julian
17c08e6392
- Nx 22.7 monorepo (pnpm 11.1, TypeScript 5.9, Node 24) - apps/api: NestJS 11 (CJS conforme CODING-RULES.md PGD-DB-004) - apps/web: React 19 + Vite 8 (ESM) - libs/shared/api-interface: Zod contract base - Docker Compose dev: Postgres 18, Valkey 8, MinIO, Mailpit - WDS artifacts: - design-artifacts/A-Product-Brief/ (5 docs canônicos + 16 dialogs) - design-artifacts/B-Trigger-Map/ (hub + 4 personas + feature impact) - Stack canon: STACK.md v2.2 + CODING-RULES.md v2.0 + brand.md - AGENTS.md + README.md como entrada para devs/agentes Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
367 lines
13 KiB
Python
367 lines
13 KiB
Python
#!/usr/bin/env python3
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# /// script
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# requires-python = ">=3.9"
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# ///
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"""Run trigger evals: does the skill's description fire on each query?
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Adapted from Anthropic skill-creator's run_eval.py
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(https://github.com/anthropics/skills/tree/main/skills/skill-creator) with two
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adaptations:
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1. Isolation. Each query runs in either a fresh Docker container off
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bmad-eval-runner:latest, or a fresh local tmp dir under ~/bmad-evals/<run-id>/
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with HOME overridden to a clean directory. This prevents the host's global
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CLAUDE.md and auto-memory from biasing whether the skill fires.
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2. Output. Results are written to a run folder alongside the artifact eval
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run-folder layout (so triggers and artifacts can share a single report).
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Usage:
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python3 run_triggers.py \\
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--skill-path PATH \\
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--triggers-file PATH/triggers.json \\
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--output-dir PATH \\
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--isolation docker|local \\
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[--workers N] [--runs-per-query N] [--timeout SECS] [--threshold 0.5]
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import shutil
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import subprocess
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import sys
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import time
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import uuid
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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SCRIPT_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(SCRIPT_DIR))
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from utils import ( # noqa: E402
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new_run_id,
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parse_skill_md,
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read_json,
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read_macos_keychain_credentials,
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stage_credentials,
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utc_now_iso,
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write_json,
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)
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DOCKER_IMAGE = "bmad-eval-runner:latest"
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_KEYCHAIN_CREDS: str | None = read_macos_keychain_credentials()
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def write_synthetic_skill(skills_dir: Path, skill_name: str, description: str, unique_id: str) -> tuple[Path, str]:
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"""Place a synthetic skill at <skills_dir>/<clean_name>/SKILL.md.
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The Skill tool only fires for entries discovered as actual skills (frontmatter
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`name` + `description` under a `.claude/skills/<name>/SKILL.md`). Slash-commands
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under `.claude/commands/` do not auto-invoke the Skill tool, so the previous
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implementation could never observe a positive trigger. This places the synthetic
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skill where Claude Code looks for skills, with a unique name so the detector
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can disambiguate it from any pre-existing skill of the same display name.
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"""
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clean_name = f"{skill_name}-skill-{unique_id}"
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skill_root = skills_dir / clean_name
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skill_root.mkdir(parents=True, exist_ok=True)
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path = skill_root / "SKILL.md"
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indented_desc = "\n ".join(description.split("\n"))
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path.write_text(
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f"---\n"
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f"name: {clean_name}\n"
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f"description: |\n"
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f" {indented_desc}\n"
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f"---\n\n"
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f"# {skill_name}\n\n"
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f"This skill handles: {description}\n",
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encoding="utf-8",
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)
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return path, clean_name
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def parse_stream_for_trigger(buffer: str, clean_name: str) -> tuple[bool | None, str]:
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"""Return (triggered_or_none, leftover_buffer). None means undecided yet."""
