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orch-refine-code

Orquestra uma refatoração que preserva o comportamento — confirma que os testes estão verdes, reorganiza sem alterar o comportamento, mantém os testes verdes, revisa e faz o commit com portão. Use quando a estrutura deve melhorar, mas o comportamento não deve mudar.

O que é orch-refine-code?

Esta habilidade (skill) orquestra uma refatoração comportamentalmente neutra para projetos que utilizam pipelines de IA como Claude Code, Cursor ou Codex. Seu objetivo é melhorar a estrutura do código — extraindo módulos, eliminando duplicação, removendo código morto, reduzindo aninhamento e renomeando para clareza — sem nunca alterar o comportamento observável. A habilidade depende de um motor compartilhado (`orch-pipeline`) e segue uma sequência rigorosa: primeiro confirma que os testes relevantes existem e estão verdes, depois planeja a reestruturação, executa mudanças incrementais mantendo os testes sempre verdes, e para em pontos de verificação (Gates) para aprovação manual antes do commit. Ao contrário de habilidades irmãs como `orch-change-feature` ou `orch-fix-defect`, esta é estritamente para melhorias estruturais; se o comportamento precisar mudar, esta é a habilidade errada. O commit final é prefixado com `refactor:` e o diff deve ser neutro em comportamento.

Funciona com✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-refine-code
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Documentação

O que orch-refine-code faz?

Ator · ação · alvo: orch · refine · code. Invólucro fino sobre o motor compartilhado em orch-pipeline.

Quando usar

  • Mesmo comportamento, melhor estrutura: extrair módulos, remover duplicação, eliminar código morto, reduzir aninhamento, renomear para clareza.
  • Distinguir de habilidades irmãs: se o comportamento deve mudar de alguma forma, esta é a habilidade errada (orch-change-feature / orch-fix-defect).

Configurações de operação

  • Tamanho mínimo padrão: standard — reestruturações tocam vários arquivos.
  • Máscara de fase: 0 → 2 (planejar a reestruturação) → 4 (manter verde) → 5 → 6. Nenhum novo teste de comportamento é escrito — a suíte existente é a rede de segurança.
  • Primeiro movimento (fase 4): confirmar que os testes relevantes existem e estão verdes antes de tocar no código; se a cobertura for fina, adicionar testes de caracterização primeiro. Em seguida, reestruturar em pequenos passos, reexecutando os testes após cada um.

Como funciona

  1. Executar o motor orch-pipeline com as configurações acima.
  2. Para varreduras de código morto / duplicação, delegar ao agente refactor-cleaner (ele executa knip / depcheck / ts-prune e remove com segurança).
  3. Parar no Portão 1 (plano de reestruturação) e no Portão 2 (pré-commit).
  4. Fazer o commit como refactor: — o diff deve ser neutro em comportamento.

Exemplo

orch-refine-code: extrair o cliente HTTP do NWS do arquivo poller.py
→ confirmar testes verdes → planejar extração  [PORTÃO 1: aprovar]
→ mover em pequenos passos, testes verdes durante todo o processo → revisão de código
→ commit refactor:  [PORTÃO 2: confirmar]

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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