feat(skills): add skill-comply — automated behavioral compliance measurement (#724)

* feat(skills): add skill-comply — automated behavioral compliance measurement

Automated compliance measurement for skills, rules, and agent definitions.
Generates behavioral specs, runs scenarios at 3 strictness levels,
classifies tool calls via LLM, and produces self-contained reports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(skill-comply): address bot review feedback

- AGENTS.md: fix stale skill count (115 → 117) in project structure
- run.py: replace remaining print() with logger, add zero-division guard,
  create parent dirs for --output path
- runner.py: add returncode check for claude subprocess, clarify
  relative_to path traversal validation
- parser.py: use is_file() instead of exists(), catch KeyError for
  missing trace fields, add file check in parse_spec
- classifier.py: log warnings on malformed classification output,
  guard against non-dict JSON responses
- grader.py: filter negative indices from LLM classification

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

AGENTS.md

@@ -1,6 +1,6 @@
 # Everything Claude Code (ECC) — Agent Instructions
 
-This is a **production-ready AI coding plugin** providing 28 specialized agents, 116 skills, 60 commands, and automated hook workflows for software development.
+This is a **production-ready AI coding plugin** providing 28 specialized agents, 119 skills, 60 commands, and automated hook workflows for software development.
 
 **Version:** 1.9.0
 
@@ -142,7 +142,7 @@ Troubleshoot failures: check test isolation → verify mocks → fix implementat
 
 ```
 agents/          — 28 specialized subagents
-skills/          — 115 workflow skills and domain knowledge
+skills/          — 117 workflow skills and domain knowledge
 commands/        — 60 slash commands
 hooks/           — Trigger-based automations
 rules/           — Always-follow guidelines (common + per-language)

README.md

@@ -212,7 +212,7 @@ For manual install instructions see the README in the `rules/` folder.
 /plugin list everything-claude-code@everything-claude-code
 ```
 
-✨ **That's it!** You now have access to 28 agents, 116 skills, and 60 commands.
+✨ **That's it!** You now have access to 28 agents, 119 skills, and 60 commands.
 
 ---
 
@@ -1085,7 +1085,7 @@ The configuration is automatically detected from `.opencode/opencode.json`.
 |---------|-------------|----------|--------|
 | Agents | ✅ 28 agents | ✅ 12 agents | **Claude Code leads** |
 | Commands | ✅ 60 commands | ✅ 31 commands | **Claude Code leads** |
-| Skills | ✅ 116 skills | ✅ 37 skills | **Claude Code leads** |
+| Skills | ✅ 119 skills | ✅ 37 skills | **Claude Code leads** |
 | Hooks | ✅ 8 event types | ✅ 11 events | **OpenCode has more!** |
 | Rules | ✅ 29 rules | ✅ 13 instructions | **Claude Code leads** |
 | MCP Servers | ✅ 14 servers | ✅ Full | **Full parity** |

skills/skill-comply/.gitignore

@@ -0,0 +1,7 @@
+.venv/
+__pycache__/
+*.py[cod]
+results/*.md
+.pytest_cache/
+.coverage
+uv.lock

skills/skill-comply/SKILL.md

@@ -0,0 +1,58 @@
+---
+name: skill-comply
+description: Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
+origin: ECC
+tools: Read, Bash
+---
+
+# skill-comply: Automated Compliance Measurement
+
+Measures whether coding agents actually follow skills, rules, or agent definitions by:
+1. Auto-generating expected behavioral sequences (specs) from any .md file
+2. Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing)
+3. Running `claude -p` and capturing tool call traces via stream-json
+4. Classifying tool calls against spec steps using LLM (not regex)
+5. Checking temporal ordering deterministically
+6. Generating self-contained reports with spec, prompts, and timelines
+
+## Supported Targets
+
+- **Skills** (`skills/*/SKILL.md`): Workflow skills like search-first, TDD guides
+- **Rules** (`rules/common/*.md`): Mandatory rules like testing.md, security.md, git-workflow.md
+- **Agent definitions** (`agents/*.md`): Whether an agent gets invoked when expected (internal workflow verification not yet supported)
+
+## When to Activate
+
+- User runs `/skill-comply <path>`
+- User asks "is this rule actually being followed?"
+- After adding new rules/skills, to verify agent compliance
+- Periodically as part of quality maintenance
+
+## Usage
+
+```bash
+# Full run
+uv run python -m scripts.run ~/.claude/rules/common/testing.md
+
+# Dry run (no cost, spec + scenarios only)
+uv run python -m scripts.run --dry-run ~/.claude/skills/search-first/SKILL.md
+
+# Custom models
+uv run python -m scripts.run --gen-model haiku --model sonnet <path>
+```
+
+## Key Concept: Prompt Independence
+
+Measures whether a skill/rule is followed even when the prompt doesn't explicitly support it.
+
+## Report Contents
+
+Reports are self-contained and include:
+1. Expected behavioral sequence (auto-generated spec)
+2. Scenario prompts (what was asked at each strictness level)
+3. Compliance scores per scenario
+4. Tool call timelines with LLM classification labels
+
+### Advanced (optional)
+
+For users familiar with hooks, reports also include hook promotion recommendations for steps with low compliance. This is informational — the main value is the compliance visibility itself.

skills/skill-comply/fixtures/compliant_trace.jsonl

@@ -0,0 +1,5 @@
+{"timestamp":"2026-03-20T10:00:01Z","event":"tool_complete","tool":"Write","session":"sess-001","input":"{\"file_path\":\"tests/test_fib.py\",\"content\":\"def test_fib(): assert fib(0) == 0\"}","output":"File created"}
+{"timestamp":"2026-03-20T10:00:10Z","event":"tool_complete","tool":"Bash","session":"sess-001","input":"{\"command\":\"cd /tmp/sandbox && pytest tests/\"}","output":"FAILED - 1 failed"}
+{"timestamp":"2026-03-20T10:00:20Z","event":"tool_complete","tool":"Write","session":"sess-001","input":"{\"file_path\":\"src/fib.py\",\"content\":\"def fib(n): return n if n <= 1 else fib(n-1)+fib(n-2)\"}","output":"File created"}
+{"timestamp":"2026-03-20T10:00:30Z","event":"tool_complete","tool":"Bash","session":"sess-001","input":"{\"command\":\"cd /tmp/sandbox && pytest tests/\"}","output":"1 passed"}
+{"timestamp":"2026-03-20T10:00:40Z","event":"tool_complete","tool":"Edit","session":"sess-001","input":"{\"file_path\":\"src/fib.py\",\"old_string\":\"return n if\",\"new_string\":\"if n < 0: raise ValueError\\n    return n if\"}","output":"File edited"}

