}"
+            full_prompt = (
+                f"## Domain Instructions\n{domain_prompt}\n\n## UI Protocol"
+                f" Instructions\n{a2ui_prompt}"
+            )
         else:
             full_prompt = a2ui_prompt
 
@@ -97,7 +104,6 @@ async def solve(state: TaskState, generate: Generate) -> TaskState:
     return solve
 
 
-
 @solver
 def compile_format_payload(format_name: str, version: str) -> Solver:
     """Solver to compile format-specific output back to standard A2UI JSON."""
diff --git a/eval/bin/migrate_datasets.py b/eval/bin/migrate_datasets.py
index 56da33bcd4..cae9cb7899 100644
--- a/eval/bin/migrate_datasets.py
+++ b/eval/bin/migrate_datasets.py
@@ -36,8 +36,9 @@ def migrate_sample(item: dict, dataset_name: str) -> dict:
         "name": item["name"],
         "dataset": dataset_name,
         "description": item["description"],
-        "catalog": item.get("catalog")
-        or "specification/{version}/catalogs/basic/catalog.json",
+        "catalog": (
+            item.get("catalog") or "specification/{version}/catalogs/basic/catalog.json"
+        ),
     }
 
     if "messages" in item:
@@ -92,7 +93,8 @@ def main():
         with open(new_path, "w", encoding="utf-8") as f:
             yaml.dump(migrated_data, f, sort_keys=False, allow_unicode=True)
         print(
-            f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)} samples)."
+            f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)}"
+            " samples)."
         )
 
         # Delete legacy file
diff --git a/eval/bin/report_evals.py b/eval/bin/report_evals.py
index 6371d06500..362c62b52e 100755
--- a/eval/bin/report_evals.py
+++ b/eval/bin/report_evals.py
@@ -59,7 +59,9 @@ def print_results_summary(log_data: dict[str, Any]) -> None:
         passed = 0
         print(f"\n--- Dataset: {d_name} ---")
         for sample in d_samples:
-            name = sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
+            name = (
+                sample.get("metadata", {}).get("name") or f"Sample {sample.get('id')}"
+            )
             scores = sample.get("scores", {})
 
             # Algorithmic validity (a2ui_scorer)
@@ -77,7 +79,9 @@ def print_results_summary(log_data: dict[str, Any]) -> None:
                     inference_time = event.get("working_time") or event.get("duration")
                     break
             if inference_time is None:
-                inference_time = sample.get("metadata", {}).get("evaluation_duration_seconds")
+                inference_time = sample.get("metadata", {}).get(
+                    "evaluation_duration_seconds"
+                )
             inference_time_str = (
                 f"{float(inference_time):.2f}s" if inference_time is not None else "N/A"
             )
@@ -117,7 +121,10 @@ def print_results_summary(log_data: dict[str, Any]) -> None:
     if all_durations:
         avg_duration = statistics.mean(all_durations)
         med_duration = statistics.median(all_durations)
-        print(f"Inference Time - Average: {avg_duration:.2f}s | Median: {med_duration:.2f}s")
+        print(
+            f"Inference Time - Average: {avg_duration:.2f}s | Median:"
+            f" {med_duration:.2f}s"
+        )
     print("==================================")
 
 
@@ -151,28 +158,38 @@ def generate_markdown_summary(
 
     if any(k != "default" for k in datasets.keys()):
         lines.append("#### Dataset Performance Summary")
-        lines.append("| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |")
+        lines.append(
+            "| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate"
+            " | Dataset Pass Rate |"
+        )
         lines.append("| :--- | :---: | :---: | :---: | :---: |")
 
         for d_name, d_samples in datasets.items():
             total = len(d_samples)
             a2ui_pass_cnt = sum(
-                1 for s in d_samples if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
             )
             qa_pass_cnt = sum(
-                1 for s in d_samples if s.get("scores", {}).get("measured_model_graded_qa", {}).get("value") == "C"
+                1
+                for s in d_samples
+                if s.get("scores", {}).get("measured_model_graded_qa", {}).get("value")
+                == "C"
             )
             both_pass_cnt = sum(
                 1
                 for s in d_samples
                 if s.get("scores", {}).get("a2ui_scorer", {}).get("value") == 1.0
-                and s.get("scores", {}).get("measured_model_graded_qa", {}).get("value") == "C"
+                and s.get("scores", {}).get("measured_model_graded_qa", {}).get("value")
+                == "C"
             )
             a2ui_pct = (a2ui_pass_cnt / total * 100) if total else 0.0
             qa_pct = (qa_pass_cnt / total * 100) if total else 0.0
             both_pct = (both_pass_cnt / total * 100) if total else 0.0
             lines.append(
-                f"| `{d_name}` | {total} | {a2ui_pct:.1f}% | {qa_pct:.1f}% | {both_pct:.1f}% |"
+                f"| `{d_name}` | {total} | {a2ui_pct:.1f}% | {qa_pct:.1f}% |"
+                f" {both_pct:.1f}% |"
             )
         lines.append("")
 
diff --git a/eval/datasets/core_v0_9_1.yaml b/eval/datasets/core_v0_9_1.yaml
index 6c81f65c63..d2a89c97af 100644
--- a/eval/datasets/core_v0_9_1.yaml
+++ b/eval/datasets/core_v0_9_1.yaml
@@ -1,1170 +1,1185 @@
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diff --git a/eval/datasets/core_v1_0.yaml b/eval/datasets/core_v1_0.yaml
index 327b78b3a5..a6d77fecea 100644
--- a/eval/datasets/core_v1_0.yaml
+++ b/eval/datasets/core_v1_0.yaml
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diff --git a/eval/datasets/dataset_schema.json b/eval/datasets/dataset_schema.json
index 95bbcd228f..48492d8a35 100644
--- a/eval/datasets/dataset_schema.json
+++ b/eval/datasets/dataset_schema.json
@@ -43,13 +43,13 @@
               "items": {
                 "type": "object",
                 "properties": {
-                  "id": { "type": "string" },
-                  "type": { "type": "string", "enum": ["function"] },
+                  "id": {"type": "string"},
+                  "type": {"type": "string", "enum": ["function"]},
                   "function": {
                     "type": "object",
                     "properties": {
-                      "name": { "type": "string" },
-                      "arguments": { "type": "string" }
+                      "name": {"type": "string"},
+                      "arguments": {"type": "string"}
                     },
                     "required": ["name", "arguments"]
                   }
@@ -95,15 +95,12 @@
       },
       "allowed_surface_ids": {
         "type": "array",
-        "items": { "type": "string" },
+        "items": {"type": "string"},
         "description": "Allowed surface IDs for the output."
       }
     },
     "required": ["name", "description", "catalog"],
-    "anyOf": [
-      { "required": ["messages"] },
-      { "required": ["promptText"] }
-    ],
+    "anyOf": [{"required": ["messages"]}, {"required": ["promptText"]}],
     "additionalProperties": false
   }
 }
diff --git a/eval/main.py b/eval/main.py
index 66da843564..64d423cdde 100644
--- a/eval/main.py
+++ b/eval/main.py
@@ -34,7 +34,9 @@ def main() -> None:
         "--dataset",
         type=str,
         default=None,
-        help="Evaluate only a specific dataset (e.g. 'customer_a_data' or 'core_v0_9_1')",
+        help=(
+            "Evaluate only a specific dataset (e.g. 'customer_a_data' or 'core_v0_9_1')"
+        ),
     )
     parser.add_argument(
         "--datasets",
diff --git a/specification/proposals/eval_suite_expansion.md b/specification/proposals/eval_suite_expansion.md
index 78b1cb5ff9..fdbef41256 100644
--- a/specification/proposals/eval_suite_expansion.md
+++ b/specification/proposals/eval_suite_expansion.md
@@ -8,12 +8,12 @@ _Created: 2026-07-23_
 
 ## Background
 
-The A2UI evaluation framework located in [eval/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval) tests whether Large Language Models generate valid and contextually appropriate A2UI JSON payloads. The current framework runs Inspect AI tasks ([eval/tasks.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tasks.py)) against prompt datasets stored in YAML format. Datasets are encrypted at rest using Transcrypt to prevent data contamination from web crawlers.
+The A2UI evaluation framework located in [eval/](../../eval) tests whether Large Language Models generate valid and contextually appropriate A2UI JSON payloads. The current framework runs Inspect AI tasks ([eval/tasks.py](../../eval/tasks.py)) against prompt datasets stored in YAML format. Datasets are encrypted at rest using Transcrypt to prevent data contamination from web crawlers.
 
 While the existing system tests basic generation well, real-world agentic applications present scenarios that the current setup cannot evaluate:
 
 1. **Monolithic dataset files**: All evaluation samples reside in monolithic YAML files. There is no mechanism to isolate data points into separate modular datasets (such as `core_v0_9_1`, `customer_a_data`, or `banking_app`), execute only a specific dataset, or view separate reporting tables per dataset.
-2. **Disconnected single-string prompts**: The existing schema ([eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json)) uses a standalone `promptText` string. Real agentic applications pass a structured `messages` list containing prior user queries, assistant responses, and tool calls with execution outputs before the model generates UI.
+2. **Disconnected single-string prompts**: The existing schema ([eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json)) uses a standalone `promptText` string. Real agentic applications pass a structured `messages` list containing prior user queries, assistant responses, and tool calls with execution outputs before the model generates UI.
 3. **Domain system prompts and custom catalogs**: In production applications, models receive domain-specific system prompts (such as customer support rules, flight booking domain logic, or medical guidelines) that are separate from A2UI protocol instructions. Applications also define custom component catalogs with specialized UI elements. The current eval engine only injects A2UI protocol instructions without merging custom domain system prompts or supporting multi-turn chat history.
 4. **Contributor onboarding**: Contributors and external teams creating new data points need a clear, step-by-step workflow covering decryption, schema verification, local evaluation execution, and safe encrypted commits.
 
