스킬 만들기 도우미 (Anthropic)

#!/usr/bin/env python3"""Run the eval + improve loop until all pass or max iterations reached.Combines run_eval.py and improve_description.py in a loop, tracking historyand returning the best description found. Supports train/test split to preventoverfitting."""import argparseimport jsonimport randomimport sysimport tempfileimport timeimport webbrowserfrom pathlib import Pathfrom scripts.generate_report import generate_htmlfrom scripts.improve_description import improve_descriptionfrom scripts.run_eval import find_project_root, run_evalfrom scripts.utils import parse_skill_mddef split_eval_set(eval_set: list[dict], holdout: float, seed: int = 42) -> tuple[list[dict], list[dict]]:    """Split eval set into train and test sets, stratified by should_trigger."""    random.seed(seed)    # Separate by should_trigger    trigger = [e for e in eval_set if e["should_trigger"]]    no_trigger = [e for e in eval_set if not e["should_trigger"]]    # Shuffle each group    random.shuffle(trigger)    random.shuffle(no_trigger)    # Calculate split points    n_trigger_test = max(1, int(len(trigger) * holdout))    n_no_trigger_test = max(1, int(len(no_trigger) * holdout))    # Split    test_set = trigger[:n_trigger_test] + no_trigger[:n_no_trigger_test]    train_set = trigger[n_trigger_test:] + no_trigger[n_no_trigger_test:]    return train_set, test_setdef run_loop(    eval_set: list[dict],    skill_path: Path,    description_override: str | None,    num_workers: int,    timeout: int,    max_iterations: int,    runs_per_query: int,    trigger_threshold: float,    holdout: float,    model: str,    verbose: bool,    live_report_path: Path | None = None,    log_dir: Path | None = None,) -> dict:    """Run the eval + improvement loop."""    project_root = find_project_root()    name, original_description, content = parse_skill_md(skill_path)    current_description = description_override or original_description    # Split into train/test if holdout > 0    if holdout > 0:        train_set, test_set = split_eval_set(eval_set, holdout)        if verbose:            print(f"Split: {len(train_set)} train, {len(test_set)} test (holdout={holdout})", file=sys.stderr)    else:        train_set = eval_set        test_set = []    history = []    exit_reason = "unknown"    for iteration in range(1, max_iterations + 1):        if verbose:            print(f"\n{'='*60}", file=sys.stderr)            print(f"Iteration {iteration}/{max_iterations}", file=sys.stderr)            print(f"Description: {current_description}", file=sys.stderr)            print(f"{'='*60}", file=sys.stderr)        # Evaluate train + test together in one batch for parallelism        all_queries = train_set + test_set        t0 = time.time()        all_results = run_eval(            eval_set=all_queries,            skill_name=name,            description=current_description,            num_workers=num_workers,            timeout=timeout,            project_root=project_root,            runs_per_query=runs_per_query,            trigger_threshold=trigger_threshold,            model=model,        )        eval_elapsed = time.time() - t0        # Split results back into train/test by matching queries        train_queries_set = {q["query"] for q in train_set}        train_result_list = [r for r in all_results["results"] if r["query"] in train_queries_set]        test_result_list = [r for r in all_results["results"] if r["query"] not in train_queries_set]        train_passed = sum(1 for r in train_result_list if r["pass"])        train_total = len(train_result_list)        train_summary = {"passed": train_passed, "failed": train_total - train_passed, "total": train_total}        train_results = {"results": train_result_list, "summary": train_summary}        if test_set:            test_passed = sum(1 for r in test_result_list if r["pass"])            test_total = len(test_result_list)            test_summary = {"passed": test_passed, "failed": test_total - test_passed, "total": test_total}            test_results = {"results": test_result_list, "summary": test_summary}        else:            test_results = None            test_summary = None        history.append({            "iteration": iteration,            "description": current_description,            "train_passed": train_summary["passed"],            "train_failed": train_summary["failed"],            "train_total": train_summary["total"],            "train_results": train_results["results"],            "test_passed": test_summary["passed"] if test_summary else None,            "test_failed": test_summary["failed"] if test_summary else None,            "test_total": test_summary["total"] if test_summary else None,            "test_results": test_results["results"] if test_results else None,            # For backward compat with report generator            "passed": train_summary["passed"],            "failed": train_summary["failed"],            "total": train_summary["total"],            "results": train_results["results"],        })        # Write live report if path provided        if live_report_path:            partial_output = {                "original_description": original_description,                "best_description": current_description,                "best_score": "in progress",                "iterations_run": len(history),                "holdout": holdout,                "train_size": len(train_set),                "test_size": len(test_set),                "history": history,            }            live_report_path.write_text(generate_html(partial_output, auto_refresh=True, skill_name=name))        if verbose:            def print_eval_stats(label, results, elapsed):                pos = [r for r in results if r["should_trigger"]]                neg = [r for r in results if not r["should_trigger"]]                tp = sum(r["triggers"] for r in pos)                pos_runs = sum(r["runs"] for r in pos)                fn = pos_runs - tp                fp = sum(r["triggers"] for r in neg)                neg_runs = sum(r["runs"] for r in neg)                tn = neg_runs - fp                total = tp + tn + fp + fn                precision = tp / (tp + fp) if (tp + fp) > 0 else 1.0                recall = tp / (tp + fn) if (tp + fn) > 0 else 1.0                accuracy = (tp + tn) / total if total > 0 else 0.0                print(f"{label}: {tp+tn}/{total} correct, precision={precision:.0%} recall={recall:.0%} accuracy={accuracy:.0%} ({elapsed:.1f}s)", file=sys.stderr)                for r in results:                    status = "PASS" if r["pass"] else "FAIL"                    rate_str = f"{r['triggers']}/{r['runs']}"                    print(f"  [{status}] rate={rate_str} expected={r['should_trigger']}: {r['query'][:60]}", file=sys.stderr)            print_eval_stats("Train", train_results["results"], eval_elapsed)            if test_summary:                print_eval_stats("Test ", test_results["results"], 0)        if train_summary["failed"] == 0:            exit_reason = f"all_passed (iteration {iteration})"            if verbose:                print(f"\nAll train queries passed on iteration {iteration}!", file=sys.stderr)            break        if iteration == max_iterations:            exit_reason = f"max_iterations ({max_iterations})"            if verbose:                print(f"\nMax iterations reached ({max_iterations}).", file=sys.stderr)            break        # Improve the description based on train results        if verbose:            print(f"\nImproving description...", file=sys.stderr)        t0 = time.time()        # Strip test scores from history so improvement model can't see them        blinded_history = [            {k: v for k, v in h.items() if not k.startswith("test_")}            for h in history        ]        new_description = improve_description(            skill_name=name,            skill_content=content,            current_description=current_description,            eval_results=train_results,            history=blinded_history,            model=model,            log_dir=log_dir,            iteration=iteration,        )        improve_elapsed = time.time() - t0        if verbose:            print(f"Proposed ({improve_elapsed:.1f}s): {new_description}", file=sys.stderr)        current_description = new_description    # Find the best iteration by TEST score (or train if no test set)    if test_set:        best = max(history, key=lambda h: h["test_passed"] or 0)        best_score = f"{best['test_passed']}/{best['test_total']}"    else:        best = max(history, key=lambda h: h["train_passed"])        best_score = f"{best['train_passed']}/{best['train_total']}"    if verbose:        print(f"\nExit reason: {exit_reason}", file=sys.stderr)        print(f"Best score: {best_score} (iteration {best['iteration']})", file=sys.stderr)    return {        "exit_reason": exit_reason,        "original_description": original_description,        "best_description": best["description"],        "best_score": best_score,        "best_train_score": f"{best['train_passed']}/{best['train_total']}",        "best_test_score": f"{best['test_passed']}/{best['test_total']}" if test_set else None,        "final_description": current_description,        "iterations_run": len(history),        "holdout": holdout,        "train_size": len(train_set),        "test_size": len(test_set),        "history": history,    }def main():    parser = argparse.ArgumentParser(description="Run eval + improve loop")    parser.add_argument("--eval-set", required=True, help="Path to eval set JSON file")    parser.add_argument("--skill-path", required=True, help="Path to skill directory")    parser.add_argument("--description", default=None, help="Override starting description")    parser.add_argument("--num-workers", type=int, default=10, help="Number of parallel workers")    parser.add_argument("--timeout", type=int, default=30, help="Timeout per query in seconds")    parser.add_argument("--max-iterations", type=int, default=5, help="Max improvement iterations")    parser.add_argument("--runs-per-query", type=int, default=3, help="Number of runs per query")    parser.add_argument("--trigger-threshold", type=float, default=0.5, help="Trigger rate threshold")    parser.add_argument("--holdout", type=float, default=0.4, help="Fraction of eval set to hold out for testing (0 to disable)")    parser.add_argument("--model", required=True, help="Model for improvement")    parser.add_argument("--verbose", action="store_true", help="Print progress to stderr")    parser.add_argument("--report", default="auto", help="Generate HTML report at this path (default: 'auto' for temp file, 'none' to disable)")    parser.add_argument("--results-dir", default=None, help="Save all outputs (results.json, report.html, log.txt) to a timestamped subdirectory here")    args = parser.parse_args()    eval_set = json.loads(Path(args.eval_set).read_text())    skill_path = Path(args.skill_path)    if not (skill_path / "SKILL.md").exists():        print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr)        sys.exit(1)    name, _, _ = parse_skill_md(skill_path)    # Set up live report path    if args.report != "none":        if args.report == "auto":            timestamp = time.strftime("%Y%m%d_%H%M%S")            live_report_path = Path(tempfile.gettempdir()) / f"skill_description_report_{skill_path.name}_{timestamp}.html"        else:            live_report_path = Path(args.report)        # Open the report immediately so the user can watch        live_report_path.write_text("<html><body><h1>Starting optimization loop...</h1><meta http-equiv='refresh' content='5'></body></html>")        webbrowser.open(str(live_report_path))    else:        live_report_path = None    # Determine output directory (create before run_loop so logs can be written)    if args.results_dir:        timestamp = time.strftime("%Y-%m-%d_%H%M%S")        results_dir = Path(args.results_dir) / timestamp        results_dir.mkdir(parents=True, exist_ok=True)    else:        results_dir = None    log_dir = results_dir / "logs" if results_dir else None    output = run_loop(        eval_set=eval_set,        skill_path=skill_path,        description_override=args.description,        num_workers=args.num_workers,        timeout=args.timeout,        max_iterations=args.max_iterations,        runs_per_query=args.runs_per_query,        trigger_threshold=args.trigger_threshold,        holdout=args.holdout,        model=args.model,        verbose=args.verbose,        live_report_path=live_report_path,        log_dir=log_dir,    )    # Save JSON output    json_output = json.dumps(output, indent=2)    print(json_output)    if results_dir:        (results_dir / "results.json").write_text(json_output)    # Write final HTML report (without auto-refresh)    if live_report_path:        live_report_path.write_text(generate_html(output, auto_refresh=False, skill_name=name))        print(f"\nReport: {live_report_path}", file=sys.stderr)    if results_dir and live_report_path:        (results_dir / "report.html").write_text(generate_html(output, auto_refresh=False, skill_name=name))    if results_dir:        print(f"Results saved to: {results_dir}", file=sys.stderr)if __name__ == "__main__":    main()