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orch-refine-code

協調保持行為不變的重構流程 — 確認測試通過、重組結構、保持測試通過、審查並進行有閘門控制的提交。適合在行為不可改變而結構需要改善時使用。

orch-refine-code 是什麼？

orch-refine-code 是一個專為行為保持重構設計的 AI 技能，基於 orch-pipeline 共享引擎。它確保重構過程全程不改變既有行為，由測試套件作為安全網。支援平臺包括 Claude Code、Codex 等相容指令管線的工具。流程包含：確認相關測試存在且為綠燈 → 規劃重構 → 以小幅步驟執行重構，每步後重新測試 → 代碼審查 → 閘門控制提交。此技能能自動處理死代碼/重複代碼，並委派給 refactor-cleaner 代理（使用 knip / depcheck / ts-prune），最終提交訊息為 refactor:。開發者應在需要改善程式碼結構（如提取模組、消除重複、移除死代碼、減少巢狀、重新命名）時使用，並與行為變更技能（orch-change-feature / orch-fix-defect）區分。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-refine-code
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說明文件

orch-refine-code 是做什麼的？

角色 · 動作 · 目標：orch · refine · code。這是對 orch-pipeline 中共享引擎的精簡封裝。

使用時機

  • 相同行為，更好的結構：提取模組、消除重複、移除死代碼、減少巢狀、為清晰而重新命名。
  • 與同類技能區分：如果行為有變，則不應使用此技能（應使用 orch-change-feature / orch-fix-defect）。

操作設定

  • 預設規模底線： 標準 — 重構會觸及多個檔案。
  • 階段遮罩： 0 → 2（規劃重構）→ 4（保持綠燈）→ 5 → 6。不撰寫新的行為測試 — 既有的測試套件即為安全網。
  • 第一步（第 4 階段）： 在觸碰程式碼之前，確認相關測試存在且為綠燈；若覆蓋率不足，先新增特徵描寫測試。然後以小幅度步驟進行重構，每步後重新執行測試。

運作方式

  1. 以上述設定執行 orch-pipeline 引擎。
  2. 處理死代碼/重複代碼掃除時，委派給 refactor-cleaner 代理（它會執行 knip / depcheck / ts-prune 並安全移除）。
  3. 在閘門 1（重構規劃）和閘門 2（提交前）停止。
  4. 提交訊息為 refactor: — diff 必須是行為中性的。

範例

 orch-refine-code: 從 poller.py 中提取 NWS HTTP 客戶端
 → 確認測試為綠燈 → 規劃提取  [閘門 1：批准]
 → 小幅步驟移動，過程中測試保持綠燈 → 代碼審查
 → 提交 refactor:  [閘門 2：確認]

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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