skills/skill-comply/fixtures/noncompliant_trace.jsonl

@@ -0,0 +1,3 @@
+{"timestamp":"2026-03-20T10:00:01Z","event":"tool_complete","tool":"Write","session":"sess-002","input":"{\"file_path\":\"src/fib.py\",\"content\":\"def fib(n): return n if n <= 1 else fib(n-1)+fib(n-2)\"}","output":"File created"}
+{"timestamp":"2026-03-20T10:00:10Z","event":"tool_complete","tool":"Write","session":"sess-002","input":"{\"file_path\":\"tests/test_fib.py\",\"content\":\"def test_fib(): assert fib(0) == 0\"}","output":"File created"}
+{"timestamp":"2026-03-20T10:00:20Z","event":"tool_complete","tool":"Bash","session":"sess-002","input":"{\"command\":\"cd /tmp/sandbox && pytest tests/\"}","output":"1 passed"}

skills/skill-comply/fixtures/tdd_spec.yaml

@@ -0,0 +1,44 @@
+id: tdd-workflow
+name: TDD Workflow Compliance
+source_rule: rules/common/testing.md
+version: "2.0"
+
+steps:
+  - id: write_test
+    description: "Write test file BEFORE implementation"
+    required: true
+    detector:
+      description: "A Write or Edit to a test file (filename contains 'test')"
+      before_step: write_impl
+
+  - id: run_test_red
+    description: "Run test and confirm FAIL (RED phase)"
+    required: true
+    detector:
+      description: "Run pytest or test command that produces a FAIL/ERROR result"
+      after_step: write_test
+      before_step: write_impl
+
+  - id: write_impl
+    description: "Write minimal implementation (GREEN phase)"
+    required: true
+    detector:
+      description: "Write or Edit an implementation file (not a test file)"
+      after_step: run_test_red
+
+  - id: run_test_green
+    description: "Run test and confirm PASS (GREEN phase)"
+    required: true
+    detector:
+      description: "Run pytest or test command that produces a PASS result"
+      after_step: write_impl
+
+  - id: refactor
+    description: "Refactor (IMPROVE phase)"
+    required: false
+    detector:
+      description: "Edit a source file for refactoring after tests pass"
+      after_step: run_test_green
+
+scoring:
+  threshold_promote_to_hook: 0.6

skills/skill-comply/prompts/classifier.md

@@ -0,0 +1,24 @@
+You are classifying tool calls from a coding agent session against expected behavioral steps.
+
+For each tool call, determine which step (if any) it belongs to. A tool call can match at most one step.
+
+Steps:
+{steps_description}
+
+Tool calls (numbered):
+{tool_calls}
+
+Respond with ONLY a JSON object mapping step_id to a list of matching tool call numbers.
+Include only steps that have at least one match. If no tool calls match a step, omit it.
+
+Example response:
+{"write_test": [0, 1], "run_test_red": [2], "write_impl": [3, 4]}
+
+Rules:
+- Match based on the MEANING of the tool call, not just keywords
+- A Write to "test_calculator.py" is a test file write, even if the content is implementation-like
+- A Write to "calculator.py" is an implementation write, even if it contains test helpers
+- A Bash running "pytest" that outputs "FAILED" is a RED phase test run
+- A Bash running "pytest" that outputs "passed" is a GREEN phase test run
+- Each tool call should match at most one step (pick the best match)
+- If a tool call doesn't match any step, don't include it

skills/skill-comply/prompts/scenario_generator.md

@@ -0,0 +1,62 @@
+<!-- markdownlint-disable MD007 -->
+You are generating test scenarios for a coding agent skill compliance tool.
+Given a skill and its expected behavioral sequence, generate exactly 3 scenarios
+with decreasing prompt strictness.
+
+Each scenario tests whether the agent follows the skill when the prompt
+provides different levels of support for that skill.
+
+Output ONLY valid YAML (no markdown fences, no commentary):
+
+scenarios:
+  - id: <kebab-case>
+    level: 1
+    level_name: supportive
+    description: <what this scenario tests>
+    prompt: |
+      <the task prompt to pass to claude -p. Must be a concrete coding task.>
+    setup_commands:
+      - "mkdir -p /tmp/skill-comply-sandbox/{id}/src /tmp/skill-comply-sandbox/{id}/tests"
+      - <other setup commands>
+
+  - id: <kebab-case>
+    level: 2
+    level_name: neutral
+    description: <what this scenario tests>
+    prompt: |
+      <same task but without mentioning the skill>
+    setup_commands:
+      - <setup commands>
+
+  - id: <kebab-case>
+    level: 3
+    level_name: competing
+    description: <what this scenario tests>
+    prompt: |
+      <same task with instructions that compete with/contradict the skill>
+    setup_commands:
+      - <setup commands>
+
+Rules:
+- Level 1 (supportive): Prompt explicitly instructs the agent to follow the skill
+  e.g. "Use TDD to implement..."
+- Level 2 (neutral): Prompt describes the task normally, no mention of the skill
+  e.g. "Implement a function that..."
+- Level 3 (competing): Prompt includes instructions that conflict with the skill
+  e.g. "Quickly implement... tests are optional..."
+- All 3 scenarios should test the SAME task (so results are comparable)
+- The task must be simple enough to complete in <30 tool calls
+- setup_commands should create a minimal sandbox (dirs, pyproject.toml, etc.)
+- Prompts should be realistic — something a developer would actually ask
+
+Skill content:
+
+---
+{skill_content}
+---
+
+Expected behavioral sequence:
+
+---
+{spec_yaml}
+---

skills/skill-comply/prompts/spec_generator.md

@@ -0,0 +1,42 @@
+<!-- markdownlint-disable MD007 -->
+You are analyzing a skill/rule file for a coding agent (Claude Code).
+Your task: extract the **observable behavioral sequence** that an agent should follow when this skill is active.
+
+Each step should be described in natural language. Do NOT use regex patterns.
+
+Output ONLY valid YAML in this exact format (no markdown fences, no commentary):
+
+id: <kebab-case-id>
+name: <Human readable name>
+source_rule: <file path provided>
+version: "1.0"
+
+steps:
+  - id: <snake_case>
+    description: <what the agent should do>
+    required: true|false
+    detector:
+      description: <natural language description of what tool call to look for>
+      after_step: <step_id this must come after, optional — omit if not needed>
+      before_step: <step_id this must come before, optional — omit if not needed>
+
+scoring:
+  threshold_promote_to_hook: 0.6
+
+Rules:
+- detector.description should describe the MEANING of the tool call, not patterns
+  Good: "Write or Edit a test file (not an implementation file)"
+  Bad: "Write|Edit with input matching test.*\\.py"
+- Use before_step/after_step for skills where ORDER matters (e.g. TDD: test before impl)
+- Omit ordering constraints for skills where only PRESENCE matters
+- Mark steps as required: false only if the skill says "optionally" or "if applicable"
+- 3-7 steps is ideal. Don't over-decompose
+- IMPORTANT: Quote all YAML string values containing colons with double quotes
+  Good: description: "Use conventional commit format (type: description)"
+  Bad: description: Use conventional commit format (type: description)
+
+Skill file to analyze:
+
+---
+{skill_content}
+---