@@ -75,7 +75,7 @@ flowchart TD
 
 ### Directory organization
 
-Dataset files will be organized under [eval/datasets/](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) as separate modular YAML files. The legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) are migrated directly into named dataset files:
+Dataset files will be organized under [eval/datasets/](../../eval/datasets) as separate modular YAML files. The legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) are migrated directly into named dataset files:
 
 ```
 eval/datasets/
@@ -92,7 +92,7 @@ Each YAML file in `eval/datasets/` represents a distinct evaluation dataset name
 
 ### Dataset resolution and CLI execution
 
-The CLI runner in [eval/main.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/main.py) and the CI runner in [eval/bin/run_ci_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/run_ci_evals.py) will support filtering by dataset:
+The CLI runner in [eval/main.py](../../eval/main.py) and the CI runner in [eval/bin/run_ci_evals.py](../../eval/bin/run_ci_evals.py) will support filtering by dataset:
 
 ```bash
 # Run all datasets (default)
@@ -110,13 +110,13 @@ uv run main.py --dataset customer_a_data --sanity
 
 #### CLI argument specifications
 
-* `--dataset <name>`: Evaluates only samples belonging to the named dataset.
-* `--datasets <name1,name2>`: Evaluates samples belonging to any of the listed datasets (comma-separated).
-* When no dataset flag is supplied, the framework defaults to evaluating all active datasets or the default core datasets.
+- `--dataset <name>`: Evaluates only samples belonging to the named dataset.
+- `--datasets <name1,name2>`: Evaluates samples belonging to any of the listed datasets (comma-separated).
+- When no dataset flag is supplied, the framework defaults to evaluating all active datasets or the default core datasets.
 
 ### Dataset-aware reporting and summary tables
 
-The evaluation reporting tools in [eval/bin/report_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/report_evals.py) will group and calculate metrics per dataset:
+The evaluation reporting tools in [eval/bin/report_evals.py](../../eval/bin/report_evals.py) will group and calculate metrics per dataset:
 
 #### Console output format
 
@@ -148,21 +148,24 @@ The generated markdown summary file will include a top-level dataset summary tab
 
 ```markdown
 ### Evaluation Summary: a2ui_v0_9_1_eval
+
 - **Status**: PASS
 - **Model**: `google/gemini-3.5-flash`
 - **Overall Pass Percentage**: `91.67%` (Threshold: `90.00%`)
 
 #### Dataset Performance Summary
-| Dataset Name | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |
-| :--- | :---: | :---: | :---: | :---: |
-| `customer_a_data` | 2 | 100.0% | 100.0% | 100.0% |
-| `core_v0_9_1` | 10 | 100.0% | 90.0% | 90.0% |
+
+| Dataset Name      | Total Samples | Algorithmic Pass Rate | Judging Pass Rate | Dataset Pass Rate |
+| :---------------- | :-----------: | :-------------------: | :---------------: | :---------------: |
+| `customer_a_data` |       2       |        100.0%         |      100.0%       |      100.0%       |
+| `core_v0_9_1`     |      10       |        100.0%         |       90.0%       |       90.0%       |
 
 #### Detailed Dataset Results: customer_a_data
-| Sample / Task | Algorithmic | Judging | Inference Time | Status |
-| :--- | :---: | :---: | :---: | :---: |
-| customer_a_flight_select | PASS | C | 3.20s | PASS |
-| customer_a_seat_picker | PASS | C | 2.80s | PASS |
+
+| Sample / Task            | Algorithmic | Judging | Inference Time | Status |
+| :----------------------- | :---------: | :-----: | :------------: | :----: |
+| customer_a_flight_select |    PASS     |    C    |     3.20s      |  PASS  |
+| customer_a_seat_picker   |    PASS     |    C    |     2.80s      |  PASS  |
 ```
 
 ---
@@ -173,53 +176,59 @@ To align the evaluation format with industry-standard LLM chat completion APIs (
 
 ### Standalone field reference
 
-| Field Name | Type | Required? | Clear Explanation & Purpose |
-| :--- | :--- | :--- | :--- |
-| **`name`** | string | **Yes** | Unique identifier for this evaluation sample (e.g. `customer_a_login_form`). Used in logs and result tables. |
-| **`description`** | string | **Yes** | Human-readable explanation of what this test case verifies. |
-| **`catalog`** | string | **Yes** | **Path to the component catalog JSON file** defining available UI widgets and validation functions. Supports the `{version}` placeholder (e.g. `specification/{version}/catalogs/basic/catalog.json`). Required on every data point so solvers and scorers explicitly know which component schema and rules apply. |
-| **`messages`** | array of message objects | **Yes** | **The conversation messages.** An ordered list of chat turns preceding and including the final UI request. Each item specifies a `role` (`user`, `assistant`, `system`, or `tool`), `content`, and optional `tool_calls` or `tool_call_id`. |
-| **`target`** | string | No | **The primary grading criteria and expected UI outcome.** Used by the LLM-as-a-judge (`measured_model_graded_qa`) to evaluate whether the generated output meets requirements. If omitted, defaults to the value of `description`. |
-| **`dataset`** | string | No | The name of the dataset module this sample belongs to. If omitted, the loader infers it from the YAML filename. |
-| **`system_prompt`** | string | No | **Domain-specific system instructions** representing the host application (e.g., application persona, customer service rules, or API constraints). The eval engine merges this domain prompt with the A2UI protocol instructions. |
-| **`protocol_role`** | string | No | **The A2UI persona instruction passed to the SDK prompt generator.** Specifies the high-level role for UI generation (Default: *"You are an AI assistant. Based on the following request, generate a stream of JSON messages that conform to the provided JSON Schemas."*). Most contributors can leave this omitted. |
-| **`generation_rules`** | string | No | **Technical protocol constraints appended to the SDK workflow prompt.** Defines formatting rules such as required surface ID naming, component root ID constraints, or adjacency list rules. Most contributors can leave this omitted to use standard protocol defaults. |
-| **`allowed_surface_ids`** | array of strings | No | List of valid surface identifiers the model is allowed to target (Default: `["main"]`). |
+| Field Name                | Type                     | Required? | Clear Explanation & Purpose                                                                                                                                                                                                                                                                                           |
+| :------------------------ | :----------------------- | :-------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **`name`**                | string                   | **Yes**   | Unique identifier for this evaluation sample (e.g. `customer_a_login_form`). Used in logs and result tables.                                                                                                                                                                                                          |
+| **`description`**         | string                   | **Yes**   | Human-readable explanation of what this test case verifies.                                                                                                                                                                                                                                                           |
+| **`catalog`**             | string                   | **Yes**   | **Path to the component catalog JSON file** defining available UI widgets and validation functions. Supports the `{version}` placeholder (e.g. `specification/{version}/catalogs/basic/catalog.json`). Required on every data point so solvers and scorers explicitly know which component schema and rules apply.    |
+| **`messages`**            | array of message objects | **Yes**   | **The conversation messages.** An ordered list of chat turns preceding and including the final UI request. Each item specifies a `role` (`user`, `assistant`, `system`, or `tool`), `content`, and optional `tool_calls` or `tool_call_id`.                                                                           |
+| **`target`**              | string                   | No        | **The primary grading criteria and expected UI outcome.** Used by the LLM-as-a-judge (`measured_model_graded_qa`) to evaluate whether the generated output meets requirements. If omitted, defaults to the value of `description`.                                                                                    |
+| **`dataset`**             | string                   | No        | The name of the dataset module this sample belongs to. If omitted, the loader infers it from the YAML filename.                                                                                                                                                                                                       |
+| **`system_prompt`**       | string                   | No        | **Domain-specific system instructions** representing the host application (e.g., application persona, customer service rules, or API constraints). The eval engine merges this domain prompt with the A2UI protocol instructions.                                                                                     |
+| **`protocol_role`**       | string                   | No        | **The A2UI persona instruction passed to the SDK prompt generator.** Specifies the high-level role for UI generation (Default: _"You are an AI assistant. Based on the following request, generate a stream of JSON messages that conform to the provided JSON Schemas."_). Most contributors can leave this omitted. |
+| **`generation_rules`**    | string                   | No        | **Technical protocol constraints appended to the SDK workflow prompt.** Defines formatting rules such as required surface ID naming, component root ID constraints, or adjacency list rules. Most contributors can leave this omitted to use standard protocol defaults.                                              |
+| **`allowed_surface_ids`** | array of strings         | No        | List of valid surface identifiers the model is allowed to target (Default: `["main"]`).                                                                                                                                                                                                                               |
 
 ---
 
 ### Detailed breakdown of key concepts
 
 #### 1. Why `catalog` is required
+
 Every A2UI evaluation requires a component catalog. Making `catalog` mandatory removes ambiguity for contributors, solver prompt generators, and validators. A contributor always explicitly declares the catalog path (such as `specification/{version}/catalogs/basic/catalog.json` for standard protocol widgets or a domain catalog path like `specification/{version}/catalogs/customer_a/catalog.json`).
 
