463 lines
17 KiB
Python
463 lines
17 KiB
Python
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#!/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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"""Trigger evals: does a skill's description fire on each near-miss query?
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A trigger query is a should/should-not user message that shares keywords with
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the skill so the description has to discriminate. For each query the runner
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stages a synthetic skill where the runtime looks for skills, sends the query
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through the adapter, and detects whether the skill loaded. Each query runs
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several times (runs-per-query) so the trigger rate is stable, not a coin flip.
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Detection lives behind the adapter. "Did the skill load" is a runtime-specific
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signal, so the adapter declares how skills are staged and how a load shows up in
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the transcript. The adapter config (see references/platform-adapter.md) adds two
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trigger-specific keys to the core ones:
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invocation : argv template; "{prompt}" (or "{query}") is replaced with the
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query text, "{cwd}" with the staging dir.
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auth_env : auth env-var name, forwarded only when set non-empty on the
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host. No model id.
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skill_dir : path under the staging cwd where a skill is discovered, e.g.
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".claude/skills". The runner writes the synthetic skill there.
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load_signal: which tool_use events count as a load:
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{"skill_tool": "Skill", "read_tool": "Read"} (defaults)
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A load is a tool_use of skill_tool whose input names the
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synthetic skill, or a read_tool whose file_path falls inside
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the synthetic skill's directory. Whole-transcript substring
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matching is NOT supported: the runtime's init event lists
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every discovered skill, so a substring match reports 100%
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trigger rate regardless of the description.
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Each query runs in a built-from-scratch environment (PATH, fresh empty HOME,
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CLAUDE_CONFIG_DIR inside it, auth var only when set, adapter env_passthrough
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keys) so the host's installed skills, memory, and config cannot bias firing.
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If no adapter is configured the runner degrades gracefully: it stages each query
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and records "skipped: no runtime adapter configured" rather than crashing.
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Usage:
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python3 run_triggers.py \\
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--skill-path SKILL_DIR \\
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--queries QUERIES.json \\
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--output-dir DIR \\
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[--adapter ADAPTER.json] \\
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[--runs-per-query N] [--threshold 0.5] [--timeout SECS] \\
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[--workers N] [--quiet]
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QUERIES.json is a list of {"query": "...", "should_trigger": true|false}.
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SKILL_DIR contains the SKILL.md whose name + description are under test; the
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description is what the synthetic skill advertises.
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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 re
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import shutil
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import subprocess
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import sys
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import uuid
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime, timezone
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from pathlib import Path
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# --- self-contained helpers -------------------------------------------------
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def utc_now_iso() -> str:
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return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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def new_run_id(label: str) -> str:
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return f"{datetime.now().strftime('%Y%m%d-%H%M%S')}-{label}"
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def write_json(path: Path, data: object) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(json.dumps(data, indent=2) + "\n", encoding="utf-8")
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def read_json(path: Path) -> object:
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return json.loads(path.read_text(encoding="utf-8"))
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def parse_skill_md(skill_path: Path) -> tuple[str, str]:
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"""Return (name, description) from SKILL.md frontmatter."""
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text = (skill_path / "SKILL.md").read_text(encoding="utf-8")
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m = re.match(r"^---\s*\n(.*?)\n---\s*\n", text, re.DOTALL)
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if not m:
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raise ValueError(f"SKILL.md at {skill_path} is missing frontmatter")
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frontmatter = m.group(1)
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name = None
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desc_lines: list[str] = []
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in_desc = False
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for line in frontmatter.splitlines():
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if line.startswith("name:"):
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name = line.split(":", 1)[1].strip()
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in_desc = False
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elif line.startswith("description:"):
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value = line.split(":", 1)[1].strip()
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if value in ("|", ">"):
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in_desc = True
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else:
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desc_lines = [value]
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in_desc = False
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elif in_desc and line.startswith((" ", "\t")):
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desc_lines.append(line.strip())
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elif in_desc:
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in_desc = False
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if not name:
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raise ValueError(f"SKILL.md at {skill_path} has no name")
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return name, " ".join(desc_lines).strip()
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# --- adapter ----------------------------------------------------------------
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def find_adapter(explicit: Path | None, queries_file: Path) -> Path | None:
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if explicit is not None:
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return explicit if explicit.is_file() else None
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env_path = os.environ.get("BMAD_EVAL_ADAPTER")
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if env_path and Path(env_path).is_file():
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return Path(env_path)
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for candidate in (
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queries_file.parent / "adapter.json",
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queries_file.parent / ".bmad-eval-adapter.json",
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):
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if candidate.is_file():
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return candidate
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return None
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def load_adapter(path: Path) -> dict:
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cfg = read_json(path)
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if not isinstance(cfg, dict) or "invocation" not in cfg:
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raise ValueError(f"adapter config missing 'invocation': {path}")
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return cfg
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def build_argv(invocation: list, query: str, cwd: str) -> list[str]:
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out: list[str] = []
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for tok in invocation:
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tok = (str(tok).replace("{prompt}", query)
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.replace("{query}", query)
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.replace("{cwd}", cwd))
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out.append(tok)
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return out
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def build_case_env(adapter: dict | None, home_dir: Path,
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host_env: dict) -> dict[str, str]:
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"""Build the subprocess environment from scratch — never from os.environ.