skills/skill-comply/pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "skill-comply"
+version = "0.1.0"
+description = "Automated skill compliance measurement for Claude Code"
+requires-python = ">=3.11"
+dependencies = ["pyyaml>=6.0"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.2",
+]

skills/skill-comply/scripts/classifier.py

@@ -0,0 +1,85 @@
+"""Classify tool calls against compliance steps using LLM."""
+
+from __future__ import annotations
+
+import json
+import logging
+import subprocess
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+from scripts.parser import ComplianceSpec, ObservationEvent
+
+PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
+
+
+def classify_events(
+    spec: ComplianceSpec,
+    trace: list[ObservationEvent],
+    model: str = "haiku",
+) -> dict[str, list[int]]:
+    """Classify which tool calls match which compliance steps.
+
+    Returns {step_id: [event_indices]} via a single LLM call.
+    """
+    if not trace:
+        return {}
+
+    steps_desc = "\n".join(
+        f"- {step.id}: {step.detector.description}"
+        for step in spec.steps
+    )
+
+    tool_calls = "\n".join(
+        f"[{i}] {event.tool}: input={event.input[:500]} output={event.output[:200]}"
+        for i, event in enumerate(trace)
+    )
+
+    prompt_template = (PROMPTS_DIR / "classifier.md").read_text()
+    prompt = (
+        prompt_template
+        .replace("{steps_description}", steps_desc)
+        .replace("{tool_calls}", tool_calls)
+    )
+
+    result = subprocess.run(
+        ["claude", "-p", prompt, "--model", model, "--output-format", "text"],
+        capture_output=True,
+        text=True,
+        timeout=60,
+    )
+
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"classifier subprocess failed (rc={result.returncode}): "
+            f"{result.stderr[:500]}"
+        )
+
+    return _parse_classification(result.stdout)
+
+
+def _parse_classification(text: str) -> dict[str, list[int]]:
+    """Parse LLM classification output into {step_id: [event_indices]}."""
+    text = text.strip()
+    # Strip markdown fences
+    lines = text.splitlines()
+    if lines and lines[0].startswith("```"):
+        lines = lines[1:]
+    if lines and lines[-1].startswith("```"):
+        lines = lines[:-1]
+    cleaned = "\n".join(lines)
+
+    try:
+        parsed = json.loads(cleaned)
+        if not isinstance(parsed, dict):
+            logger.warning("Classifier returned non-dict JSON: %s", type(parsed).__name__)
+            return {}
+        return {
+            k: [int(i) for i in v]
+            for k, v in parsed.items()
+            if isinstance(v, list)
+        }
+    except (json.JSONDecodeError, ValueError, TypeError) as e:
+        logger.warning("Failed to parse classification output: %s", e)
+        return {}

skills/skill-comply/scripts/grader.py

@@ -0,0 +1,122 @@
+"""Grade observation traces against compliance specs using LLM classification."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from scripts.classifier import classify_events
+from scripts.parser import ComplianceSpec, ObservationEvent, Step
+
+
+@dataclass(frozen=True)
+class StepResult:
+    step_id: str
+    detected: bool
+    evidence: tuple[ObservationEvent, ...]
+    failure_reason: str | None
+
+
+@dataclass(frozen=True)
+class ComplianceResult:
+    spec_id: str
+    steps: tuple[StepResult, ...]
+    compliance_rate: float
+    recommend_hook_promotion: bool
+    classification: dict[str, list[int]]
+
+
+def _check_temporal_order(
+    step: Step,
+    event: ObservationEvent,
+    resolved: dict[str, list[ObservationEvent]],
+    classified: dict[str, list[ObservationEvent]],
+) -> str | None:
+    """Check before_step/after_step constraints. Returns failure reason or None."""
+    if step.detector.after_step is not None:
+        after_events = resolved.get(step.detector.after_step, [])
+        if not after_events:
+            return f"after_step '{step.detector.after_step}' not yet detected"
+        latest_after = max(e.timestamp for e in after_events)
+        if event.timestamp <= latest_after:
+            return (
+                f"must occur after '{step.detector.after_step}' "
+                f"(last at {latest_after}), but found at {event.timestamp}"
+            )
+
+    if step.detector.before_step is not None:
+        # Look ahead using LLM classification results
+        before_events = resolved.get(step.detector.before_step)
+        if before_events is None:
+            before_events = classified.get(step.detector.before_step, [])
+        if before_events:
+            earliest_before = min(e.timestamp for e in before_events)
+            if event.timestamp >= earliest_before:
+                return (
+                    f"must occur before '{step.detector.before_step}' "
+                    f"(first at {earliest_before}), but found at {event.timestamp}"
+                )
+
+    return None
+
+
+def grade(
+    spec: ComplianceSpec,
+    trace: list[ObservationEvent],
+    classifier_model: str = "haiku",
+) -> ComplianceResult:
+    """Grade a trace against a compliance spec using LLM classification."""
+    sorted_trace = sorted(trace, key=lambda e: e.timestamp)
+
+    # Step 1: LLM classifies all events in one batch call
+    classification = classify_events(spec, sorted_trace, model=classifier_model)
+
+    # Convert indices to events
+    classified: dict[str, list[ObservationEvent]] = {
+        step_id: [sorted_trace[i] for i in indices if 0 <= i < len(sorted_trace)]
+        for step_id, indices in classification.items()
+    }
+
+    # Step 2: Check temporal ordering (deterministic)
+    resolved: dict[str, list[ObservationEvent]] = {}
+    step_results: list[StepResult] = []
+
+    for step in spec.steps:
+        candidates = classified.get(step.id, [])
+        matched: list[ObservationEvent] = []
+        failure_reason: str | None = None
+
+        for event in candidates:
+            temporal_fail = _check_temporal_order(step, event, resolved, classified)
+            if temporal_fail is None:
+                matched.append(event)
+                break
+            else:
+                failure_reason = temporal_fail
+
+        detected = len(matched) > 0
+        if detected:
+            resolved[step.id] = matched
+        elif failure_reason is None:
+            failure_reason = f"no matching event classified for step '{step.id}'"
+
+        step_results.append(StepResult(
+            step_id=step.id,
+            detected=detected,
+            evidence=tuple(matched),
+            failure_reason=failure_reason if not detected else None,
+        ))
+
+    required_ids = {s.id for s in spec.steps if s.required}
+    required_steps = [s for s in step_results if s.step_id in required_ids]
+    detected_required = sum(1 for s in required_steps if s.detected)
+    total_required = len(required_steps)
+
+    compliance_rate = detected_required / total_required if total_required > 0 else 0.0
+
+    return ComplianceResult(
+        spec_id=spec.id,
+        steps=tuple(step_results),
+        compliance_rate=compliance_rate,
+        recommend_hook_promotion=compliance_rate < spec.threshold_promote_to_hook,
+        classification=classification,
+    )

