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>

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 {}