 #### 2. Why `messages` is the preferred terminology
+
 Across the LLM ecosystem (OpenAI, Anthropic, LiteLLM, Ollama, and Inspect AI), `messages` is the universal term for chat interaction sequences:
-* **Direct framework alignment**: Inspect AI stores conversation turns in `state.messages`. Using `messages` in our YAML schema creates a 1:1 conceptual mapping.
-* **Avoids ambiguity**: The word "context" is overloaded in AI (often referring to RAG retrieval chunks, context windows, or A2UI action event contexts like `action.event.context`). `messages` unambiguously refers to the chat history.
-* **Consistent single-turn and multi-turn authoring**:
-  * **Single-turn prompt**: `messages` contains a single user turn (`role: user`, `content: "Create a login form"`).
-  * **Multi-turn conversation**: `messages` contains prior user, assistant, and tool turns, ending with the user turn that prompts the UI response.
+
+- **Direct framework alignment**: Inspect AI stores conversation turns in `state.messages`. Using `messages` in our YAML schema creates a 1:1 conceptual mapping.
+- **Avoids ambiguity**: The word "context" is overloaded in AI (often referring to RAG retrieval chunks, context windows, or A2UI action event contexts like `action.event.context`). `messages` unambiguously refers to the chat history.
+- **Consistent single-turn and multi-turn authoring**:
+  - **Single-turn prompt**: `messages` contains a single user turn (`role: user`, `content: "Create a login form"`).
+  - **Multi-turn conversation**: `messages` contains prior user, assistant, and tool turns, ending with the user turn that prompts the UI response.
 
 #### 3. `target` vs `description`
-* **`description`** is a high-level summary of what the test case does, used for human documentation and readability in test manifests.
-* **`target`** is the detailed rubric evaluated by the grading model. It specifies the exact expectations for the UI, such as required component types (e.g. "Must contain a Card with a TextField and a primary Button"), data bindings (e.g. "TextField must bind to `/auth/email`"), and interactive actions.
+
+- **`description`** is a high-level summary of what the test case does, used for human documentation and readability in test manifests.
+- **`target`** is the detailed rubric evaluated by the grading model. It specifies the exact expectations for the UI, such as required component types (e.g. "Must contain a Card with a TextField and a primary Button"), data bindings (e.g. "TextField must bind to `/auth/email`"), and interactive actions.
 
 #### 4. `system_prompt` vs `protocol_role`
-* **`system_prompt`** represents the **domain application persona**. In a real application, an agent has domain rules unrelated to A2UI (for example: *"You are the Acme Airlines Agent. Always confirm passenger baggage before checkout."*).
-* **`protocol_role`** represents the **A2UI protocol prompt**. This is the standard SDK instruction telling the LLM that it must output structured JSON conforming to the A2UI schema.
-* The eval engine merges both: domain instructions are presented first, followed by protocol instructions.
+
+- **`system_prompt`** represents the **domain application persona**. In a real application, an agent has domain rules unrelated to A2UI (for example: _"You are the Acme Airlines Agent. Always confirm passenger baggage before checkout."_).
+- **`protocol_role`** represents the **A2UI protocol prompt**. This is the standard SDK instruction telling the LLM that it must output structured JSON conforming to the A2UI schema.
+- The eval engine merges both: domain instructions are presented first, followed by protocol instructions.
 
 #### 5. `generation_rules`
-* This field controls the technical A2UI rules injected into the prompt's `## Workflow Description:` section.
-* Standard protocol rules (e.g. *"Construct the UI on surfaceId 'main'"* and *"Ensure there is one root component with id 'root'"*) are injected by default.
-* If a test case needs custom protocol behavior (for example, rendering to a surface named `'sidebar'`), the contributor specifies those extra rules in `generation_rules`.
+
+- This field controls the technical A2UI rules injected into the prompt's `## Workflow Description:` section.
+- Standard protocol rules (e.g. _"Construct the UI on surfaceId 'main'"_ and _"Ensure there is one root component with id 'root'"_) are injected by default.
+- If a test case needs custom protocol behavior (for example, rendering to a surface named `'sidebar'`), the contributor specifies those extra rules in `generation_rules`.
 
 ---
 
 ### JSON schema specification
 
-The schema in [eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json) validates the clean dataset structure with `catalog` required:
+The schema in [eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json) validates the clean dataset structure with `catalog` required:
 
 ```json
 {
@@ -267,13 +276,13 @@ The schema in [eval/datasets/dataset_schema.json](file:///Users/jsimionato/devel
               "items": {
                 "type": "object",
                 "properties": {
-                  "id": { "type": "string" },
-                  "type": { "type": "string", "enum": ["function"] },
+                  "id": {"type": "string"},
+                  "type": {"type": "string", "enum": ["function"]},
                   "function": {
                     "type": "object",
                     "properties": {
-                      "name": { "type": "string" },
-                      "arguments": { "type": "string" }
+                      "name": {"type": "string"},
+                      "arguments": {"type": "string"}
                     },
                     "required": ["name", "arguments"]
                   }
@@ -307,7 +316,7 @@ The schema in [eval/datasets/dataset_schema.json](file:///Users/jsimionato/devel
       },
       "allowed_surface_ids": {
         "type": "array",
-        "items": { "type": "string" },
+        "items": {"type": "string"},
         "description": "Allowed surface IDs for the output."
       }
     },
@@ -325,15 +334,15 @@ Below is an example of an agentic data point representing a flight booking assis
 - name: customer_a_flight_confirmation
   dataset: customer_a_data
   description: Multi-turn flight booking agent renders confirmation card with custom catalog.
-  catalog: "specification/{version}/catalogs/customer_a/catalog.json"
+  catalog: 'specification/{version}/catalogs/customer_a/catalog.json'
   system_prompt: |
     You are the Acme Airlines Booking Agent. Assist users with flight reservations.
     Always confirm passenger details and baggage selections before finalizing bookings.
   messages:
     - role: user
-      content: "I want to book flight AC-402 for Jane Doe."
+      content: 'I want to book flight AC-402 for Jane Doe.'
     - role: assistant
-      content: "Let me check the flight details and available seats for flight AC-402."
+      content: 'Let me check the flight details and available seats for flight AC-402.'
       tool_calls:
         - id: call_flight_lookup_001
           type: function
@@ -344,9 +353,9 @@ Below is an example of an agentic data point representing a flight booking assis
       tool_call_id: call_flight_lookup_001
       content: '{"status": "available", "flight": "AC-402", "origin": "SFO", "dest": "JFK", "departure": "2026-08-15T08:00:00Z", "price": 350.00}'
     - role: assistant
-      content: "Flight AC-402 from SFO to JFK is available for $350.00. Would you like to select a seat and confirm?"
+      content: 'Flight AC-402 from SFO to JFK is available for $350.00. Would you like to select a seat and confirm?'
     - role: user
-      content: "Yes, please show me the booking confirmation UI with a button to confirm and an option to add baggage."
+      content: 'Yes, please show me the booking confirmation UI with a button to confirm and an option to add baggage.'
   target: |
     The response must generate valid A2UI JSON containing:
     1. A Card or Column root container.
@@ -363,10 +372,10 @@ Below is an example of a migrated single-turn core data point:
 - name: contact_form
   dataset: core_v0_9_1
   description: Renders a contact form with email and submit button.
-  catalog: "specification/{version}/catalogs/basic/catalog.json"
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
   messages:
     - role: user
-      content: "Create a contact form with an email text field and submit button."
+      content: 'Create a contact form with an email text field and submit button.'
   target: |
     The response must contain a Card or Column with a TextField bound to '/contact/email' and a primary Button.
 ```
@@ -375,7 +384,7 @@ Below is an example of a migrated single-turn core data point:
 
 ## 3. Eval engine execution mechanics
 
-When executing an evaluation sample, the solver in [eval/a2ui_eval/strategies/format.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/strategies/format.py) coordinates prompt merging, chat message list assembly, inference, and scoring.
+When executing an evaluation sample, the solver in [eval/a2ui_eval/strategies/format.py](../../eval/a2ui_eval/strategies/format.py) coordinates prompt merging, chat message list assembly, inference, and scoring.
 
 ### Step 1: System prompt generation and merging
 
@@ -401,9 +410,9 @@ def merge_system_prompts(domain_prompt: str, a2ui_prompt: str) -> str:
 
 The solver populates `state.messages` directly from the sample's `messages` array:
 
-* **User turns**: Converted to `ChatMessageUser(content=turn["content"])`.
-* **Assistant turns**: Converted to `ChatMessageAssistant(content=turn.get("content", ""), tool_calls=turn.get("tool_calls"))`.
-* **Tool turns**: Converted to `ChatMessageTool(content=turn["content"], tool_call_id=turn["tool_call_id"])`.
+- **User turns**: Converted to `ChatMessageUser(content=turn["content"])`.
+- **Assistant turns**: Converted to `ChatMessageAssistant(content=turn.get("content", ""), tool_calls=turn.get("tool_calls"))`.
+- **Tool turns**: Converted to `ChatMessageTool(content=turn["content"], tool_call_id=turn["tool_call_id"])`.
 
 The final turn in `messages` triggers the assistant's UI generation response.
 
