Improvement Evaluator

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name: improvement-evaluator category: tool description: "当需要验证 Skill 改进是否真正提升了 AI 执行效果时使用。通过预定义任务集（YAML）运行 AI 任务，判定 pass/fail，输出 execution_pass_rate。不用于文档结构评分（用 improvement-learner）或候选打分（用 improvement-discriminator）。" license: MIT triggers: - evaluate skill execution - run task suite - execution pass rate - 执行效果评估 - 任务集验证 version: 0.1.0 author: OpenClaw Team

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Improvement Evaluator

Measures whether a Skill actually makes AI perform better on real tasks.

When to Use

Verify that a SKILL.md change improves AI task execution (not just document structure)

Run a task suite against a candidate SKILL.md and compare with baseline

Get execution_pass_rate as a concrete quality metric

Run standalone evaluation on current SKILL.md to discover baseline failures

When NOT to Use

只想检查 SKILL.md 结构质量 → use improvement-learner

只想给候选打分 → use improvement-discriminator

跑全流程 → use improvement-orchestrator

2 Modes

| Mode | When | Required Params | |------|------|-----------------| | Pipeline | Called by orchestrator after discriminator | --input, --candidate-id, --task-suite, --state-root | | Standalone | Direct evaluation of current SKILL.md | --standalone, --task-suite, --state-root, --skill-path |

CLI

# Pipeline mode: evaluate candidate vs baseline
python3 scripts/evaluate.py --input ranking.json --candidate-id cand-01-docs \
  --task-suite tasks.yaml --state-root ./state \
  [--pass-k 1] [--eval-threshold 6.0] [--baseline-cache-dir /cache] [--mock] [--output eval.json]
Standalone mode: evaluate current SKILL.md directly
python3 scripts/evaluate.py --standalone --task-suite tasks.yaml \
  --state-root ./state --skill-path /path/to/skill [--mock]

| Param | Default | When to change | |-------|---------|---------------| | --eval-threshold | 6.0 | Orchestrator sets per-category thresholds (e.g., docs=5.0, prompt=7.0) | | --pass-k | 1 | Raise to 3 for flaky tasks | | --mock | false | Use in CI or when claude CLI is not installed | | --baseline-cache-dir | None | Set to avoid re-running baseline on unchanged SKILL.md |

3 Judge Types

| Judge | type in YAML | Mechanism | Use When | |-------|----------------|-----------|----------| | ContainsJudge | contains | Check output contains all strings in expected list | Deterministic keyword/format checks | | PytestJudge | pytest | Run pytest on fixtures/{test_file} against AI output | Structured output validation (JSON, code) | | LLMRubricJudge | llm-rubric | LLM scores output against rubric text (mock mode: random pass) | Semantic quality evaluation |

Task Suite YAML Format

skill_id: my-skill
version: "1.0"
tasks:
  - id: task-001
    prompt: "Given X, produce Y"
    judge: {type: contains, expected: ["keyword1", "keyword2"]}
  - id: task-002
    prompt: "Generate a config file"
    judge: {type: pytest, test_file: fixtures/test_config.py}
  - id: task-003
    prompt: "Explain concept Z"
    judge: {type: llm-rubric, rubric: "Must cover A, B, C with examples"}

Conditional Evaluation

Score threshold: candidates with discriminator score < --eval-threshold are skipped (verdict=skipped)

Baseline abort: if baseline pass_rate < 0.2 (20%), evaluation aborts with verdict=error — indicates broken task suite

Baseline caching: SHA256(skill_content + suite_path) → 7-day TTL cache to avoid re-running unchanged baselines

Pipeline mode: candidate vs baseline comparison $ python3 scripts/evaluate.py --input ranking.json --candidate-id c1 --task-suite tasks.yaml --state-root ./state → Candidate pass rate: 0.80 (4/5 tasks passed) → Baseline pass rate: 0.60 (3/5 tasks passed) → {"execution_pass_rate": 0.80, "baseline_pass_rate": 0.60, "delta": 0.20, "verdict": "pass"}

Using evaluator without task suite: → Evaluator requires --task-suite. Without it, orchestrator skips evaluator entirely. → No --standalone without --task-suite either — both modes require it.

Output Artifact

{"stage": "evaluated", "verdict": "pass",
 "evaluation": {"execution_pass_rate": 0.80, "baseline_pass_rate": 0.60, "delta": 0.20},
 "candidate_results": [{"task_id": "t1", "passed": true, "score": 1.0}],
 "next_step": "gate_decision", "next_owner": "gate"}

Related Skills

improvement-discriminator: Provides scores; evaluator checks score >= eval_threshold

improvement-gate: RegressionGate checks evaluator verdict via --evaluation artifact

improvement-orchestrator: Calls evaluator as stage 3; runs standalone baseline, injects failures to generator

improvement-generator: Consumes baseline-failures.json for targeted SKILL.md fixes