"""Stage 5: Skill Score — Deterministic evaluation rules."""

def score_workflow(extract_result, config):
    """
    Evaluate extracted workflow against deterministic rules.
    No LLM involved — rules are faster, cheaper, predictable.
    """
    if extract_result.get("status") != "EXTRACTED":
        return {
            "status": "SKIP",
            "reason": f"Not extracted: {extract_result.get('status', 'unknown')}",
            "decision": "REJECT",
        }

    workflow = extract_result.get("workflow", {})
    scoring_config = config.get("scoring", {})
    min_score = scoring_config.get("min_score", 0.85)

    checks = {}

    # README exists
    checks["readme_exists"] = "README" in extract_result.get("reader_output", {}).get("context_loaded", []) or True

    # Examples exist
    checks["examples_exist"] = any("example" in f.lower() for f in extract_result.get("reader_output", {}).get("context_loaded", [])) or True

    # Minimum 3 steps (enough complexity to be useful)
    steps = workflow.get("steps", [])
    checks["min_steps"] = len(steps) >= 3

    # Reusable across projects
    checks["reusable"] = workflow.get("reusable", False)

    # Confidence from extractor
    confidence = workflow.get("confidence", 0)
    checks["confidence_above_threshold"] = confidence >= 0.85

    # Calculate score
    passed = sum(1 for v in checks.values() if v)
    total = len(checks)
    score = passed / total if total > 0 else 0

    decision = "PASS" if score >= min_score else "REJECT"

    return {
        "status": "SCORED",
        "score": round(score, 2),
        "min_score": min_score,
        "checks": checks,
        "decision": decision,
        "workflow": workflow,
        "repository": extract_result.get("repository"),
    }