@@ -413,13 +422,13 @@ The solver invokes `measured_generate()`, passing the assembled message list to
 
 ### Step 4: Multi-stage scoring
 
-1. **Programmatic validation ([a2ui_scorer](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py))**:
-   * Resolves the required `catalog` path specified in the data point.
-   * Parses JSON from `<a2ui-json>` tags.
-   * Runs the catalog validator ([A2uiValidator](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/agent_sdks/python/a2ui_agent/README.md)) to verify schema adherence, parent-child references, and root component presence.
-2. **LLM judge scoring ([measured_model_graded_qa](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py))**:
-   * Evaluates the generated UI against the `target` criteria using the grading model (e.g. `google/gemini-3.5-flash`).
-   * Produces a grade of `C` (Correct), `P` (Partial Credit), or `I` (Incorrect).
+1. **Programmatic validation ([a2ui_scorer](../../eval/a2ui_eval/scorers.py))**:
+   - Resolves the required `catalog` path specified in the data point.
+   - Parses JSON from `<a2ui-json>` tags.
+   - Runs the catalog validator ([A2uiValidator](../../agent_sdks/python/a2ui_agent/README.md)) to verify schema adherence, parent-child references, and root component presence.
+2. **LLM judge scoring ([measured_model_graded_qa](../../eval/a2ui_eval/scorers.py))**:
+   - Evaluates the generated UI against the `target` criteria using the grading model (e.g. `google/gemini-3.5-flash`).
+   - Produces a grade of `C` (Correct), `P` (Partial Credit), or `I` (Incorrect).
 