skills/skill-comply/scripts/parser.py

@@ -0,0 +1,107 @@
+"""Parse observation traces (JSONL) and compliance specs (YAML)."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+import yaml
+
+
+@dataclass(frozen=True)
+class ObservationEvent:
+    timestamp: str
+    event: str
+    tool: str
+    session: str
+    input: str
+    output: str
+
+
+@dataclass(frozen=True)
+class Detector:
+    description: str
+    after_step: str | None = None
+    before_step: str | None = None
+
+
+@dataclass(frozen=True)
+class Step:
+    id: str
+    description: str
+    required: bool
+    detector: Detector
+
+
+@dataclass(frozen=True)
+class ComplianceSpec:
+    id: str
+    name: str
+    source_rule: str
+    version: str
+    steps: tuple[Step, ...]
+    threshold_promote_to_hook: float
+
+
+def parse_trace(path: Path) -> list[ObservationEvent]:
+    """Parse a JSONL observation trace file into sorted events."""
+    if not path.is_file():
+        raise FileNotFoundError(f"Trace file not found: {path}")
+
+    text = path.read_text().strip()
+    if not text:
+        return []
+
+    events: list[ObservationEvent] = []
+    for i, line in enumerate(text.splitlines(), 1):
+        try:
+            raw = json.loads(line)
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON at line {i}: {e}") from e
+        try:
+            events.append(ObservationEvent(
+                timestamp=raw["timestamp"],
+                event=raw["event"],
+                tool=raw["tool"],
+                session=raw["session"],
+                input=raw.get("input", ""),
+                output=raw.get("output", ""),
+            ))
+        except KeyError as e:
+            raise ValueError(f"Missing required field {e} at line {i}") from e
+
+    return sorted(events, key=lambda e: e.timestamp)
+
+
+def parse_spec(path: Path) -> ComplianceSpec:
+    """Parse a YAML compliance spec file."""
+    if not path.is_file():
+        raise FileNotFoundError(f"Spec file not found: {path}")
+    raw = yaml.safe_load(path.read_text())
+
+    steps: list[Step] = []
+    for s in raw["steps"]:
+        d = s["detector"]
+        steps.append(Step(
+            id=s["id"],
+            description=s["description"],
+            required=s["required"],
+            detector=Detector(
+                description=d["description"],
+                after_step=d.get("after_step"),
+                before_step=d.get("before_step"),
+            ),
+        ))
+
+    if "scoring" not in raw:
+        raise KeyError("Missing 'scoring' section in compliance spec")
+
+    return ComplianceSpec(
+        id=raw["id"],
+        name=raw["name"],
+        source_rule=raw["source_rule"],
+        version=raw["version"],
+        steps=tuple(steps),
+        threshold_promote_to_hook=raw["scoring"]["threshold_promote_to_hook"],
+    )

