common-skills/scripts/run_evals.py

#!/usr/bin/env python3
"""
Skill eval runner with regression protection.

Usage:
  python scripts/run_evals.py                     # run all skills
  python scripts/run_evals.py codereview          # run single skill
  python scripts/run_evals.py codereview --iter 2 # specify iteration
  python scripts/run_evals.py --check-regression  # fail if any skill regressed
"""
import argparse
import json
import re
import subprocess
import time
from pathlib import Path

SKILLS_DIR = Path("skills")
WORKSPACE_ROOT = Path("evals-workspace")
BASELINE_FILE = Path("baselines.json")


def run_prompt(prompt: str, with_skill: bool) -> tuple[str, float]:
    agent = "main" if with_skill else "default"
    start = time.time()
    result = subprocess.run(
        ["kiro-cli", "chat", "--agent", agent, "--no-interactive", "--message", prompt],
        capture_output=True, text=True, timeout=90,
    )
    elapsed = round(time.time() - start, 2)
    response = re.sub(r'\x1b\[[0-9;]*[A-Za-z]', '', result.stdout).strip()
    return response, elapsed


def grade(response: str, expected_output: str) -> dict:
    words = re.findall(r'[a-zA-Z]{5,}', expected_output)
    keywords = list({w.lower() for w in words})
    matched = [kw for kw in keywords if kw in response.lower()]
    score = round(len(matched) / len(keywords), 2) if keywords else 0.0
    return {"score": score, "passed": score >= 0.4, "matched_keywords": matched[:10]}


def load_baselines() -> dict:
    return json.loads(BASELINE_FILE.read_text()) if BASELINE_FILE.exists() else {}


def save_baselines(baselines: dict) -> None:
    BASELINE_FILE.write_text(json.dumps(baselines, indent=2))


def run_skill_evals(skill_name: str, iteration: int) -> dict:
    evals_file = SKILLS_DIR / skill_name / "evals" / "evals.json"
    if not evals_file.exists():
        print(f"  ⚠️  No evals.json: {evals_file}")
        return {}

    evals = json.loads(evals_file.read_text()).get("evals", [])
    iter_dir = WORKSPACE_ROOT / skill_name / f"iteration-{iteration}"
    results = []
    print(f"\n[Skill: {skill_name}] iteration-{iteration}")

    for case in evals:
        case_id, prompt, expected = case["id"], case["prompt"], case["expected_output"]
        case_dir = iter_dir / f"eval-{skill_name}-{case_id}"

        for mode in ("with_skill", "without_skill"):
            out_dir = case_dir / mode
            out_dir.mkdir(parents=True, exist_ok=True)
            response, elapsed = run_prompt(prompt, mode == "with_skill")
            grading = grade(response, expected)
            (out_dir / "response.txt").write_text(response)
            (out_dir / "timing.json").write_text(json.dumps({"duration_seconds": elapsed, "with_skill": mode == "with_skill"}, indent=2))
            (out_dir / "grading.json").write_text(json.dumps(grading, indent=2))

        with_grade = json.loads((case_dir / "with_skill" / "grading.json").read_text())
        without_grade = json.loads((case_dir / "without_skill" / "grading.json").read_text())
        delta = round(with_grade["score"] - without_grade["score"], 2)
        status = "✅" if with_grade["passed"] else "❌"
        print(f"  Case {case_id}: {status} score={with_grade['score']} (Δ{delta:+.2f} vs baseline)")
        results.append({"id": case_id, "with_skill": with_grade, "without_skill": without_grade})

    passed = sum(1 for r in results if r["with_skill"]["passed"])
    pass_rate = round(passed / len(results), 2) if results else 0
    benchmark = {"skill": skill_name, "iteration": iteration, "total": len(results), "passed": passed, "pass_rate": pass_rate, "cases": results}
    (iter_dir / "benchmark.json").write_text(json.dumps(benchmark, indent=2))
    print(f"  Pass rate: {passed}/{len(results)}")
    return benchmark


def check_regression(benchmark: dict, baselines: dict) -> list[str]:
    """Return list of regression messages, empty if no regression."""
    skill = benchmark.get("skill")
    if not skill or skill not in baselines:
        return []
    baseline_rate = baselines[skill]["pass_rate"]
    current_rate = benchmark.get("pass_rate", 0)
    if current_rate < baseline_rate:
        return [f"{skill}: pass_rate dropped {baseline_rate} → {current_rate}"]
    return []


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("skill", nargs="?", help="Skill name (default: all)")
    parser.add_argument("--iter", type=int, default=1)
    parser.add_argument("--update-baseline", action="store_true", help="Save current results as new baseline")
    parser.add_argument("--check-regression", action="store_true", help="Exit non-zero if any skill regressed")
    args = parser.parse_args()

    skills = [args.skill] if args.skill else [d.name for d in SKILLS_DIR.iterdir() if d.is_dir()]
    baselines = load_baselines()
    regressions = []
    all_results = []

    for skill in filter(None, skills):
        result = run_skill_evals(skill, args.iter)
        if result:
            all_results.append(result)
            regressions.extend(check_regression(result, baselines))

    total = sum(r.get("total", 0) for r in all_results)
    passed = sum(r.get("passed", 0) for r in all_results)
    print(f"\n{'='*40}")
    print(f"Overall: {passed}/{total} cases passed")

    if args.update_baseline:
        for r in all_results:
            baselines[r["skill"]] = {"pass_rate": r["pass_rate"], "iteration": r["iteration"]}
        save_baselines(baselines)
        print(f"Baselines updated → {BASELINE_FILE}")

    if args.check_regression and regressions:
        print("\n🚨 Regressions detected:")
        for msg in regressions:
            print(f"  ❌ {msg}")
        raise SystemExit(1)

    print(f"Results saved to {WORKSPACE_ROOT}/")


if __name__ == "__main__":
    main()