 ---
 
@@ -440,7 +449,7 @@ cd eval
 bin/transcrypt -p <PASSWORD>
 ```
 
-*(Request the password from an A2UI maintainer if you do not have it.)*
+_(Request the password from an A2UI maintainer if you do not have it.)_
 
 Once decrypted, all YAML files in `eval/datasets/` appear as regular plaintext files on your local filesystem.
 
@@ -453,11 +462,11 @@ Once decrypted, all YAML files in `eval/datasets/` appear as regular plaintext f
 - name: customer_a_login_form
   dataset: customer_a_data
   description: Tests generation of a secure login card with email and password fields.
-  catalog: "specification/{version}/catalogs/basic/catalog.json"
-  system_prompt: "You are the Customer A Authentication Agent. Help users sign in."
+  catalog: 'specification/{version}/catalogs/basic/catalog.json'
+  system_prompt: 'You are the Customer A Authentication Agent. Help users sign in.'
   messages:
     - role: user
-      content: "Create a login form with email, password, and a submit button."
+      content: 'Create a login form with email, password, and a submit button.'
   target: |
     The response must contain:
     1. A Card or Column root component.
@@ -620,7 +629,7 @@ if __name__ == "__main__":
 
 ### In-memory runtime fallback (loader safety)
 
-To prevent failures if a developer branch or scratch file still contains legacy fields during the transition period, the dataset loader in [eval/a2ui_eval/dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/dataset.py) includes a runtime normalizer:
+To prevent failures if a developer branch or scratch file still contains legacy fields during the transition period, the dataset loader in [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py) includes a runtime normalizer:
 
 1. If a sample contains `promptText` instead of `messages`, the loader automatically constructs `messages = [ChatMessageUser(content=item["promptText"])]` in memory.
 2. If `catalog` is missing from a legacy sample during loading, the loader defaults to `specification/{version}/catalogs/basic/catalog.json`.
@@ -656,16 +665,16 @@ To prevent failures if a developer branch or scratch file still contains legacy
 
 ## Code changes roadmap
 
-| File Path | Nature of Change | Purpose |
-| :--- | :--- | :--- |
-| [eval/datasets/dataset_schema.json](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets/dataset_schema.json) | Update | Require `catalog`, use `messages` for chat turns, and include `dataset`, `system_prompt`, `protocol_role`, and `generation_rules` fields. |
-| [eval/datasets/core_v0_9_1.yaml](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) | Create | Migrated v0.9.1 data points using the `messages` format and explicit `catalog`. |
-| [eval/datasets/core_v1_0.yaml](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/datasets) | Create | Migrated v1.0 data points using the `messages` format and explicit `catalog`. |
-| [eval/a2ui_eval/dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/dataset.py) | Update | Add dataset discovery, `messages` array conversion to Inspect AI `ChatMessage` objects, required `catalog` enforcement, and validation CLI. |
-| [eval/a2ui_eval/strategies/format.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/strategies/format.py) | Update | Implement domain system prompt merging and `messages` chat history prepending. |
-| [eval/a2ui_eval/scorers.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/a2ui_eval/scorers.py) | Update | Use explicit `catalog` path in `a2ui_scorer`. |
-| [eval/tasks.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tasks.py) | Update | Point to migrated datasets and accept `dataset` and `datasets` filtering parameters. |
-| [eval/main.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/main.py) | Update | Add `--dataset` and `--datasets` CLI flags. |
-| [eval/bin/report_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/report_evals.py) | Update | Add per-dataset grouping in console summaries and markdown reports. |
-| [eval/bin/run_ci_evals.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/bin/run_ci_evals.py) | Update | Pass dataset filtering arguments to `main.py`. |
-| [eval/tests/test_dataset.py](file:///Users/jsimionato/development/a2ui_repos/datapoint/A2UI/eval/tests/test_dataset.py) | Update | Add unit tests for dataset loading, `messages` chat turn parsing, required `catalog` check, and schema validation. |
+| File Path                                                                        | Nature of Change | Purpose                                                                                                                                     |
+| :------------------------------------------------------------------------------- | :--------------- | :------------------------------------------------------------------------------------------------------------------------------------------ |
+| [eval/datasets/dataset_schema.json](../../eval/datasets/dataset_schema.json)     | Update           | Require `catalog`, use `messages` for chat turns, and include `dataset`, `system_prompt`, `protocol_role`, and `generation_rules` fields.   |
+| [eval/datasets/core_v0_9_1.yaml](../../eval/datasets)                            | Create           | Migrated v0.9.1 data points using the `messages` format and explicit `catalog`.                                                             |
+| [eval/datasets/core_v1_0.yaml](../../eval/datasets)                              | Create           | Migrated v1.0 data points using the `messages` format and explicit `catalog`.                                                               |
+| [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py)                     | Update           | Add dataset discovery, `messages` array conversion to Inspect AI `ChatMessage` objects, required `catalog` enforcement, and validation CLI. |
+| [eval/a2ui_eval/strategies/format.py](../../eval/a2ui_eval/strategies/format.py) | Update           | Implement domain system prompt merging and `messages` chat history prepending.                                                              |
+| [eval/a2ui_eval/scorers.py](../../eval/a2ui_eval/scorers.py)                     | Update           | Use explicit `catalog` path in `a2ui_scorer`.                                                                                               |
+| [eval/tasks.py](../../eval/tasks.py)                                             | Update           | Point to migrated datasets and accept `dataset` and `datasets` filtering parameters.                                                        |
+| [eval/main.py](../../eval/main.py)                                               | Update           | Add `--dataset` and `--datasets` CLI flags.                                                                                                 |
+| [eval/bin/report_evals.py](../../eval/bin/report_evals.py)                       | Update           | Add per-dataset grouping in console summaries and markdown reports.                                                                         |
+| [eval/bin/run_ci_evals.py](../../eval/bin/run_ci_evals.py)                       | Update           | Pass dataset filtering arguments to `main.py`.                                                                                              |
+| [eval/tests/test_dataset.py](../../eval/tests/test_dataset.py)                   | Update           | Add unit tests for dataset loading, `messages` chat turn parsing, required `catalog` check, and schema validation.                          |

From 3820fa60e7a796982a2a0ba62370c1af33d1f9cc Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:21:09 +0930
Subject: [PATCH 07/14] fix(eval): fix mypy typing annotations in dataset and
 migrate scripts

---
 eval/a2ui_eval/dataset.py    | 5 +++--
 eval/bin/migrate_datasets.py | 7 ++++---
 eval/tests/test_dataset.py   | 2 +-
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index 63748c07e4..6736277edf 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -22,6 +22,7 @@
 import yaml
 from inspect_ai.dataset import MemoryDataset, Sample
 from inspect_ai.model import (
+    ChatMessage,
     ChatMessageAssistant,
     ChatMessageSystem,
     ChatMessageTool,
@@ -77,11 +78,11 @@ def _parse_tool_calls(
     return parsed
 
 
-def _parse_messages(item: dict[str, Any]) -> list[Any]:
+def _parse_messages(item: dict[str, Any]) -> list[ChatMessage]:
     """Parses a sample's messages list or promptText into Inspect AI ChatMessage objects."""
     messages_raw = item.get("messages")
     if messages_raw:
-        parsed = []
+        parsed: list[ChatMessage] = []
         call_id_to_func: dict[str, str] = {}
         for m in messages_raw:
             role = m.get("role", "user")
diff --git a/eval/bin/migrate_datasets.py b/eval/bin/migrate_datasets.py
index cae9cb7899..564ca3ba95 100644
--- a/eval/bin/migrate_datasets.py
+++ b/eval/bin/migrate_datasets.py
@@ -17,6 +17,7 @@
 
 import json
 import os
+from typing import Any
 import jsonschema
 import yaml
 
@@ -30,9 +31,9 @@
 }
 
 
-def migrate_sample(item: dict, dataset_name: str) -> dict:
+def migrate_sample(item: dict[str, Any], dataset_name: str) -> dict[str, Any]:
     """Transforms a single legacy sample dictionary into the clean messages schema."""
-    new_item = {
+    new_item: dict[str, Any] = {
         "name": item["name"],
         "dataset": dataset_name,
         "description": item["description"],
@@ -68,7 +69,7 @@ def migrate_sample(item: dict, dataset_name: str) -> dict:
     return new_item
 
 
-def main():
+def main() -> None:
     with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
         schema = json.load(f)
 
diff --git a/eval/tests/test_dataset.py b/eval/tests/test_dataset.py
index a737bdaa6e..5751a61863 100644
--- a/eval/tests/test_dataset.py
+++ b/eval/tests/test_dataset.py
@@ -100,5 +100,5 @@ def test_load_a2ui_dataset_multi_turn(tmp_path: Path) -> None:
     assert len(dataset[0].input) == 4
     assert isinstance(dataset[0].input[0], ChatMessageUser)
     assert isinstance(dataset[0].input[1], ChatMessageAssistant)
-    assert isinstance(dataset[0].input[2], ChatMessageTool)
+    assert dataset[0].metadata is not None
     assert dataset[0].metadata["system_prompt"] == "Domain prompt"

From 19eed17b9d9a28ae5b31ed57200cb8f61b8571cd Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:22:52 +0930
Subject: [PATCH 08/14] refactor(eval): remove migrate script and enable
 dynamic dataset discovery without hardcoded paths

---
 eval/bin/migrate_datasets.py                  | 107 ------------------
 eval/tasks.py                                 |  24 +---
 .../proposals/eval_suite_expansion.md         |  89 +--------------
 3 files changed, 5 insertions(+), 215 deletions(-)
 delete mode 100644 eval/bin/migrate_datasets.py

diff --git a/eval/bin/migrate_datasets.py b/eval/bin/migrate_datasets.py
deleted file mode 100644
index 564ca3ba95..0000000000
--- a/eval/bin/migrate_datasets.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Migrates legacy A2UI dataset YAML files to the modern messages format."""
-
-import json
-import os
-from typing import Any
-import jsonschema
-import yaml
-
-EVAL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-DATASETS_DIR = os.path.join(EVAL_DIR, "datasets")
-SCHEMA_PATH = os.path.join(DATASETS_DIR, "dataset_schema.json")
-
-MIGRATIONS = {
-    "prompts_v0_9_1.yaml": "core_v0_9_1.yaml",
-    "prompts_v1_0.yaml": "core_v1_0.yaml",
-}
-
-
-def migrate_sample(item: dict[str, Any], dataset_name: str) -> dict[str, Any]:
-    """Transforms a single legacy sample dictionary into the clean messages schema."""
-    new_item: dict[str, Any] = {
-        "name": item["name"],
-        "dataset": dataset_name,
-        "description": item["description"],
-        "catalog": (
-            item.get("catalog") or "specification/{version}/catalogs/basic/catalog.json"
-        ),
-    }
-
-    if "messages" in item:
-        new_item["messages"] = item["messages"]
-    elif "promptText" in item:
-        new_item["messages"] = [{"role": "user", "content": item["promptText"]}]
-    else:
-        raise ValueError(
-            f"Sample {item.get('name')} has neither messages nor promptText."
-        )
-
-    if "target" in item:
-        new_item["target"] = item["target"]
-    if "system_prompt" in item:
-        new_item["system_prompt"] = item["system_prompt"]
-    if "protocol_role" in item or "role_description" in item:
-        new_item["protocol_role"] = item.get("protocol_role") or item.get(
-            "role_description"
-        )
-    if "generation_rules" in item or "workflow_description" in item:
-        new_item["generation_rules"] = item.get("generation_rules") or item.get(
-            "workflow_description"
-        )
-    if "allowed_surface_ids" in item:
-        new_item["allowed_surface_ids"] = item["allowed_surface_ids"]
-
-    return new_item
-
-
-def main() -> None:
-    with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
-        schema = json.load(f)
-
-    for old_file, new_file in MIGRATIONS.items():
-        old_path = os.path.join(DATASETS_DIR, old_file)
-        new_path = os.path.join(DATASETS_DIR, new_file)
-
-        if not os.path.exists(old_path):
-            print(f"Skipping {old_file} (not found on disk).")
-            continue
-
-        with open(old_path, "r", encoding="utf-8") as f:
-            raw_data = yaml.safe_load(f) or []
-
-        dataset_name = os.path.splitext(new_file)[0]
-        migrated_data = [migrate_sample(sample, dataset_name) for sample in raw_data]
-
-        # Validate migrated data against updated schema
-        jsonschema.validate(instance=migrated_data, schema=schema)
-
-        # Write new modular dataset file
-        with open(new_path, "w", encoding="utf-8") as f:
-            yaml.dump(migrated_data, f, sort_keys=False, allow_unicode=True)
-        print(
-            f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)}"
-            " samples)."
-        )
-
-        # Delete legacy file
-        os.remove(old_path)
-        print(f"Removed legacy file: {old_file}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/eval/tasks.py b/eval/tasks.py
index 798599e513..6d0c2d40d4 100644
--- a/eval/tasks.py
+++ b/eval/tasks.py
@@ -16,22 +16,16 @@
 
 # pylint: disable=duplicate-code
 
-import sys
-from pathlib import Path
 from google import genai
 from google.genai import errors
-from inspect_ai import task, Task
+from inspect_ai import Task, task
 from inspect_ai.dataset import MemoryDataset, Sample
-from inspect_ai.scorer import scorer, Score, Scorer, Target
+from inspect_ai.scorer import Score, Scorer, Target, scorer
 from inspect_ai.solver import TaskState
+
 from a2ui_eval.dataset import load_a2ui_dataset
-from a2ui_eval.strategies import get_solver
 from a2ui_eval.scorers import a2ui_scorer, measured_model_graded_qa
-
-# Paths relative to the eval directory where we run inspect
-CURRENT_DIR = Path(__file__).resolve().parent
-DATASET_V0_9_1_PATH = (CURRENT_DIR / "datasets/core_v0_9_1.yaml").resolve()
-DATASET_V1_0_PATH = (CURRENT_DIR / "datasets/core_v1_0.yaml").resolve()
+from a2ui_eval.strategies import get_solver
 
 
 @scorer(metrics=[])
@@ -98,12 +92,7 @@ def a2ui_v0_9_1_eval(
     default_catalog_path = "specification/v0_9_1/catalogs/basic/catalog.json"
     format_name = strategy if strategy in ["express", "elemental"] else "json"
 
-    file_path = None
-    if not dataset and DATASET_V0_9_1_PATH.exists():
-        file_path = str(DATASET_V0_9_1_PATH)
-
     dataset_obj = load_a2ui_dataset(
-        file_path=file_path,
         dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
@@ -160,12 +149,7 @@ def a2ui_v1_0_eval(
     default_catalog_path = "specification/v1_0/catalogs/basic/catalog.json"
     format_name = strategy if strategy in ["express", "elemental"] else "json"
 
-    file_path = None
-    if not dataset and DATASET_V1_0_PATH.exists():
-        file_path = str(DATASET_V1_0_PATH)
-
     dataset_obj = load_a2ui_dataset(
-        file_path=file_path,
         dataset=dataset,
         default_catalog_path=default_catalog_path,
         version=active_version,
diff --git a/specification/proposals/eval_suite_expansion.md b/specification/proposals/eval_suite_expansion.md
index fdbef41256..880c1bf5cb 100644
--- a/specification/proposals/eval_suite_expansion.md
+++ b/specification/proposals/eval_suite_expansion.md
@@ -540,93 +540,6 @@ If Transcrypt is active, the staged diff will show encrypted cipher text.
 
 To guarantee a smooth transition from legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) to modular dataset files without breaking ongoing developer workflows or CI runs, the framework provides an automated migration script and an in-memory runtime fallback.
 
-### Automated migration script (`eval/bin/migrate_datasets.py`)
-
-A dedicated migration script automates the transformation of existing datasets into the modern `messages` format with the required `catalog` field:
-
-```python
-#!/usr/bin/env python3
-"""Migrates legacy A2UI dataset YAML files to the modern messages format."""
-
-import os
-import yaml
-import jsonschema
-import json
-
-EVAL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-DATASETS_DIR = os.path.join(EVAL_DIR, "datasets")
-SCHEMA_PATH = os.path.join(DATASETS_DIR, "dataset_schema.json")
-
-MIGRATIONS = {
-    "prompts_v0_9_1.yaml": "core_v0_9_1.yaml",
-    "prompts_v1_0.yaml": "core_v1_0.yaml",
-}
-
-def migrate_sample(item: dict, dataset_name: str) -> dict:
-    """Transforms a single legacy sample dictionary into the clean messages schema."""
-    new_item = {
-        "name": item["name"],
-        "dataset": dataset_name,
-        "description": item["description"],
-        "catalog": item.get("catalog") or "specification/{version}/catalogs/basic/catalog.json",
-    }
-
-    # Transform promptText to messages array
-    if "messages" in item:
-        new_item["messages"] = item["messages"]
-    elif "promptText" in item:
-        new_item["messages"] = [{"role": "user", "content": item["promptText"]}]
-    else:
-        raise ValueError(f"Sample {item.get('name')} has neither messages nor promptText.")
-
-    # Copy standard fields
-    if "target" in item:
-        new_item["target"] = item["target"]
-    if "system_prompt" in item:
-        new_item["system_prompt"] = item["system_prompt"]
-    if "protocol_role" in item or "role_description" in item:
-        new_item["protocol_role"] = item.get("protocol_role") or item.get("role_description")
-    if "generation_rules" in item or "workflow_description" in item:
-        new_item["generation_rules"] = item.get("generation_rules") or item.get("workflow_description")
-    if "allowed_surface_ids" in item:
-        new_item["allowed_surface_ids"] = item["allowed_surface_ids"]
-
-    return new_item
-
-def main():
-    with open(SCHEMA_PATH, "r", encoding="utf-8") as f:
-        schema = json.load(f)
-
-    for old_file, new_file in MIGRATIONS.items():
-        old_path = os.path.join(DATASETS_DIR, old_file)
-        new_path = os.path.join(DATASETS_DIR, new_file)
-
-        if not os.path.exists(old_path):
-            print(f"Skipping {old_file} (not found on disk).")
-            continue
-
-        with open(old_path, "r", encoding="utf-8") as f:
-            raw_data = yaml.safe_load(f) or []
-
-        dataset_name = os.path.splitext(new_file)[0]
-        migrated_data = [migrate_sample(sample, dataset_name) for sample in raw_data]
-
-        # Validate migrated data against updated schema
-        jsonschema.validate(instance=migrated_data, schema=schema)
-
-        # Write new modular dataset file
-        with open(new_path, "w", encoding="utf-8") as f:
-            yaml.dump(migrated_data, f, sort_keys=False, allow_unicode=True)
-        print(f"Successfully migrated {old_file} -> {new_file} ({len(migrated_data)} samples).")
-
-        # Delete legacy file
-        os.remove(old_path)
-        print(f"Removed legacy file: {old_file}")
-
-if __name__ == "__main__":
-    main()
-```
-
 ### In-memory runtime fallback (loader safety)
 
 To prevent failures if a developer branch or scratch file still contains legacy fields during the transition period, the dataset loader in [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py) includes a runtime normalizer:
@@ -644,7 +557,7 @@ To prevent failures if a developer branch or scratch file still contains legacy
    ```
 2. **Run migration script**: Execute the automated migration script:
    ```bash
-   uv run python bin/migrate_datasets.py
+
    ```
 3. **Verify dataset schema compliance**: Run the dataset test suite:
    ```bash

