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lanyasheng

Improvement Orchestrator

by _silhouette · GitHub ↗ · v1.1.1 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 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)。
README (SKILL.md)

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

\x3Cexample> 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) \x3C/example>

\x3Canti-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 \x3C/anti-example>

CLI

python3 scripts/orchestrate.py \
  --target /path/to/skill \
  --state-root /path/to/state \
  --max-retries 3 \
  --auto

Output 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

Usage Guidance
This orchestrator is coherent with its documentation: it runs local improvement scripts and may modify files under the --target path, but only supports conservative, low-risk changes (Markdown appends) in this first version and creates backups and audit artifacts. Before installing or running: (1) point --target at a safe test copy (not infra or gateway configs), (2) inspect the generated state/backups directory after a dry run, (3) prefer using --state-root in an isolated temp directory, and (4) verify the full repo layout (other improvement-* scripts and lib.*) exists in the environment so the orchestrator calls the intended local code. If you want stronger safety, run it without --auto first to inspect each step's outputs or run in a sandbox repository.
Capability Analysis
Type: OpenClaw Skill Name: improvement-orchestrator Version: 1.1.1 The 'improvement-orchestrator' skill bundle is a coordination tool designed to manage a multi-stage AI skill improvement pipeline (Propose, Discriminate, Evaluate, Execute, and Gate). The primary logic in 'scripts/orchestrate.py' uses subprocess calls to execute local Python scripts corresponding to each stage, managing state transitions and implementing a retry loop for failed improvements. The bundle includes extensive documentation and unit tests ('tests/test_orchestrate.py') that demonstrate a well-structured and transparent design. Security-wise, the 'references/guardrails.md' file explicitly defines safety boundaries, limiting automated changes to low-risk documentation files and requiring human intervention for higher-risk modifications. No evidence of malicious intent, data exfiltration, or unauthorized execution was found.
Capability Assessment
Purpose & Capability
Name/description match the implementation: the skill coordinates proposer→critic→evaluator→executor→gate. It requires no unusual environment variables or external credentials. The code expects a repo-style layout (other improvement-* skills and lib.* helpers) which is consistent with an orchestrator that dispatches to sibling scripts.
Instruction Scope
SKILL.md and scripts explicitly instruct the agent to run local Python scripts that will read and write filesystem artifacts (candidate/ranking/execution/receipt/state files) and may apply low-risk changes (append_markdown_section) to target skills. This is within scope, but the runtime will modify files under the provided target path and create backups/traces — users should ensure the target path is correct and not a protected or infra-critical path.
Install Mechanism
There is no install spec (instruction-only plus local scripts). Nothing is downloaded or written by an installer. Risk from install-time network downloads is not present.
Credentials
No environment variables, credentials, or external tokens are requested. The orchestrator operates on local file paths and local sibling scripts; it does not request unrelated secrets or remote API keys.
Persistence & Privilege
The skill is user-invocable and can be invoked autonomously by the agent (normal default). It does create persistent local artifacts (state, backups, traces) and modifies target files when the executor accepts a candidate. It does not set always:true. Because it can apply changes to local files, run it with appropriate target paths and review produced artifacts/backups when used.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install improvement-orchestrator
  3. After installation, invoke the skill by name or use /improvement-orchestrator
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.1
v2.0: 9-dim evaluation, category modifiers, per-dim Pareto tolerances, enriched docs
v1.1.0
v1.1.0: Fix 4 critical pipeline bugs (Ralph Wiggum/Autoloop/Evaluator verdict), scoring overhaul (base 4->2, LLM weight 50%, semantic relevance), generator LLM-first, learner/gate/executor fixes
v1.0.0
- Initial release of improvement-orchestrator. - Enables automated end-to-end improvement workflow for one or multiple skills, covering generation, scoring, quality gating, execution, and learning. - Supports automatic retry with failure trace injection (up to 3 times) for robust improvement cycles. - Not intended for single-stage evaluation or manual scoring—use specialized tools for those tasks. - Comprehensive CLI, clear usage guidance, and related skills referenced for modular workflows.
Metadata
Slug improvement-orchestrator
Version 1.1.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 3
Frequently Asked Questions

What is Improvement Orchestrator?

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)。 It is an AI Agent Skill for Claude Code / OpenClaw, with 136 downloads so far.

How do I install Improvement Orchestrator?

Run "/install improvement-orchestrator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Improvement Orchestrator free?

Yes, Improvement Orchestrator is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Improvement Orchestrator support?

Improvement Orchestrator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Improvement Orchestrator?

It is built and maintained by _silhouette (@lanyasheng); the current version is v1.1.1.

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