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triggered: bool | None = None
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pending_tool: str | None = None
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accumulated_json = ""
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leftover = ""
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while "\n" in buffer:
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line, buffer = buffer.split("\n", 1)
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line = line.strip()
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if not line:
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continue
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try:
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evt = json.loads(line)
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except json.JSONDecodeError:
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continue
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if evt.get("type") == "stream_event":
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se = evt.get("event", {})
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t = se.get("type", "")
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if t == "content_block_start":
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cb = se.get("content_block", {})
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if cb.get("type") == "tool_use":
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name = cb.get("name", "")
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if name in ("Skill", "Read"):
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pending_tool = name
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accumulated_json = ""
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else:
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return False, ""
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elif t == "content_block_delta" and pending_tool:
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delta = se.get("delta", {})
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if delta.get("type") == "input_json_delta":
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accumulated_json += delta.get("partial_json", "")
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if clean_name in accumulated_json:
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return True, ""
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elif t in ("content_block_stop", "message_stop"):
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if pending_tool:
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return clean_name in accumulated_json, ""
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if t == "message_stop":
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return False, ""
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elif evt.get("type") == "assistant":
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for item in evt.get("message", {}).get("content", []):
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if item.get("type") != "tool_use":
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continue
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tname = item.get("name", "")
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tinput = item.get("input", {})
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if tname == "Skill" and clean_name in tinput.get("skill", ""):
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return True, ""
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if tname == "Read" and clean_name in tinput.get("file_path", ""):
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return True, ""
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return False, ""
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elif evt.get("type") == "result":
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return triggered if triggered is not None else False, ""
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leftover = buffer
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return triggered, leftover
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def run_query_local(query: str, skill_name: str, description: str,
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workspace_root: Path, timeout: int) -> bool:
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workspace_root.mkdir(parents=True, exist_ok=True)
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home_dir = workspace_root / ".home"
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(home_dir / ".claude").mkdir(parents=True, exist_ok=True)
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stage_credentials(home_dir / ".claude", _KEYCHAIN_CREDS)
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project_dir = workspace_root / "project"
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skills_dir = project_dir / ".claude" / "skills"
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project_dir.mkdir(parents=True, exist_ok=True)
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unique = uuid.uuid4().hex[:8]
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cmd_file, clean_name = write_synthetic_skill(skills_dir, skill_name, description, unique)
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env = {
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"HOME": str(home_dir),
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"CLAUDE_CONFIG_DIR": str(home_dir / ".claude"),
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"PATH": os.environ.get("PATH", ""),
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"ANTHROPIC_API_KEY": os.environ.get("ANTHROPIC_API_KEY", ""),
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}
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cmd = [
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"claude", "-p", query,
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"--output-format", "stream-json",
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"--verbose",
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"--include-partial-messages",
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"--dangerously-skip-permissions",
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]
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try:
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proc = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.DEVNULL,
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cwd=str(project_dir),
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env=env,
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)
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buffer = ""
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triggered: bool | None = None
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start = time.time()
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try:
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while time.time() - start < timeout:
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if proc.poll() is not None:
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rest = proc.stdout.read()
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if rest:
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buffer += rest.decode("utf-8", errors="replace")
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break
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chunk = proc.stdout.read1(8192) if hasattr(proc.stdout, "read1") else proc.stdout.read(8192)
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if not chunk:
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time.sleep(0.05)
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continue
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buffer += chunk.decode("utf-8", errors="replace")
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decided, buffer = parse_stream_for_trigger(buffer, clean_name)
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if decided is not None:
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triggered = decided
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break
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finally:
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if proc.poll() is None:
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proc.kill()
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proc.wait()
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if triggered is None:
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decided, _ = parse_stream_for_trigger(buffer + "\n", clean_name)
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triggered = bool(decided)
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return bool(triggered)
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finally:
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try:
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shutil.rmtree(cmd_file.parent, ignore_errors=True)
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except OSError:
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pass
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def run_query_docker(query: str, skill_name: str, description: str,
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workspace_root: Path, timeout: int) -> bool:
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workspace_root.mkdir(parents=True, exist_ok=True)
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unique = uuid.uuid4().hex[:8]
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skills_in = workspace_root / "skills_in"
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skills_in.mkdir(parents=True, exist_ok=True)
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_, clean_name = write_synthetic_skill(skills_in, skill_name, description, unique)
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creds_dir: Path | None = None
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if _KEYCHAIN_CREDS:
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creds_dir = workspace_root / "creds_in"
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creds_dir.mkdir(parents=True, exist_ok=True)
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(creds_dir / ".credentials.json").write_text(_KEYCHAIN_CREDS, encoding="utf-8")
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container_script = f"""
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set -e