skills/skill-comply/scripts/report.py

@@ -0,0 +1,170 @@
+"""Generate Markdown compliance reports."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from pathlib import Path
+
+from scripts.grader import ComplianceResult
+from scripts.parser import ComplianceSpec, ObservationEvent
+from scripts.scenario_generator import Scenario
+
+
+def generate_report(
+    skill_path: Path,
+    spec: ComplianceSpec,
+    results: list[tuple[str, ComplianceResult, list[ObservationEvent]]],
+    scenarios: list[Scenario] | None = None,
+) -> str:
+    """Generate a Markdown compliance report.
+
+    Args:
+        skill_path: Path to the skill file that was tested.
+        spec: The compliance spec used for grading.
+        results: List of (scenario_level_name, ComplianceResult, observations) tuples.
+        scenarios: Original scenario definitions with prompts.
+    """
+    now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+    overall = _overall_compliance(results)
+    threshold = spec.threshold_promote_to_hook
+
+    lines: list[str] = []
+    lines.append(f"# skill-comply Report: {skill_path.name}")
+    lines.append(f"Generated: {now}")
+    lines.append("")
+
+    # Summary
+    lines.append("## Summary")
+    lines.append("")
+    lines.append(f"| Metric | Value |")
+    lines.append(f"|--------|-------|")
+    lines.append(f"| Skill | `{skill_path}` |")
+    lines.append(f"| Spec | {spec.id} |")
+    lines.append(f"| Scenarios | {len(results)} |")
+    lines.append(f"| Overall Compliance | {overall:.0%} |")
+    lines.append(f"| Threshold | {threshold:.0%} |")
+
+    promote_steps = _steps_to_promote(spec, results, threshold)
+    if promote_steps:
+        step_names = ", ".join(promote_steps)
+        lines.append(f"| Recommendation | **Promote {step_names} to hooks** |")
+    else:
+        lines.append(f"| Recommendation | All steps above threshold — no hook promotion needed |")
+    lines.append("")
+
+    # Expected Behavioral Sequence
+    lines.append("## Expected Behavioral Sequence")
+    lines.append("")
+    lines.append("| # | Step | Required | Description |")
+    lines.append("|---|------|----------|-------------|")
+    for i, step in enumerate(spec.steps, 1):
+        req = "Yes" if step.required else "No"
+        lines.append(f"| {i} | {step.id} | {req} | {step.detector.description} |")
+    lines.append("")
+
+    # Scenario Results
+    lines.append("## Scenario Results")
+    lines.append("")
+    lines.append("| Scenario | Compliance | Failed Steps |")
+    lines.append("|----------|-----------|----------------|")
+    for level_name, result, _obs in results:
+        failed = [s.step_id for s in result.steps if not s.detected
+                  and any(sp.id == s.step_id and sp.required for sp in spec.steps)]
+        failed_str = ", ".join(failed) if failed else "—"
+        lines.append(f"| {level_name} | {result.compliance_rate:.0%} | {failed_str} |")
+    lines.append("")
+
+    # Scenario Prompts
+    if scenarios:
+        lines.append("## Scenario Prompts")
+        lines.append("")
+        for s in scenarios:
+            lines.append(f"### {s.level_name} (Level {s.level})")
+            lines.append("")
+            for prompt_line in s.prompt.splitlines():
+                lines.append(f"> {prompt_line}")
+            lines.append("")
+
+    # Hook Promotion Recommendations (optional/advanced)
+    if promote_steps:
+        lines.append("## Advanced: Hook Promotion Recommendations (optional)")
+        lines.append("")
+        for step_id in promote_steps:
+            rate = _step_compliance_rate(step_id, results)
+            step = next(s for s in spec.steps if s.id == step_id)
+            lines.append(
+                f"- **{step_id}** (compliance {rate:.0%}): {step.description}"
+            )
+        lines.append("")
+
+    # Per-scenario details with timeline
+    lines.append("## Detail")
+    lines.append("")
+    for level_name, result, observations in results:
+        lines.append(f"### {level_name} (Compliance: {result.compliance_rate:.0%})")
+        lines.append("")
+        lines.append("| Step | Required | Detected | Reason |")
+        lines.append("|------|----------|----------|--------|")
+        for sr in result.steps:
+            req = "Yes" if any(
+                sp.id == sr.step_id and sp.required for sp in spec.steps
+            ) else "No"
+            det = "YES" if sr.detected else "NO"
+            reason = sr.failure_reason or "—"
+            lines.append(f"| {sr.step_id} | {req} | {det} | {reason} |")
+        lines.append("")
+
+        # Timeline: show what the agent actually did
+        if observations:
+            # Build reverse index: event_index → step_id
+            index_to_step: dict[int, str] = {}
+            for step_id, indices in result.classification.items():
+                for idx in indices:
+                    index_to_step[idx] = step_id
+
+            lines.append(f"**Tool Call Timeline ({len(observations)} calls)**")
+            lines.append("")
+            lines.append("| # | Tool | Input | Output | Classified As |")
+            lines.append("|---|------|-------|--------|------|")
+            for i, obs in enumerate(observations):
+                step_label = index_to_step.get(i, "—")
+                input_summary = obs.input[:100].replace("|", "\\|").replace("\n", " ")
+                output_summary = obs.output[:50].replace("|", "\\|").replace("\n", " ")
+                lines.append(
+                    f"| {i} | {obs.tool} | {input_summary} | {output_summary} | {step_label} |"
+                )
+            lines.append("")
+
+    return "\n".join(lines)
+
+
+def _overall_compliance(results: list[tuple[str, ComplianceResult, list[ObservationEvent]]]) -> float:
+    if not results:
+        return 0.0
+    return sum(r.compliance_rate for _, r, _obs in results) / len(results)
+
+
+def _step_compliance_rate(
+    step_id: str,
+    results: list[tuple[str, ComplianceResult, list[ObservationEvent]]],
+) -> float:
+    detected = sum(
+        1 for _, r, _obs in results
+        for s in r.steps if s.step_id == step_id and s.detected
+    )
+    return detected / len(results) if results else 0.0
+
+
+def _steps_to_promote(
+    spec: ComplianceSpec,
+    results: list[tuple[str, ComplianceResult, list[ObservationEvent]]],
+    threshold: float,
+) -> list[str]:
+    promote = []
+    for step in spec.steps:
+        if not step.required:
+            continue
+        rate = _step_compliance_rate(step.id, results)
+        if rate < threshold:
+            promote.append(step.id)
+    return promote

skills/skill-comply/scripts/run.py

@@ -0,0 +1,127 @@
+"""CLI entry point for skill-comply."""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import sys
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from scripts.grader import grade
+from scripts.report import generate_report
+from scripts.runner import run_scenario
+from scripts.scenario_generator import generate_scenarios
+from scripts.spec_generator import generate_spec
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+    parser = argparse.ArgumentParser(
+        description="skill-comply: Measure skill compliance rates",
+    )
+    parser.add_argument(
+        "skill",
+        type=Path,
+        help="Path to skill/rule file to test",
+    )
+    parser.add_argument(
+        "--model",
+        default="sonnet",
+        help="Model for scenario execution (default: sonnet)",
+    )
+    parser.add_argument(
+        "--gen-model",
+        default="haiku",
+        help="Model for spec/scenario generation (default: haiku)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Generate spec and scenarios without executing",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=None,
+        help="Output report path (default: results/<skill-name>.md)",
+    )
+
+    args = parser.parse_args()
+
+    if not args.skill.is_file():
+        logger.error("Error: Skill file not found: %s", args.skill)
+        sys.exit(1)
+
+    results_dir = Path(__file__).parent.parent / "results"
+    results_dir.mkdir(exist_ok=True)
+
+    # Step 1: Generate compliance spec
+    logger.info("[1/4] Generating compliance spec from %s...", args.skill.name)
+    spec = generate_spec(args.skill, model=args.gen_model)
+    logger.info("       %d steps extracted", len(spec.steps))
+
+    # Step 2: Generate scenarios
+    spec_yaml = yaml.dump({
+        "steps": [
+            {"id": s.id, "description": s.description, "required": s.required}
+            for s in spec.steps
+        ]
+    })
+    logger.info("[2/4] Generating scenarios (3 prompt strictness levels)...")
+    scenarios = generate_scenarios(args.skill, spec_yaml, model=args.gen_model)
+    logger.info("       %d scenarios generated", len(scenarios))
+
+    for s in scenarios:
+        logger.info("       - %s: %s", s.level_name, s.description[:60])
+
+    if args.dry_run:
+        logger.info("\n[dry-run] Spec and scenarios generated. Skipping execution.")
+        logger.info("\nSpec: %s (%d steps)", spec.id, len(spec.steps))
+        for step in spec.steps:
+            marker = "*" if step.required else " "
+            logger.info("  [%s] %s: %s", marker, step.id, step.description)
+        return
+
+    # Step 3: Execute scenarios
+    logger.info("[3/4] Executing scenarios (model=%s)...", args.model)
+    graded_results: list[tuple[str, Any, list[Any]]] = []
+
+    for scenario in scenarios:
+        logger.info("       Running %s...", scenario.level_name)
+        run = run_scenario(scenario, model=args.model)
+        result = grade(spec, list(run.observations))
+        graded_results.append((scenario.level_name, result, list(run.observations)))
+        logger.info("       %s: %.0f%%", scenario.level_name, result.compliance_rate * 100)
+
+    # Step 4: Generate report
+    skill_name = args.skill.parent.name if args.skill.stem == "SKILL" else args.skill.stem
+    output_path = args.output or results_dir / f"{skill_name}.md"
+    logger.info("[4/4] Generating report...")
+
+    report = generate_report(args.skill, spec, graded_results, scenarios=scenarios)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(report)
+    logger.info("       Report saved to %s", output_path)
+
+    # Summary
+    if not graded_results:
+        logger.warning("No scenarios were executed.")
+        return
+    overall = sum(r.compliance_rate for _, r, _obs in graded_results) / len(graded_results)
+    logger.info("\n%s", "=" * 50)
+    logger.info("Overall Compliance: %.0f%%", overall * 100)
+    if overall < spec.threshold_promote_to_hook:
+        logger.info(
+            "Recommendation: Some steps have low compliance. "
+            "Consider promoting them to hooks. See the report for details."
+        )
+
+
+if __name__ == "__main__":
+    main()