From e92762368e086b868d99fde1969f12d88b766ebf Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:23:37 +0930
Subject: [PATCH 09/14] docs(eval): retain migration explanation in proposal
 while removing migrate script

---
 .../proposals/eval_suite_expansion.md         | 43 ++++---------------
 1 file changed, 8 insertions(+), 35 deletions(-)

diff --git a/specification/proposals/eval_suite_expansion.md b/specification/proposals/eval_suite_expansion.md
index 880c1bf5cb..1112ee2580 100644
--- a/specification/proposals/eval_suite_expansion.md
+++ b/specification/proposals/eval_suite_expansion.md
@@ -538,41 +538,14 @@ If Transcrypt is active, the staged diff will show encrypted cipher text.
 
 ## 5. Migration path for existing data points
 
-To guarantee a smooth transition from legacy monolithic files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) to modular dataset files without breaking ongoing developer workflows or CI runs, the framework provides an automated migration script and an in-memory runtime fallback.
-
-### In-memory runtime fallback (loader safety)
-
-To prevent failures if a developer branch or scratch file still contains legacy fields during the transition period, the dataset loader in [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py) includes a runtime normalizer:
-
-1. If a sample contains `promptText` instead of `messages`, the loader automatically constructs `messages = [ChatMessageUser(content=item["promptText"])]` in memory.
-2. If `catalog` is missing from a legacy sample during loading, the loader defaults to `specification/{version}/catalogs/basic/catalog.json`.
-3. If a sample contains `role_description` or `workflow_description`, the loader automatically maps them to `protocol_role` and `generation_rules`.
-
-### Step-by-step migration workflow for maintainers
-
-1. **Decrypt datasets**: Initialize Transcrypt locally if not already unlocked:
-   ```bash
-   cd eval
-   bin/transcrypt -p <PASSWORD>
-   ```
-2. **Run migration script**: Execute the automated migration script:
-   ```bash
-
-   ```
-3. **Verify dataset schema compliance**: Run the dataset test suite:
-   ```bash
-   uv run python -m pytest tests/test_dataset.py
-   ```
-4. **Run sanity check evaluation**: Execute a quick eval run to confirm model inference and scoring work as expected:
-   ```bash
-   uv run main.py --sanity
-   ```
-5. **Stage and commit changes**: Git will automatically encrypt the new modular files via `.gitattributes`:
-   ```bash
-   git add eval/datasets/core_v0_9_1.yaml eval/datasets/core_v1_0.yaml
-   git rm eval/datasets/prompts_v0_9_1.yaml eval/datasets/prompts_v1_0.yaml
-   git commit -m "feat(eval): migrate dataset files to messages format"
-   ```
+The evaluation framework migrated legacy monolithic prompt files (`prompts_v0_9_1.yaml` and `prompts_v1_0.yaml`) into clean, modular dataset files (`core_v0_9_1.yaml` and `core_v1_0.yaml`) conforming to the new `messages` schema.
+
+### Migration strategy
+
+1. **Standardizing on `messages`**: Single-string `promptText` queries are converted into structured `messages` arrays with role-based conversation turns (`user`, `assistant`, `tool`, `system`).
+2. **Mandatory component catalogs**: Every sample specifies an explicit relative `catalog` path (e.g. `specification/{version}/catalogs/basic/catalog.json`).
+3. **Modular dataset separation**: Data points are partitioned into logical, manageable YAML files under `eval/datasets/` that are dynamically discovered by the dataset loader.
+4. **In-memory runtime fallback (loader safety)**: To prevent breakage if a developer branch or scratch file still contains legacy fields, the dataset loader in [eval/a2ui_eval/dataset.py](../../eval/a2ui_eval/dataset.py) includes a runtime normalizer that converts `promptText` to `messages` and maps legacy field aliases on the fly.
 
 ---
 

From faa761369f643f4a8241f5324cd0b2490a1e70ae Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:28:17 +0930
Subject: [PATCH 10/14] fix(eval): address code review feedback on
 gitattributes, error handling, and docstrings

---
 .gitattributes            |  2 +-
 eval/a2ui_eval/dataset.py |  3 ++-
 eval/bin/report_evals.py  | 45 +++++++++++++++++++++++++++++++++------
 eval/bin/run_ci_evals.py  | 33 +++++++++++++++++++++-------
 4 files changed, 66 insertions(+), 17 deletions(-)

diff --git a/.gitattributes b/.gitattributes
index e341638568..f822baf0e7 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1 @@
-#pattern  filter=crypt diff=crypt merge=crypt
+eval/datasets/*.yaml filter=crypt diff=crypt merge=crypt
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index 6736277edf..dc3756d19a 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -68,7 +68,8 @@ def _parse_tool_calls(
         if isinstance(func_args, str):
             try:
                 func_args = json.loads(func_args)
-            except Exception:
+            except (json.JSONDecodeError, TypeError):
+                # If it's not a valid JSON string, treat it as a raw string argument.
                 pass
         parsed.append(
             ToolCall(
diff --git a/eval/bin/report_evals.py b/eval/bin/report_evals.py
index 362c62b52e..74d17c67c8 100755
--- a/eval/bin/report_evals.py
+++ b/eval/bin/report_evals.py
@@ -25,7 +25,17 @@
 
 
 def extract_accuracy(log_data: dict[str, Any]) -> float:
-    """Extracts accuracy from parsed JSON log data."""
+    """Extracts accuracy metric from parsed JSON log data.
+
+    Args:
+        log_data: Dictionary containing parsed evaluation log data.
+
+    Returns:
+        The accuracy score as a float between 0.0 and 1.0.
+
+    Raises:
+        ValueError: If no scores or accuracy metrics are present.
+    """
     scores = log_data.get("results", {}).get("scores", [])
     if not scores:
         raise ValueError("No scores found in log file.")
@@ -41,7 +51,11 @@ def extract_accuracy(log_data: dict[str, Any]) -> float:
 
 
 def print_results_summary(log_data: dict[str, Any]) -> None:
-    """Prints a summary of the results for each sample grouped by dataset."""
+    """Prints a summary of the evaluation results for each sample grouped by dataset.
+
+    Args:
+        log_data: Dictionary containing parsed evaluation log data.
+    """
     samples = log_data.get("samples", [])
     print("\n=== Evaluation Results Summary ===")
 
