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orch-refine-code（代码重构编排）

编排一次行为保持的重构——确认测试通过，在不改变行为的前提下重组代码，保持测试通过，进行审查并添加门控提交。当结构需要改进但行为不能改变时使用。

orch-refine-code（代码重构编排） 是什么？

orch-refine-code 是一个基于 orch-pipeline 引擎的 AI 代理技能，专为行为保持的重构任务设计。其核心思想是在确保所有现有测试通过的前提下，安全地改进代码结构——例如提取模块、消除重复代码、删除死代码、减少嵌套层级、重命名以提高可读性等。它与 orch-change-feature 和 orch-fix-defect 等技能形成互补：只要行为有任何变更，就不应使用此技能。技能通过分阶段掩码控制操作流程：从规划重构（第 2 阶段）开始，经测试保持（第 4 阶段）到提交准备。在操作中，它会首先确认相关测试存在且为绿色，若覆盖不足则先添加表征测试；随后以微小步骤进行重构，每步后都重新运行测试。对于死代码或重复代码清理，它还委托给 refactor-cleaner 代理（使用 knip / depcheck / ts-prune 等工具）。最终提交信息为 refactor:，确保差异是行为无关的。此技能适用于主流代码编辑器和 AI 代理工具（如 Claude Code, Cursor, Codex）。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-refine-code
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orch-refine-code（代码重构编排） 是做什么的？

演员 · 动作 · 目标：orch · refine · code。对 orch-pipeline 中共享引擎的轻量封装。

何时使用

  • 相同行为，更好结构：提取模块、消除重复、删除死代码、减少嵌套、为清晰重命名。
  • 与同类技能区分：如果行为有任何变更，则不应使用此技能（应改用 orch-change-feature / orch-fix-defect）。

操作设置

  • 默认规模下限： 标准——重构涉及多个文件。
  • 阶段掩码： 0 → 2（规划重构）→ 4（保持绿色）→ 5 → 6。不编写新的行为测试——现有的测试套件是安全网。
  • 第一步（第 4 阶段）： 确认相关测试存在且在修改代码前为绿色；如果覆盖率不足，先添加表征测试。然后以微小步骤进行重构，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/重复代码清理，委托给 refactor-cleaner 代理（它运行 knip / depcheck / ts-prune 并安全地移除）。
  3. 在门控 1（重构计划）和门控 2（提交前）停止。
  4. 提交信息为 refactor: ——差异必须保持行为无关。

示例

 orch-refine-code: 从 poller.py 中提取 NWS HTTP 客户端
 → 确认测试通过 → 规划提取  [门控 1: 批准]
 → 以微小步骤移动，全程测试通过 → 代码审查
 → 提交 refactor:  [门控 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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳：このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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