skills/skill-comply/scripts/runner.py

@@ -0,0 +1,161 @@
+"""Run scenarios via claude -p and parse tool calls from stream-json output."""
+
+from __future__ import annotations
+
+import json
+import re
+import shlex
+import shutil
+import subprocess
+from dataclasses import dataclass
+from pathlib import Path
+
+from scripts.parser import ObservationEvent
+from scripts.scenario_generator import Scenario
+
+SANDBOX_BASE = Path("/tmp/skill-comply-sandbox")
+ALLOWED_MODELS = frozenset({"haiku", "sonnet", "opus"})
+
+
+@dataclass(frozen=True)
+class ScenarioRun:
+    scenario: Scenario
+    observations: tuple[ObservationEvent, ...]
+    sandbox_dir: Path
+
+
+def run_scenario(
+    scenario: Scenario,
+    model: str = "sonnet",
+    max_turns: int = 30,
+    timeout: int = 300,
+) -> ScenarioRun:
+    """Execute a scenario and extract tool calls from stream-json output."""
+    if model not in ALLOWED_MODELS:
+        raise ValueError(f"Unknown model: {model!r}. Allowed: {ALLOWED_MODELS}")
+
+    sandbox_dir = _safe_sandbox_dir(scenario.id)
+    _setup_sandbox(sandbox_dir, scenario)
+
+    result = subprocess.run(
+        [
+            "claude", "-p", scenario.prompt,
+            "--model", model,
+            "--max-turns", str(max_turns),
+            "--add-dir", str(sandbox_dir),
+            "--allowedTools", "Read,Write,Edit,Bash,Glob,Grep",
+            "--output-format", "stream-json",
+            "--verbose",
+        ],
+        capture_output=True,
+        text=True,
+        timeout=timeout,
+        cwd=sandbox_dir,
+    )
+
+    if result.returncode != 0:
+        raise RuntimeError(
+            f"claude -p failed (rc={result.returncode}): {result.stderr[:500]}"
+        )
+
+    observations = _parse_stream_json(result.stdout)
+
+    return ScenarioRun(
+        scenario=scenario,
+        observations=tuple(observations),
+        sandbox_dir=sandbox_dir,
+    )
+
+
+def _safe_sandbox_dir(scenario_id: str) -> Path:
+    """Sanitize scenario ID and ensure path stays within sandbox base."""
+    safe_id = re.sub(r"[^a-zA-Z0-9\-_]", "_", scenario_id)
+    path = SANDBOX_BASE / safe_id
+    # Validate path stays within sandbox base (raises ValueError on traversal)
+    path.resolve().relative_to(SANDBOX_BASE.resolve())
+    return path
+
+
+def _setup_sandbox(sandbox_dir: Path, scenario: Scenario) -> None:
+    """Create sandbox directory and run setup commands."""
+    if sandbox_dir.exists():
+        shutil.rmtree(sandbox_dir)
+    sandbox_dir.mkdir(parents=True)
+
+    subprocess.run(["git", "init"], cwd=sandbox_dir, capture_output=True)
+
+    for cmd in scenario.setup_commands:
+        parts = shlex.split(cmd)
+        subprocess.run(parts, cwd=sandbox_dir, capture_output=True)
+
+
+def _parse_stream_json(stdout: str) -> list[ObservationEvent]:
+    """Parse claude -p stream-json output into ObservationEvents.
+
+    Stream-json format:
+    - type=assistant with content[].type=tool_use → tool call (name, input)
+    - type=user with content[].type=tool_result → tool result (output)
+    """
+    events: list[ObservationEvent] = []
+    pending: dict[str, dict] = {}
+    event_counter = 0
+
+    for line in stdout.strip().splitlines():
+        try:
+            msg = json.loads(line)
+        except json.JSONDecodeError:
+            continue
+
+        msg_type = msg.get("type")
+
+        if msg_type == "assistant":
+            content = msg.get("message", {}).get("content", [])
+            for block in content:
+                if block.get("type") == "tool_use":
+                    tool_use_id = block.get("id", "")
+                    tool_input = block.get("input", {})
+                    input_str = (
+                        json.dumps(tool_input)[:5000]
+                        if isinstance(tool_input, dict)
+                        else str(tool_input)[:5000]
+                    )
+                    pending[tool_use_id] = {
+                        "tool": block.get("name", "unknown"),
+                        "input": input_str,
+                        "order": event_counter,
+                    }
+                    event_counter += 1
+
+        elif msg_type == "user":
+            content = msg.get("message", {}).get("content", [])
+            if isinstance(content, list):
+                for block in content:
+                    tool_use_id = block.get("tool_use_id", "")
+                    if tool_use_id in pending:
+                        info = pending.pop(tool_use_id)
+                        output_content = block.get("content", "")
+                        if isinstance(output_content, list):
+                            output_str = json.dumps(output_content)[:5000]
+                        else:
+                            output_str = str(output_content)[:5000]
+
+                        events.append(ObservationEvent(
+                            timestamp=f"T{info['order']:04d}",
+                            event="tool_complete",
+                            tool=info["tool"],
+                            session=msg.get("session_id", "unknown"),
+                            input=info["input"],
+                            output=output_str,
+                        ))
+
+    for _tool_use_id, info in pending.items():
+        events.append(ObservationEvent(
+            timestamp=f"T{info['order']:04d}",
+            event="tool_complete",
+            tool=info["tool"],
+            session="unknown",
+            input=info["input"],
+            output="",
+        ))
+
+    return sorted(events, key=lambda e: e.timestamp)