@@ -131,7 +145,16 @@ def print_results_summary(log_data: dict[str, Any]) -> None:
 def generate_markdown_summary(
     log_data: dict[str, Any], accuracy_percentage: float, threshold: float
 ) -> str:
-    """Generates a markdown summary of the evaluation results."""
+    """Generates a markdown summary of the evaluation results.
+
+    Args:
+        log_data: Dictionary containing parsed evaluation log data.
+        accuracy_percentage: Pass percentage achieved in the evaluation.
+        threshold: Minimum pass percentage required by CI.
+
+    Returns:
+        A formatted markdown string summarizing evaluation performance.
+    """
     eval_spec = log_data.get("eval", {}) or {}
     task_name = eval_spec.get("task", "Unknown Task")
     model_name = eval_spec.get("model", "Unknown Model")
@@ -258,7 +281,14 @@ def generate_markdown_summary(
 
 
 def load_log_data(log_path: str) -> dict[str, Any]:
-    """Runs inspect log dump to get JSON and parses it."""
+    """Loads and parses Inspect AI evaluation log JSON data using inspect log dump.
+
+    Args:
+        log_path: Path to the .eval log file.
+
+    Returns:
+        A dictionary containing parsed evaluation log data.
+    """
     dump_cmd = ["uv", "run", "inspect", "log", "dump", log_path]
     dump_output = subprocess.check_output(dump_cmd, text=True)
     result: dict[str, Any] = json.loads(dump_output)
@@ -266,6 +296,7 @@ def load_log_data(log_path: str) -> dict[str, Any]:
 
 
 def main() -> None:
+    """Main entrypoint for reporting evaluation results."""
     parser = argparse.ArgumentParser(
         description="Report results from an Inspect AI eval log file."
     )
@@ -273,7 +304,7 @@ def main() -> None:
     args = parser.parse_args()
 
     if not os.path.exists(args.log):
-        print(f"Error: Log file not found: {args.log}")
+        print(f"Error: Log file not found: {args.log}", file=sys.stderr)
         sys.exit(1)
 
     print(f"Processing log file: {args.log}")
@@ -285,8 +316,8 @@ def main() -> None:
         percentage = accuracy * 100
         print(f"Pass percentage: {percentage:.2f}%")
         sys.exit(0)
-    except Exception as e:
-        print(f"Error processing log file: {e}")
+    except (subprocess.CalledProcessError, ValueError) as e:
+        print(f"Error processing log file: {e}", file=sys.stderr)
         sys.exit(1)
 
 
diff --git a/eval/bin/run_ci_evals.py b/eval/bin/run_ci_evals.py
index 3c476d4695..f6e001cd81 100755
--- a/eval/bin/run_ci_evals.py
+++ b/eval/bin/run_ci_evals.py
@@ -35,12 +35,28 @@
 
 
 def check_threshold(percentage: float, threshold: float) -> bool:
-    """Compares percentage with threshold."""
+    """Compares pass percentage against the required threshold.
+
+    Args:
+        percentage: The calculated pass percentage.
+        threshold: The minimum pass percentage threshold.
+
+    Returns:
+        True if percentage >= threshold, False otherwise.
+    """
     return percentage >= threshold
 
 
 def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
-    """Builds the command line arguments for main.py."""
+    """Builds the command line arguments for main.py execution.
+
+    Args:
+        args: Parsed command line arguments.
+        seed: Date-based seed for sample shuffling and logging.
+
+    Returns:
+        A list of command line arguments for subprocess execution.
+    """
     cmd = [
         "uv",
         "run",
@@ -67,6 +83,7 @@ def build_main_command(args: argparse.Namespace, seed: str) -> list[str]:
 
 
 def main() -> None:
+    """Main entrypoint for CI evaluation runner."""
     parser = argparse.ArgumentParser(description="Run A2UI evals for CI.")
     parser.add_argument(
         "--dataset",
@@ -85,8 +102,8 @@ def main() -> None:
         type=int,
         default=100,
         help=(
-            "Maximum number of samples to evaluate. Set to 0 for all samples. Default"
-            " is 100."
+            "Maximum number of samples to evaluate. Set to 0 for all samples."
+            " Default is 100."
         ),
     )
     parser.add_argument(
@@ -159,8 +176,8 @@ def main() -> None:
 
             if not check_threshold(percentage, args.threshold):
                 print(
-                    f"Error: Pass percentage {percentage:.2f}% is less than threshold"
-                    f" {args.threshold:.2f}%"
+                    f"Error: Pass percentage {percentage:.2f}% is less than"
+                    f" threshold {args.threshold:.2f}%"
                 )
                 all_passed = False
             else:
@@ -180,8 +197,8 @@ def main() -> None:
 
     if not all_passed:
         print(
-            "\nCI Failed: One or more evaluation strategies did not meet the threshold"
-            " or encountered an error."
+            "\nCI Failed: One or more evaluation strategies did not meet the"
+            " threshold or encountered an error."
         )
         sys.exit(1)
 

From b3188ccaff29077a4c4dd8d48bbaea06c7677373 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:32:24 +0930
Subject: [PATCH 11/14] feat(eval): migrate datasets and schema to native
 Inspect AI ToolCall format

---
 .../skills/a2ui-add-eval-datapoint/SKILL.md   |   6 +-
 eval/a2ui_eval/dataset.py                     |  19 +-
 eval/datasets/dataset_schema.json             |  43 +-
 .../multi_turn_conversation_dataset.yaml      | 811 +++++++++---------
 eval/tests/test_dataset.py                    |  26 +-
 5 files changed, 471 insertions(+), 434 deletions(-)

diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
index 2b78d11e4c..f866fc41fa 100644
--- a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -49,9 +49,9 @@ Every data point **must conform** to the JSON Schema in `eval/datasets/dataset_s
       tool_calls:
         - id: call_001
           type: function
-          function:
-            name: query_database
-            arguments: '{"key": "value"}'
+          function: query_database
+          arguments:
+            key: value
     - role: tool
       tool_call_id: call_001
       function: query_database
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index dc3756d19a..8bf90530b4 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -51,18 +51,17 @@ def _version_to_dir_name(version: str) -> str:
 def _parse_tool_calls(
     raw_tool_calls: list[dict[str, Any]] | None,
 ) -> list[ToolCall] | None:
-    """Converts raw tool calls into Inspect AI ToolCall objects."""
+    """Converts raw tool calls in native Inspect AI format into ToolCall objects."""
     if not raw_tool_calls:
         return None
     parsed = []
     for tc in raw_tool_calls:
-        call_id = tc.get("id", "")
-        call_type = tc.get("type", "function")
-        if "function" in tc and isinstance(tc["function"], dict):
-            func_name = tc["function"].get("name", "")
-            func_args = tc["function"].get("arguments", {})
+        func_name = tc.get("function")
+        if isinstance(func_name, dict):
+            func_args = func_name.get("arguments", {})
+            func_name = str(func_name.get("name", ""))
         else:
-            func_name = str(tc.get("function", ""))
+            func_name = str(func_name or "")
             func_args = tc.get("arguments", {})
 
         if isinstance(func_args, str):
@@ -71,9 +70,13 @@ def _parse_tool_calls(
             except (json.JSONDecodeError, TypeError):
                 # If it's not a valid JSON string, treat it as a raw string argument.
                 pass
+
         parsed.append(
             ToolCall(
-                id=call_id, function=func_name, arguments=func_args, type=call_type
+                id=str(tc.get("id", "")),
+                function=func_name,
+                arguments=func_args,
+                type=tc.get("type", "function"),
             )
         )
     return parsed
diff --git a/eval/datasets/dataset_schema.json b/eval/datasets/dataset_schema.json
index 48492d8a35..35a4cffce0 100644
--- a/eval/datasets/dataset_schema.json
+++ b/eval/datasets/dataset_schema.json
@@ -39,27 +39,39 @@
             },
             "tool_calls": {
               "type": "array",
-              "description": "List of tool calls made by the assistant in this turn.",
+              "description": "List of tool calls made by the assistant in this turn in native Inspect AI ToolCall format.",
               "items": {
                 "type": "object",
                 "properties": {
-                  "id": {"type": "string"},
-                  "type": {"type": "string", "enum": ["function"]},
+                  "id": {
+                    "type": "string",
+                    "description": "Unique identifier for the tool call."
+                  },
+                  "type": {
+                    "type": "string",
+                    "enum": ["function"],
+                    "default": "function"
+                  },
                   "function": {
+                    "type": "string",
+                    "description": "Function name directly as a string."
+                  },
+                  "arguments": {
                     "type": "object",
-                    "properties": {
-                      "name": {"type": "string"},
-                      "arguments": {"type": "string"}
-                    },
-                    "required": ["name", "arguments"]
+                    "description": "JSON dictionary of function arguments."
                   }
                 },
-                "required": ["id", "function"]
+                "required": ["id", "function", "arguments"],
+                "additionalProperties": false
               }
             },
             "tool_call_id": {
               "type": "string",
               "description": "Identifier linking a tool response turn to its preceding assistant tool call."
+            },
+            "function": {
+              "type": "string",
+              "description": "Optional function name on tool response turns."
             }
           },
           "required": ["role"]
@@ -95,12 +107,21 @@
       },
       "allowed_surface_ids": {
         "type": "array",
-        "items": {"type": "string"},
+        "items": {
+          "type": "string"
+        },
         "description": "Allowed surface IDs for the output."
       }
     },
     "required": ["name", "description", "catalog"],
-    "anyOf": [{"required": ["messages"]}, {"required": ["promptText"]}],
+    "anyOf": [
+      {
+        "required": ["messages"]
+      },
+      {
+        "required": ["promptText"]
+      }
+    ],
     "additionalProperties": false
   }
 }
diff --git a/eval/datasets/multi_turn_conversation_dataset.yaml b/eval/datasets/multi_turn_conversation_dataset.yaml
index 7be74b7e2b..8feeba25ae 100644
--- a/eval/datasets/multi_turn_conversation_dataset.yaml
+++ b/eval/datasets/multi_turn_conversation_dataset.yaml
@@ -1,407 +1,404 @@
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diff --git a/eval/tests/test_dataset.py b/eval/tests/test_dataset.py
index 5751a61863..b2a00d23b3 100644
--- a/eval/tests/test_dataset.py
+++ b/eval/tests/test_dataset.py
@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""Unit tests for dataset loading and tool call parsing."""
+
 from pathlib import Path
 import pytest
-from inspect_ai.model import ChatMessageUser, ChatMessageAssistant, ChatMessageTool
+from inspect_ai.model import ChatMessageAssistant, ChatMessageTool, ChatMessageUser
 from a2ui_eval.dataset import load_a2ui_dataset
 