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Inheriting the host env would leak shell config, tokens, and runtime
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state into the clean room. The env holds exactly: PATH, a fresh HOME,
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CLAUDE_CONFIG_DIR inside it, the adapter's auth var ONLY when set
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non-empty in the host (an empty-string auth var breaks the runtime's own
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credential fallback), and any adapter env_passthrough keys present in
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the host env.
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"""
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adapter = adapter or {}
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env = {
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"PATH": host_env.get("PATH", ""),
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"HOME": str(home_dir),
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"CLAUDE_CONFIG_DIR": str(home_dir / ".claude"),
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}
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auth_env = adapter.get("auth_env")
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if auth_env:
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val = host_env.get(str(auth_env))
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if val:
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env[str(auth_env)] = val
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for key in adapter.get("env_passthrough") or []:
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val = host_env.get(str(key))
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if val is not None:
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env[str(key)] = val
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return env
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# --- synthetic skill staging ------------------------------------------------
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def write_synthetic_skill(skills_dir: Path, skill_name: str,
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description: str, unique: str) -> str:
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"""Write a synthetic skill the runtime can discover. Returns its unique name.
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A unique suffix lets the detector tell this synthetic skill apart from any
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real skill of the same display name.
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"""
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clean_name = f"{skill_name}-trig-{unique}"
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root = skills_dir / clean_name
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root.mkdir(parents=True, exist_ok=True)
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indented = "\n ".join(description.split("\n"))
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(root / "SKILL.md").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}\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 clean_name
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# --- load detection (behind the adapter) ------------------------------------
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def validate_load_signal(load_signal: dict | None) -> None:
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"""Reject substring-style load signals before any query runs."""
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if (load_signal or {}).get("type") == "string":
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raise ValueError(
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"load_signal type 'string' is not supported: the runtime's init "
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"event lists every discovered skill, so a whole-transcript "
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"substring match reports 100% trigger rate regardless of the "
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"description. Use tool-call detection "
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'({"skill_tool": ..., "read_tool": ...}).'
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)
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def detect_load(transcript_text: str, load_signal: dict, clean_name: str) -> bool:
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"""Did the synthetic skill load? Only tool_use events count.
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The init event of a stream-json transcript lists every discovered skill
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by name, so the name appearing somewhere in the transcript proves
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nothing. A load is a skill-invocation tool call naming the synthetic
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skill, or a read of a file inside the synthetic skill's directory (its
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SKILL.md) — the two ways a runtime actually pulls a skill into context.
