chore: import upstream snapshot with attribution
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wehub-resource-sync
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#!/usr/bin/env python3
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"""Live meta-skill creator E2E harness.
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This intentionally prints only structural evidence. It never prints provider
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API keys loaded from ``--env-file`` or the process environment.
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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 tempfile
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from pathlib import Path
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from typing import Any
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from opensquilla.skills import proposals_lib
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from opensquilla.skills.creator import proposer
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DEFAULT_HISTORY = {
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"co_occurrences": [
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{"skills": ["history-explorer", "summarize"], "freq": 8},
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],
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"note": "Prefer a two-step read-and-summarize workflow using low-risk skills.",
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}
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DEFAULT_INTENT = (
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"Create a meta-skill that first uses history-explorer to inspect recent "
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"OpenSquilla decision history for a query, then uses summarize to produce "
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"a concise operational summary. Use only history-explorer and summarize."
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)
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def _load_env_file(path: Path | None) -> None:
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if path is None or not path.is_file():
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return
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for raw in path.read_text(encoding="utf-8").splitlines():
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line = raw.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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key, value = line.split("=", 1)
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key = key.strip()
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value = value.strip().strip("'\"")
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if key and key not in os.environ:
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os.environ[key] = value
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def run_live_meta_skill_creator_e2e(
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*,
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home: Path | None = None,
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pattern_id: str = "p1_sequential",
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history_summary: str | None = None,
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user_intent: str = DEFAULT_INTENT,
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provider: str | None = None,
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model: str | None = None,
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auto_enable: bool = True,
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auto_enable_max_risk: str = "low",
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) -> dict[str, Any]:
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"""Run fill_slots -> assemble -> lint -> smoke -> persist/auto-enable."""
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previous_provider = os.environ.get("OPENSQUILLA_LLM_PROVIDER")
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previous_model = os.environ.get("OPENSQUILLA_LLM_MODEL")
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if provider:
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os.environ["OPENSQUILLA_LLM_PROVIDER"] = provider
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if model:
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os.environ["OPENSQUILLA_LLM_MODEL"] = model
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try:
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home_path = home or Path(tempfile.mkdtemp(prefix="opensquilla-live-meta-skill-"))
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home_path.mkdir(parents=True, exist_ok=True)
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proposals_lib.write_auto_propose_settings(
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home_path,
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{
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"auto_enable": auto_enable,
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"auto_enable_max_risk": auto_enable_max_risk,
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},
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)
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history = history_summary or json.dumps(DEFAULT_HISTORY, ensure_ascii=False)
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slots_json = proposer.meta_skill_fill_slots(pattern_id, history, user_intent)
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slots = json.loads(slots_json)
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skill_md = proposer.meta_skill_assemble(pattern_id, slots_json)
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lint_result = json.loads(proposer.meta_skill_lint_run(skill_md, "G1,G2"))
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smoke_result = proposer.run_smoke_gates(
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skill_md=skill_md,
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fixture_gen_fn=lambda _md, kind: {
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"positive": f"please use {slots['triggers'][0]} for recent decisions",
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"negative": "what is the weather tomorrow in Tokyo?",
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}[kind],
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classifier_model=model or "live-meta-skill-creator-e2e",
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)
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persist = json.loads(proposer.meta_skill_persist_proposal(
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skill_md,
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json.dumps(lint_result),
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json.dumps(smoke_result),
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home=str(home_path),
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))
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managed = (
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sorted(p.name for p in (home_path / "skills").iterdir())
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if (home_path / "skills").is_dir()
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else []
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)
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pending = (
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sorted(p.name for p in (home_path / "proposals").iterdir())
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if (home_path / "proposals").is_dir()
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else []
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)
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return {
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"ok": True,
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"home": str(home_path),
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"llm_slots": {
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"name": slots.get("name"),
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"triggers": slots.get("triggers"),
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"steps": [
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{"id": s.get("id"), "skill": s.get("skill")}
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for s in slots.get("steps", [])
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],
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},
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"lint": lint_result,
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"smoke": smoke_result,
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"persist": persist,
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"managed": managed,
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"pending": pending,
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}
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finally:
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if previous_provider is None:
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os.environ.pop("OPENSQUILLA_LLM_PROVIDER", None)
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else:
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os.environ["OPENSQUILLA_LLM_PROVIDER"] = previous_provider
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if previous_model is None:
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os.environ.pop("OPENSQUILLA_LLM_MODEL", None)
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else:
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os.environ["OPENSQUILLA_LLM_MODEL"] = previous_model
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def _parser() -> argparse.ArgumentParser:
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument("--env-file", type=Path, default=None)
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p.add_argument("--home", type=Path, default=None)
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p.add_argument("--provider", default=None)
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p.add_argument("--model", default=None)
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p.add_argument("--pattern-id", default="p1_sequential")
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p.add_argument("--history-summary", default=None)
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p.add_argument("--user-intent", default=DEFAULT_INTENT)
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p.add_argument("--no-auto-enable", action="store_true")
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p.add_argument("--auto-enable-max-risk", default="low")
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return p
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def main(argv: list[str] | None = None) -> int:
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args = _parser().parse_args(argv)
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_load_env_file(args.env_file)
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result = run_live_meta_skill_creator_e2e(
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home=args.home,
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pattern_id=args.pattern_id,
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history_summary=args.history_summary,
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user_intent=args.user_intent,
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provider=args.provider,
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model=args.model,
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auto_enable=not args.no_auto_enable,
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auto_enable_max_risk=args.auto_enable_max_risk,
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)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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