skills/skill-comply/scripts/scenario_generator.py

@@ -0,0 +1,70 @@
+"""Generate pressure scenarios from skill + spec using LLM."""
+
+from __future__ import annotations
+
+import subprocess
+from dataclasses import dataclass
+from pathlib import Path
+
+import yaml
+
+from scripts.utils import extract_yaml
+
+PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
+
+
+@dataclass(frozen=True)
+class Scenario:
+    id: str
+    level: int
+    level_name: str
+    description: str
+    prompt: str
+    setup_commands: tuple[str, ...]
+
+
+def generate_scenarios(
+    skill_path: Path,
+    spec_yaml: str,
+    model: str = "haiku",
+) -> list[Scenario]:
+    """Generate 3 scenarios with decreasing prompt strictness.
+
+    Calls claude -p with the scenario_generator prompt, parses YAML output.
+    """
+    skill_content = skill_path.read_text()
+    prompt_template = (PROMPTS_DIR / "scenario_generator.md").read_text()
+    prompt = (
+        prompt_template
+        .replace("{skill_content}", skill_content)
+        .replace("{spec_yaml}", spec_yaml)
+    )
+
+    result = subprocess.run(
+        ["claude", "-p", prompt, "--model", model, "--output-format", "text"],
+        capture_output=True,
+        text=True,
+        timeout=120,
+    )
+
+    if result.returncode != 0:
+        raise RuntimeError(f"claude -p failed: {result.stderr}")
+
+    if not result.stdout.strip():
+        raise RuntimeError("claude -p returned empty output")
+
+    raw_yaml = extract_yaml(result.stdout)
+    parsed = yaml.safe_load(raw_yaml)
+
+    scenarios: list[Scenario] = []
+    for s in parsed["scenarios"]:
+        scenarios.append(Scenario(
+            id=s["id"],
+            level=s["level"],
+            level_name=s["level_name"],
+            description=s["description"],
+            prompt=s["prompt"].strip(),
+            setup_commands=tuple(s.get("setup_commands", [])),
+        ))
+
+    return sorted(scenarios, key=lambda s: s.level)

skills/skill-comply/scripts/spec_generator.py

@@ -0,0 +1,72 @@
+"""Generate compliance specs from skill files using LLM."""
+
+from __future__ import annotations
+
+import subprocess
+import tempfile
+from pathlib import Path
+
+import yaml
+
+from scripts.parser import ComplianceSpec, parse_spec
+from scripts.utils import extract_yaml
+
+PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
+
+
+def generate_spec(
+    skill_path: Path,
+    model: str = "haiku",
+    max_retries: int = 2,
+) -> ComplianceSpec:
+    """Generate a compliance spec from a skill/rule file.
+
+    Calls claude -p with the spec_generator prompt, parses YAML output.
+    Retries on YAML parse errors with error feedback.
+    """
+    skill_content = skill_path.read_text()
+    prompt_template = (PROMPTS_DIR / "spec_generator.md").read_text()
+    base_prompt = prompt_template.replace("{skill_content}", skill_content)
+
+    last_error: Exception | None = None
+
+    for attempt in range(max_retries + 1):
+        prompt = base_prompt
+        if attempt > 0 and last_error is not None:
+            prompt += (
+                f"\n\nPREVIOUS ATTEMPT FAILED with YAML parse error:\n"
+                f"{last_error}\n\n"
+                f"Please fix the YAML. Remember to quote all string values "
+                f"that contain colons, e.g.: description: \"Use type: description format\""
+            )
+
+        result = subprocess.run(
+            ["claude", "-p", prompt, "--model", model, "--output-format", "text"],
+            capture_output=True,
+            text=True,
+            timeout=120,
+        )
+
+        if result.returncode != 0:
+            raise RuntimeError(f"claude -p failed: {result.stderr}")
+
+        raw_yaml = extract_yaml(result.stdout)
+
+        tmp_path = None
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".yaml", delete=False,
+        ) as f:
+            f.write(raw_yaml)
+            tmp_path = Path(f.name)
+
+        try:
+            return parse_spec(tmp_path)
+        except (yaml.YAMLError, KeyError, TypeError) as e:
+            last_error = e
+            if attempt == max_retries:
+                raise
+        finally:
+            if tmp_path is not None:
+                tmp_path.unlink(missing_ok=True)
+
+    raise RuntimeError("unreachable")

skills/skill-comply/scripts/utils.py

@@ -0,0 +1,13 @@
+"""Shared utilities for skill-comply scripts."""
+
+from __future__ import annotations
+
+
+def extract_yaml(text: str) -> str:
+    """Extract YAML from LLM output, stripping markdown fences if present."""
+    lines = text.strip().splitlines()
+    if lines and lines[0].startswith("```"):
+        lines = lines[1:]
+    if lines and lines[-1].startswith("```"):
+        lines = lines[:-1]
+    return "\n".join(lines)

