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orch-refine-code

编排一次保持行为的重构——确认测试通过、重构结构且不改变行为、保持测试通过、评审并受限提交。当需要改善结构但不能修改行为时使用。

orch-refine-code 是什么？

orch-refine-code 是一个 AI 代理技能，用于执行保持行为不变的结构重构。它包含一个完整的编排过程：首先确认相关测试存在且为绿色，然后分小步重构（提取模块、消除重复、删除死代码、减少嵌套、重命名以提高清晰度），每步后重新运行测试以确保行为一致。该技能与 orch-change-feature 或 orch-fix-defect 不同，后者允许行为发生变化。它依赖于 orch-pipeline 共享引擎，并在计划阶段（Gate 1）和提交前（Gate 2）设置关卡，确保重构是行为中性的。死代码或重复扫描可委托给 refactor-cleaner 代理。最终提交消息为 refactor:。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/orch-refine-code
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文档

orch-refine-code 是做什么的？

行为者 · 动作 · 目标：orch · refine · code。基于 orch-pipeline 共享引擎的轻量封装。

何时使用

  • 相同行为，更好的结构：提取模块、消除重复、删除死代码、减少嵌套、重命名以提高清晰度。
  • 与同类技能区分：如果行为将要发生任何改变，则不应使用此技能（应使用 orch-change-feature / orch-fix-defect）。

操作设置

  • **默认尺寸下限：**标准——重构涉及多个文件。
  • **阶段掩码：**0 → 2（计划重构）→ 4（保持绿色）→ 5 → 6。不编写新的行为测试——现有测试套件充当安全网。
  • 第一步（阶段 4）：在修改代码前确认相关测试存在且为绿色；如果覆盖率不足，先添加表征测试。然后分小步重构，每步后重新运行测试。

工作原理

  1. 使用上述设置运行 orch-pipeline 引擎。
  2. 对于死代码/重复清理，委托给 refactor-cleaner 代理（它运行 knip / depcheck / ts-prune 并安全移除）。
  3. 在关卡 1（重构计划）和关卡 2（提交前）停下。
  4. 提交为 refactor: ——差异必须行为中性。

示例

 orch-refine-code: 从 poller.py 中提取 NWS HTTP 客户端
 → 确认测试为绿色 → 计划提取  [关卡 1：批准]
 → 小步移动，全程测试为绿色 → 代码评审
 → 提交 refactor:  [关卡 2：确认]

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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