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"""
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validate_load_signal(load_signal)
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sig = load_signal or {}
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skill_tool = sig.get("skill_tool", "Skill")
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read_tool = sig.get("read_tool", "Read")
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for raw in transcript_text.splitlines():
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raw = raw.strip()
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if not raw:
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continue
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try:
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evt = json.loads(raw)
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except json.JSONDecodeError:
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continue
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if not isinstance(evt, dict) or evt.get("type") != "assistant":
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continue
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msg = evt.get("message", {})
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content = msg.get("content", []) if isinstance(msg, dict) else []
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for item in content:
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if not isinstance(item, dict) or item.get("type") != "tool_use":
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continue
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name = item.get("name")
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inp = item.get("input", {})
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if not isinstance(inp, dict):
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inp = {}
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if name == skill_tool and clean_name in json.dumps(inp):
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return True
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if name == read_tool and clean_name in str(inp.get("file_path", "")):
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return True
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return False
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# --- per-query execution ----------------------------------------------------
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def run_query_once(query: str, skill_name: str, description: str,
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adapter: dict, stage_dir: Path, timeout: int) -> bool:
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skill_subdir = adapter.get("skill_dir", ".claude/skills")
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skills_dir = stage_dir / skill_subdir
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skills_dir.mkdir(parents=True, exist_ok=True)
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unique = uuid.uuid4().hex[:8]
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clean_name = write_synthetic_skill(skills_dir, skill_name, description, unique)
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home_dir = stage_dir / ".home"
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(home_dir / ".claude").mkdir(parents=True, exist_ok=True)
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env = build_case_env(adapter, home_dir, dict(os.environ))
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argv = build_argv(adapter["invocation"], query, str(stage_dir))
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try:
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proc = subprocess.run(
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argv,
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stdout=subprocess.PIPE,
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stderr=subprocess.DEVNULL,
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cwd=str(stage_dir),
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env=env,
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timeout=timeout,
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)
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captured = proc.stdout or b""
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except subprocess.TimeoutExpired as e:
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captured = e.stdout or b""
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except FileNotFoundError:
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# invocation command absent; treat as undetected and let caller note it
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raise
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transcript_cfg = adapter.get("transcript", {"format": "stdout-jsonl"})
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if transcript_cfg.get("format") == "file":
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f = stage_dir / transcript_cfg.get("path", "transcript.jsonl")
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text = f.read_text(encoding="utf-8", errors="replace") if f.is_file() else ""
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else:
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text = captured.decode("utf-8", errors="replace")
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return detect_load(text, adapter.get("load_signal", {}), clean_name)
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# --- main -------------------------------------------------------------------
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def main(argv: list[str] | None = None) -> int:
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p = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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p.add_argument("--skill-path", required=True, type=Path)
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p.add_argument("--queries", required=True, type=Path)
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p.add_argument("--output-dir", required=True, type=Path)
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p.add_argument("--adapter", type=Path, default=None)
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p.add_argument("--runs-per-query", type=int, default=3)