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mkdir -p /workspace/.claude/skills
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cp -R /skills/. /workspace/.claude/skills/ 2>/dev/null || true
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if [ -f /creds/.credentials.json ]; then
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mkdir -p /home/evaluator/.claude
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cp /creds/.credentials.json /home/evaluator/.claude/.credentials.json
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fi
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cd /workspace
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claude -p "$EVAL_QUERY" \\
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--output-format stream-json --verbose --include-partial-messages \\
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--dangerously-skip-permissions \\
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> /output/stream.jsonl 2>/dev/null || true
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"""
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output_dir = workspace_root / "output"
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output_dir.mkdir(parents=True, exist_ok=True)
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cmd = [
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"docker", "run", "--rm",
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"-v", f"{skills_in}:/skills:ro",
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"-v", f"{output_dir}:/output",
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"-e", "ANTHROPIC_API_KEY",
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"-e", f"EVAL_QUERY={query}",
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]
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if creds_dir:
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cmd += ["-v", f"{creds_dir}:/creds:ro"]
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cmd += [DOCKER_IMAGE, "bash", "-c", container_script]
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try:
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subprocess.run(cmd, capture_output=True, timeout=timeout + 30)
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except subprocess.TimeoutExpired:
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pass
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stream_file = output_dir / "stream.jsonl"
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if not stream_file.is_file():
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return False
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decided, _ = parse_stream_for_trigger(stream_file.read_text(encoding="utf-8", errors="replace") + "\n", clean_name)
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return bool(decided)
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def main() -> int:
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parser = argparse.ArgumentParser(description="Run trigger evals in isolation")
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parser.add_argument("--skill-path", required=True, type=Path)
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parser.add_argument("--triggers-file", required=True, type=Path)
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parser.add_argument("--output-dir", required=True, type=Path)
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parser.add_argument("--isolation", choices=("docker", "local"), required=True)
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parser.add_argument("--workers", type=int, default=8)
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parser.add_argument("--runs-per-query", type=int, default=3)
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parser.add_argument("--timeout", type=int, default=45)
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parser.add_argument("--threshold", type=float, default=0.5)
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parser.add_argument("--quiet", action="store_true")
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args = parser.parse_args()
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skill_path = args.skill_path.resolve()
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triggers_file = args.triggers_file.resolve()
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if not triggers_file.is_file():
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print(f"triggers file not found: {triggers_file}", file=sys.stderr)
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return 2
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skill_name, description, _ = parse_skill_md(skill_path)
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queries = read_json(triggers_file)
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run_id = new_run_id(f"{skill_name}-triggers")
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run_dir = (args.output_dir / run_id).resolve()
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(run_dir / "queries").mkdir(parents=True, exist_ok=True)
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write_json(run_dir / "run.json", {
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"run_id": run_id,
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"skill_name": skill_name,
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"description": description,
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"isolation": args.isolation,
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"started_at": utc_now_iso(),
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"query_count": len(queries),
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"runs_per_query": args.runs_per_query,
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"threshold": args.threshold,
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})
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runner = run_query_docker if args.isolation == "docker" else run_query_local
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def run_one(idx: int, q: dict, run_idx: int) -> tuple[int, bool]:
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ws = run_dir / "queries" / f"q{idx:03d}-r{run_idx}"
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triggered = runner(q["query"], skill_name, description, ws, args.timeout)
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return idx, triggered
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per_query: dict[int, list[bool]] = {}
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if not args.quiet:
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print(f"[run_triggers] {len(queries)} queries × {args.runs_per_query} runs, isolation={args.isolation}", file=sys.stderr)
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with ThreadPoolExecutor(max_workers=args.workers) as pool:
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futures = []
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for idx, q in enumerate(queries):
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for run_idx in range(args.runs_per_query):
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futures.append(pool.submit(run_one, idx, q, run_idx))
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for fut in as_completed(futures):
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try:
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idx, triggered = fut.result()
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except Exception as e:
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print(f"Warning: query failed: {e}", file=sys.stderr)
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continue
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per_query.setdefault(idx, []).append(triggered)
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results = []
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for idx, q in enumerate(queries):
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triggers = per_query.get(idx, [])
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rate = (sum(triggers) / len(triggers)) if triggers else 0.0
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should = bool(q["should_trigger"])
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if should:
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passed = rate >= args.threshold
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else:
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passed = rate < args.threshold
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results.append({
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"query": q["query"],
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"should_trigger": should,
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"trigger_rate": rate,
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"triggers": int(sum(triggers)),
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"runs": len(triggers),
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"pass": passed,
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})
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output = {
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"run_id": run_id,
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"completed_at": utc_now_iso(),
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"skill_name": skill_name,
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"description": description,
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"isolation": args.isolation,
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"results": results,
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"summary": {
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"total": len(results),
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"passed": sum(1 for r in results if r["pass"]),
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"failed": sum(1 for r in results if not r["pass"]),
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},
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}
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write_json(run_dir / "triggers-result.json", output)
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print(json.dumps(output, indent=2))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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