- name
- improvement-orchestrator
- description
- 当需要一键跑完「生成→评分→评估→执行→门禁」全流程、失败后自动重试、或批量改进多个 skill 时使用。不用于单独评估 skill 质量（用 improvement-learner）或手动打分（用 improvement-discriminator）。
- license
- MIT
- triggers
- version
- 0.1.0
- author
- OpenClaw Team
Improvement Orchestrator
Coordinates the full improvement pipeline: Generator → Discriminator → Evaluator → Executor → Gate.
When to Use
- Run a full improvement cycle on one or more skills
- Coordinate the 5-stage pipeline end-to-end (with optional evaluator)
- Retry failed improvements with trace-aware feedback (Ralph Wiggum loop)
When NOT to Use
- 只想检查 skill 质量评分 → use
improvement-learner - 只想手动给候选打分 → use
improvement-discriminator - 只想改一个文件 → use
improvement-executor - 只想查基准数据 → use
benchmark-store
Pipeline
propose → discriminate → evaluate* → execute → gate
↻ Ralph Wiggum: fail → inject trace → retry (max 3)
* evaluate is optional — skipped if no task_suite.yaml exists<example> 正确用法: 对一个 skill 运行全流程改进 $ python3 scripts/orchestrate.py --target /path/to/skill --state-root ./state → 自动完成: 生成候选 → 多人盲审 → 任务评估 → 执行变更 → 6层门禁 → 失败时自动注入 trace 重试（最多 3 次） </example>
<anti-example> 错误用法: 只想看评分却用了 orchestrator $ python3 scripts/orchestrate.py --target /path/to/skill # 会执行变更！ → 应该用: python3 improvement-learner/scripts/self_improve.py --skill-path /path/to/skill --max-iterations 1 </anti-example>
CLI
python3 scripts/orchestrate.py \
--target /path/to/skill \
--state-root /path/to/state \
--max-retries 3 \
--autoOutput Artifacts
| Request | Deliverable |
|---|---|
| Full pipeline | JSON with all stage outputs, final scores, execution trace |
| Retry cycle | Updated candidates with injected failure traces |
Related Skills
- improvement-generator: Produces candidate proposals (stage 1)
- improvement-discriminator: Multi-reviewer panel scoring (stage 2)
- improvement-evaluator: Task suite execution validation (stage 3, optional)
- improvement-executor: Applies changes with backup/rollback (stage 4)
- improvement-gate: 6-layer quality gate (stage 5)
- benchmark-store: Frozen benchmarks and Pareto front data
References
- Architecture — System design and data flow
- Guardrails — Safety rules and protected targets
- End-to-End Demo — Complete walkthrough