 
@@ -85,11 +87,12 @@ def test_load_a2ui_dataset_multi_turn(tmp_path: Path) -> None:
       tool_calls:
         - id: call_1
           type: function
-          function:
-            name: lookup
-            arguments: '{"id": "123"}'
+          function: lookup
+          arguments:
+            id: "123"
     - role: tool
       tool_call_id: call_1
+      function: lookup
       content: '{"balance": 500}'
     - role: user
       content: "Render my balance card"
@@ -99,6 +102,19 @@ def test_load_a2ui_dataset_multi_turn(tmp_path: Path) -> None:
     assert len(dataset) == 1
     assert len(dataset[0].input) == 4
     assert isinstance(dataset[0].input[0], ChatMessageUser)
-    assert isinstance(dataset[0].input[1], ChatMessageAssistant)
+
+    assistant = dataset[0].input[1]
+    assert isinstance(assistant, ChatMessageAssistant)
+    assert assistant.tool_calls is not None
+    assert len(assistant.tool_calls) == 1
+    assert assistant.tool_calls[0].id == "call_1"
+    assert assistant.tool_calls[0].function == "lookup"
+    assert assistant.tool_calls[0].arguments == {"id": "123"}
+
+    tool_msg = dataset[0].input[2]
+    assert isinstance(tool_msg, ChatMessageTool)
+    assert tool_msg.tool_call_id == "call_1"
+    assert tool_msg.function == "lookup"
+
     assert dataset[0].metadata is not None
     assert dataset[0].metadata["system_prompt"] == "Domain prompt"

From 0fbc3bde03bb00487ccf40cd9891494a978ebee3 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:44:49 +0930
Subject: [PATCH 12/14] docs(eval): reinstate transcrypt upgrade instructions
 and remove gitattributes tracking

---
 .gitattributes            |  1 -
 eval/README.md            | 22 ++++++++++++++++++++++
 eval/a2ui_eval/dataset.py | 18 +++++++++++++++++-
 3 files changed, 39 insertions(+), 2 deletions(-)
 delete mode 100644 .gitattributes

diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index f822baf0e7..0000000000
--- a/.gitattributes
+++ /dev/null
@@ -1 +0,0 @@
-eval/datasets/*.yaml filter=crypt diff=crypt merge=crypt
diff --git a/eval/README.md b/eval/README.md
index 9fedcd5e7d..b4e2ae04da 100644
--- a/eval/README.md
+++ b/eval/README.md
@@ -46,6 +46,28 @@ Make sure you are in the `eval/` directory.
 
    After this setup, git transparently encrypts files on `git add` and decrypts them on checkout.
 
+### Upgrading Transcrypt
+
+If you pull updates that change the encryption settings (such as transitioning from MD5 to PBKDF2), you may encounter decryption errors during `git pull` or see OpenSSL deprecation warnings.
+
+To upgrade your local Transcrypt configuration to the latest settings:
+
+1. Run the upgrade command:
+
+   ```bash
+   bin/transcrypt --upgrade
+   ```
+
+   This updates the local filter scripts in your `.git` directory while preserving your saved password.
+
+2. Force Git to re-decrypt the files:
+
+   ```bash
+   git checkout HEAD -- $(git ls-crypt)
+   ```
+
+   This runs the files through the newly upgraded smudge filter, decrypting them.
+
 ### Executing Evals
 
 To run all datasets:
diff --git a/eval/a2ui_eval/dataset.py b/eval/a2ui_eval/dataset.py
index 8bf90530b4..704bc1c6ea 100644
--- a/eval/a2ui_eval/dataset.py
+++ b/eval/a2ui_eval/dataset.py
@@ -177,6 +177,16 @@ def load_a2ui_dataset(
             if filter_names and item_dataset not in filter_names:
                 continue
 
+            # Skip datasets specifically targeted at another protocol version if version is set
+            if version == "0.9.1" and (
+                item_dataset.endswith("v1_0") or item_dataset == "core_v1_0"
+            ):
+                continue
+            if version == "1.0" and (
+                item_dataset.endswith("v0_9_1") or item_dataset == "core_v0_9_1"
+            ):
+                continue
+
             catalog_path = (
                 item.get("catalog") or default_catalog_path or DEFAULT_CATALOG_PATH
             )
@@ -209,9 +219,15 @@ def load_a2ui_dataset(
 
             chat_messages = _parse_messages(item)
 
+            sample_id = (
+                f"{item_dataset}_{item.get('name')}"
+                if item.get("name")
+                else str(len(samples))
+            )
+
             samples.append(
                 Sample(
-                    id=item.get("name"),
+                    id=sample_id,
                     input=chat_messages,
                     target=item.get("target") or item["description"],
                     metadata={

From 213adc3a2a6b3251ab651784fd4a4ee5cdc7ee85 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Thu, 23 Jul 2026 12:52:28 +0930
Subject: [PATCH 13/14] docs(eval): update transcrypt flags in
 add-eval-datapoint skill

---
 .agents/skills/a2ui-add-eval-datapoint/SKILL.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
index f866fc41fa..40443c6ad1 100644
--- a/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
+++ b/.agents/skills/a2ui-add-eval-datapoint/SKILL.md
@@ -25,7 +25,7 @@ Datasets in `eval/datasets/` are encrypted at rest. Unlock Transcrypt locally be
 
 ```bash
 cd eval
-bin/transcrypt -p <PASSWORD>
+bin/transcrypt -y -c aes-256-cbc -p <PASSWORD>
 ```
 
 ### 2. Author the data point

From 302e2e7ab82af66307245d71ff723fc3edb95cd8 Mon Sep 17 00:00:00 2001
From: Jacob Simionato <jsimionato@google.com>
Date: Fri, 24 Jul 2026 15:13:09 +0930
Subject: [PATCH 14/14] fix(eval): enforce polymorphic message schemas per role
 in dataset_schema.json

---
 eval/datasets/dataset_schema.json | 139 +++++++++++++++++++++---------
 1 file changed, 96 insertions(+), 43 deletions(-)

diff --git a/eval/datasets/dataset_schema.json b/eval/datasets/dataset_schema.json
index 35a4cffce0..b32973d283 100644
--- a/eval/datasets/dataset_schema.json
+++ b/eval/datasets/dataset_schema.json
@@ -26,55 +26,108 @@
         "type": "array",
         "description": "Ordered chat completion messages preceding and including the final UI generation request.",
         "items": {
-          "type": "object",
-          "properties": {
-            "role": {
-              "type": "string",
-              "enum": ["user", "assistant", "system", "tool"],
-              "description": "The role of the message sender."
-            },
-            "content": {
-              "type": "string",
-              "description": "Text content of the message."
+          "oneOf": [
+            {
+              "type": "object",
+              "title": "User Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["user"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Text content of the user message."
+                }
+              },
+              "required": ["role", "content"],
+              "additionalProperties": false
             },
-            "tool_calls": {
-              "type": "array",
-              "description": "List of tool calls made by the assistant in this turn in native Inspect AI ToolCall format.",
-              "items": {
-                "type": "object",
-                "properties": {
-                  "id": {
-                    "type": "string",
-                    "description": "Unique identifier for the tool call."
-                  },
-                  "type": {
-                    "type": "string",
-                    "enum": ["function"],
-                    "default": "function"
-                  },
-                  "function": {
-                    "type": "string",
-                    "description": "Function name directly as a string."
-                  },
-                  "arguments": {
+            {
+              "type": "object",
+              "title": "Assistant Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["assistant"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Optional text response generated by the assistant."
+                },
+                "tool_calls": {
+                  "type": "array",
+                  "description": "List of tool calls made by the assistant in native Inspect AI ToolCall format.",
+                  "items": {
                     "type": "object",
-                    "description": "JSON dictionary of function arguments."
+                    "properties": {
+                      "id": {
+                        "type": "string",
+                        "description": "Unique identifier for the tool call."
+                      },
+                      "type": {
+                        "type": "string",
+                        "enum": ["function"],
+                        "default": "function"
+                      },
+                      "function": {
+                        "type": "string",
+                        "description": "Function name directly as a string."
+                      },
+                      "arguments": {
+                        "type": "object",
+                        "description": "JSON dictionary of function arguments."
+                      }
+                    },
+                    "required": ["id", "function", "arguments"],
+                    "additionalProperties": false
                   }
-                },
-                "required": ["id", "function", "arguments"],
-                "additionalProperties": false
-              }
+                }
+              },
+              "required": ["role"],
+              "additionalProperties": false
             },
-            "tool_call_id": {
-              "type": "string",
-              "description": "Identifier linking a tool response turn to its preceding assistant tool call."
+            {
+              "type": "object",
+              "title": "Tool Response Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["tool"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "Execution output returned by the tool."
+                },
+                "tool_call_id": {
+                  "type": "string",
+                  "description": "Identifier linking this tool response to its preceding assistant tool call."
+                },
+                "function": {
+                  "type": "string",
+                  "description": "Optional function name for the tool response."
+                }
+              },
+              "required": ["role", "content", "tool_call_id"],
+              "additionalProperties": false
             },
-            "function": {
-              "type": "string",
-              "description": "Optional function name on tool response turns."
+            {
+              "type": "object",
+              "title": "System Message",
+              "properties": {
+                "role": {
+                  "type": "string",
+                  "enum": ["system"]
+                },
+                "content": {
+                  "type": "string",
+                  "description": "System instruction prompt."
+                }
+              },
+              "required": ["role", "content"],
+              "additionalProperties": false
             }
-          },
-          "required": ["role"]
+          ]
         }
       },
       "promptText": {