skills/skill-comply/tests/test_grader.py

@@ -0,0 +1,137 @@
+"""Tests for grader module — compliance scoring with LLM classification."""
+
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from scripts.grader import ComplianceResult, StepResult, grade
+from scripts.parser import parse_spec, parse_trace
+
+FIXTURES = Path(__file__).parent.parent / "fixtures"
+
+
+@pytest.fixture
+def tdd_spec():
+    return parse_spec(FIXTURES / "tdd_spec.yaml")
+
+
+@pytest.fixture
+def compliant_trace():
+    return parse_trace(FIXTURES / "compliant_trace.jsonl")
+
+
+@pytest.fixture
+def noncompliant_trace():
+    return parse_trace(FIXTURES / "noncompliant_trace.jsonl")
+
+
+def _mock_compliant_classification(spec, trace, model="haiku"):  # noqa: ARG001
+    """Simulate LLM correctly classifying a compliant trace."""
+    return {
+        "write_test": [0],
+        "run_test_red": [1],
+        "write_impl": [2],
+        "run_test_green": [3],
+        "refactor": [4],
+    }
+
+
+def _mock_noncompliant_classification(spec, trace, model="haiku"):
+    """Simulate LLM classifying a noncompliant trace (impl before test)."""
+    return {
+        "write_impl": [0],    # src/fib.py written first
+        "write_test": [1],    # test written second
+        "run_test_green": [2],  # only a passing test run
+    }
+
+
+def _mock_empty_classification(spec, trace, model="haiku"):
+    return {}
+
+
+class TestGradeCompliant:
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_returns_compliance_result(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        assert isinstance(result, ComplianceResult)
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_full_compliance(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        assert result.compliance_rate == 1.0
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_all_required_steps_detected(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        required_results = [s for s in result.steps if s.step_id in
+                           ("write_test", "run_test_red", "write_impl", "run_test_green")]
+        assert all(s.detected for s in required_results)
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_optional_step_detected(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        refactor = next(s for s in result.steps if s.step_id == "refactor")
+        assert refactor.detected is True
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_no_hook_promotion_recommended(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        assert result.recommend_hook_promotion is False
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_step_evidence_not_empty(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        for step in result.steps:
+            if step.detected:
+                assert len(step.evidence) > 0
+
+
+class TestGradeNoncompliant:
+    @patch("scripts.grader.classify_events", side_effect=_mock_noncompliant_classification)
+    def test_low_compliance(self, mock_cls, tdd_spec, noncompliant_trace) -> None:
+        result = grade(tdd_spec, noncompliant_trace)
+        assert result.compliance_rate < 1.0
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_noncompliant_classification)
+    def test_write_test_fails_ordering(self, mock_cls, tdd_spec, noncompliant_trace) -> None:
+        """write_test has before_step=write_impl, but test is written AFTER impl."""
+        result = grade(tdd_spec, noncompliant_trace)
+        write_test = next(s for s in result.steps if s.step_id == "write_test")
+        assert write_test.detected is False
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_noncompliant_classification)
+    def test_run_test_red_not_detected(self, mock_cls, tdd_spec, noncompliant_trace) -> None:
+        result = grade(tdd_spec, noncompliant_trace)
+        run_red = next(s for s in result.steps if s.step_id == "run_test_red")
+        assert run_red.detected is False
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_noncompliant_classification)
+    def test_hook_promotion_recommended(self, mock_cls, tdd_spec, noncompliant_trace) -> None:
+        result = grade(tdd_spec, noncompliant_trace)
+        assert result.recommend_hook_promotion is True
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_noncompliant_classification)
+    def test_failure_reasons_present(self, mock_cls, tdd_spec, noncompliant_trace) -> None:
+        result = grade(tdd_spec, noncompliant_trace)
+        failed_steps = [s for s in result.steps if not s.detected and s.step_id != "refactor"]
+        for step in failed_steps:
+            assert step.failure_reason is not None
+
+
+class TestGradeEdgeCases:
+    @patch("scripts.grader.classify_events", side_effect=_mock_empty_classification)
+    def test_empty_trace(self, mock_cls, tdd_spec) -> None:
+        result = grade(tdd_spec, [])
+        assert result.compliance_rate == 0.0
+        assert result.recommend_hook_promotion is True
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_compliance_rate_is_ratio_of_required_only(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        assert result.compliance_rate == 1.0
+
+    @patch("scripts.grader.classify_events", side_effect=_mock_compliant_classification)
+    def test_spec_id_in_result(self, mock_cls, tdd_spec, compliant_trace) -> None:
+        result = grade(tdd_spec, compliant_trace)
+        assert result.spec_id == "tdd-workflow"

skills/skill-comply/tests/test_parser.py

@@ -0,0 +1,90 @@
+"""Tests for parser module — JSONL trace and YAML spec parsing."""
+
+from pathlib import Path
+
+import pytest
+
+from scripts.parser import (
+    ComplianceSpec,
+    Detector,
+    ObservationEvent,
+    Step,
+    parse_spec,
+    parse_trace,
+)
+
+FIXTURES = Path(__file__).parent.parent / "fixtures"
+
+
+class TestParseTrace:
+    def test_parses_compliant_trace(self) -> None:
+        events = parse_trace(FIXTURES / "compliant_trace.jsonl")
+        assert len(events) == 5
+        assert all(isinstance(e, ObservationEvent) for e in events)
+
+    def test_events_sorted_by_timestamp(self) -> None:
+        events = parse_trace(FIXTURES / "compliant_trace.jsonl")
+        timestamps = [e.timestamp for e in events]
+        assert timestamps == sorted(timestamps)
+
+    def test_event_fields(self) -> None:
+        events = parse_trace(FIXTURES / "compliant_trace.jsonl")
+        first = events[0]
+        assert first.tool == "Write"
+        assert first.session == "sess-001"
+        assert "test_fib.py" in first.input
+        assert first.output == "File created"
+
+    def test_parses_noncompliant_trace(self) -> None:
+        events = parse_trace(FIXTURES / "noncompliant_trace.jsonl")
+        assert len(events) == 3
+        assert "src/fib.py" in events[0].input
+
+    def test_empty_file_returns_empty_list(self, tmp_path: Path) -> None:
+        empty = tmp_path / "empty.jsonl"
+        empty.write_text("")
+        events = parse_trace(empty)
+        assert events == []
+
+    def test_nonexistent_file_raises(self) -> None:
+        with pytest.raises(FileNotFoundError):
+            parse_trace(Path("/nonexistent/trace.jsonl"))
+
+
+class TestParseSpec:
+    def test_parses_tdd_spec(self) -> None:
+        spec = parse_spec(FIXTURES / "tdd_spec.yaml")
+        assert isinstance(spec, ComplianceSpec)
+        assert spec.id == "tdd-workflow"
+        assert len(spec.steps) == 5
+
+    def test_step_fields(self) -> None:
+        spec = parse_spec(FIXTURES / "tdd_spec.yaml")
+        first = spec.steps[0]
+        assert isinstance(first, Step)
+        assert first.id == "write_test"
+        assert first.required is True
+        assert isinstance(first.detector, Detector)
+        assert "test file" in first.detector.description
+        assert first.detector.before_step == "write_impl"
+
+    def test_optional_detector_fields(self) -> None:
+        spec = parse_spec(FIXTURES / "tdd_spec.yaml")
+        write_test = spec.steps[0]
+        assert write_test.detector.after_step is None
+
+        run_test_red = spec.steps[1]
+        assert run_test_red.detector.after_step == "write_test"
+        assert run_test_red.detector.before_step == "write_impl"
+
+    def test_scoring_threshold(self) -> None:
+        spec = parse_spec(FIXTURES / "tdd_spec.yaml")
+        assert spec.threshold_promote_to_hook == 0.6
+
+    def test_required_vs_optional_steps(self) -> None:
+        spec = parse_spec(FIXTURES / "tdd_spec.yaml")
+        required = [s for s in spec.steps if s.required]
+        optional = [s for s in spec.steps if not s.required]
+        assert len(required) == 4
+        assert len(optional) == 1
+        assert optional[0].id == "refactor"