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p.add_argument("--threshold", type=float, default=0.5)
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p.add_argument("--timeout", type=int, default=60)
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p.add_argument("--workers", type=int, default=4)
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p.add_argument("--quiet", action="store_true")
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args = p.parse_args(argv)
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skill_path = args.skill_path.resolve()
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queries_file = args.queries.resolve()
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if not queries_file.is_file():
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print(f"queries file not found: {queries_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(queries_file)
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if not isinstance(queries, list):
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print("queries file must be a JSON list", file=sys.stderr)
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return 2
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adapter_path = find_adapter(args.adapter, queries_file)
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adapter: dict | None = None
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adapter_note = "none"
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if adapter_path is not None:
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try:
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adapter = load_adapter(adapter_path)
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validate_load_signal(adapter.get("load_signal"))
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adapter_note = str(adapter_path)
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except Exception as e:
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print(f"adapter config invalid ({e}); degrading to skip-only",
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file=sys.stderr)
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adapter = None
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adapter_note = f"invalid: {e}"
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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,
|
||
|
|
"description": description,
|
||
|
|
"adapter": adapter_note,
|
||
|
|
"started_at": utc_now_iso(),
|
||
|
|
"query_count": len(queries),
|
||
|
|
"runs_per_query": args.runs_per_query,
|
||
|
|
"threshold": args.threshold,
|
||
|
|
})
|
||
|
|
|
||
|
|
if adapter is None:
|
||
|
|
if not args.quiet:
|
||
|
|
print("[run_triggers] no runtime adapter configured; staging only "
|
||
|
|
"(no crash).", file=sys.stderr)
|
||
|
|
output = {
|
||
|
|
"run_id": run_id,
|
||
|
|
"completed_at": utc_now_iso(),
|
||
|
|
"skill_name": skill_name,
|
||
|
|
"description": description,
|
||
|
|
"status": "skipped",
|
||
|
|
"reason": "no runtime adapter configured",
|
||
|
|
"results": [],
|
||
|
|
"summary": {"total": len(queries), "passed": 0, "failed": 0,
|
||
|
|
"skipped": len(queries)},
|
||
|
|
}
|
||
|
|
write_json(run_dir / "triggers-result.json", output)
|
||
|
|
print(json.dumps(output, indent=2))
|
||
|
|
return 0
|
||
|
|
|
||
|
|
adapter_missing = {"flag": False}
|
||
|
|
|
||
|
|
def run_one(idx: int, q: dict, run_idx: int) -> tuple[int, bool]:
|
||
|
|
stage = run_dir / "queries" / f"q{idx:03d}-r{run_idx}"
|
||
|
|
stage.mkdir(parents=True, exist_ok=True)
|
||
|
|
try:
|
||
|
|
triggered = run_query_once(
|
||
|
|
q["query"], skill_name, description, adapter, stage, args.timeout)
|
||
|
|
except FileNotFoundError:
|
||
|
|
adapter_missing["flag"] = True
|
||
|
|
triggered = False
|
||
|
|
finally:
|
||
|
|
shutil.rmtree(stage / adapter.get("skill_dir", ".claude/skills").split("/")[0],
|
||
|
|
ignore_errors=True)
|
||
|
|
return idx, triggered
|
||
|
|
|
||
|
|
per_query: dict[int, list[bool]] = {}
|
||
|
|
if not args.quiet:
|
||
|
|
print(f"[run_triggers] {len(queries)} queries x {args.runs_per_query} "
|
||
|
|
f"runs", file=sys.stderr)
|
||
|
|
|
||
|
|
with ThreadPoolExecutor(max_workers=max(1, args.workers)) as pool:
|
||
|
|
futures = []
|
||
|
|
for idx, q in enumerate(queries):
|
||
|
|
for run_idx in range(args.runs_per_query):
|
||
|
|
futures.append(pool.submit(run_one, idx, q, run_idx))
|
||
|
|
for fut in as_completed(futures):
|
||
|
|
try:
|
||
|
|
idx, triggered = fut.result()
|
||
|
|
except Exception as e:
|
||
|
|
print(f"Warning: query run failed: {e}", file=sys.stderr)
|
||
|
|
continue
|
||
|
|
per_query.setdefault(idx, []).append(triggered)
|
||
|
|
|
||
|
|
if adapter_missing["flag"]:
|
||
|
|
output = {
|
||
|
|
"run_id": run_id,
|
||
|
|
"completed_at": utc_now_iso(),
|
||
|
|
"skill_name": skill_name,
|
||
|
|
"status": "adapter-missing",
|
||
|
|
"reason": "adapter invocation command not found on PATH",
|
||
|
|
"results": [],
|
||
|
|
"summary": {"total": len(queries), "passed": 0, "failed": 0},
|
||
|
|
}
|
||
|
|
write_json(run_dir / "triggers-result.json", output)
|
||
|
|
print(json.dumps(output, indent=2))
|
||
|
|
return 0
|
||
|
|
|
||
|
|
results = []
|
||
|
|
for idx, q in enumerate(queries):
|
||
|
|
runs = per_query.get(idx, [])
|
||
|
|
rate = (sum(runs) / len(runs)) if runs else 0.0
|
||
|
|
should = bool(q.get("should_trigger", True))
|
||
|
|
passed = (rate >= args.threshold) if should else (rate < args.threshold)
|
||
|
|
results.append({
|
||
|
|
"query": q["query"],
|
||
|
|
"should_trigger": should,
|
||
|
|
"trigger_rate": round(rate, 3),
|
||
|
|
"triggers": int(sum(runs)),
|
||
|
|
"runs": len(runs),
|
||
|
|
"pass": passed,
|
||
|
|
})
|
||
|
|
|
||
|
|
output = {
|
||
|
|
"run_id": run_id,
|
||
|
|
"completed_at": utc_now_iso(),
|
||
|
|
"skill_name": skill_name,
|
||
|
|
"description": description,
|
||
|
|
"adapter": adapter_note,
|
||
|
|
"results": results,
|
||
|
|
"summary": {
|
||
|
|
"total": len(results),
|
||
|
|
"passed": sum(1 for r in results if r["pass"]),
|
||
|
|
"failed": sum(1 for r in results if not r["pass"]),
|
||
|
|
},
|
||
|
|
}
|
||
|
|
write_json(run_dir / "triggers-result.json", output)
|
||
|
|
print(json.dumps(output, indent=2))
|
||
|
|
return 0
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
sys.exit(main())
|