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orch-refine-code

Orchestre un refactoring préservant le comportement — vérifie que les tests passent, restructure sans changer le comportement, maintient les tests verts, révise et valide l'engagement. À utiliser lorsque la structure doit s'améliorer mais que le comportement ne doit pas changer.

Qu'est-ce que orch-refine-code ?

orch-refine-code est une compétence agent spécialisée dans le refactoring préservant le comportement. Elle garantit que les tests sont verts avant toute modification, puis restructure le code en petites étapes tout en maintenant les tests verts, et valide l'engagement avec un message `refactor:`. Elle prend en charge les extractions de modules, la suppression de duplication, la suppression de code mort, la réduction d'imbrication et le renommage. Basée sur le moteur partagé `orch-pipeline`, elle introduit une phase de planification (Gate 1) et une phase de pré-engagement (Gate 2) pour un contrôle qualité. Elle délègue les nettoyages de code mort/duplication à l'agent `refactor-cleaner`. Cette compétence est idéale pour améliorer la structure du code sans altérer son comportement. Elle est utilisable dans des environnements comme Claude Code, Cursor, Codex ou tout autre terminal supportant les agents orchestrés.

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

Que fait orch-refine-code ?

Acteur · action · cible : orch · refine · code. Mince surcouche du moteur partagé dans orch-pipeline.

Quand l'utiliser

  • Même comportement, meilleure structure : extraire des modules, supprimer la duplication, éliminer le code mort, réduire l'imbrication, renommer pour clarifier.
  • Distinguer des compétences sœurs : si le comportement est censé changer du tout au tout, ce n'est pas la bonne compétence (orch-change-feature / orch-fix-defect).

Paramètres d'opération

  • Taille minimale par défaut : standard — les restructurations touchent plusieurs fichiers.
  • Masque de phase : 0 → 2 (planifier la restructuration) → 4 (maintenir vert) → 5 → 6. Aucun nouveau test de comportement n'est écrit — la suite existante est le filet de sécurité.
  • Première action (phase 4) : vérifier que les tests pertinents existent et sont verts avant de toucher au code ; si la couverture est faible, ajouter d'abord des tests de caractérisation. Ensuite, restructurer par petites étapes, en relançant les tests après chacune.

Comment ça fonctionne

  1. Exécuter le moteur orch-pipeline avec les paramètres ci-dessus.
  2. Pour les nettoyages de code mort / duplication, déléguer à l'agent refactor-cleaner (il exécute knip / depcheck / ts-prune et supprime en toute sécurité).
  3. S'arrêter à la Porte 1 (plan de restructuration) et à la Porte 2 (pré-engagement).
  4. Engager avec refactor: — le diff doit être neutre en comportement.

Exemple

orch-refine-code: extraire le client HTTP NWS de poller.py
→ vérifier que les tests sont verts → planifier l'extraction  [PORTE 1 : approuver]
→ déplacer par petites étapes, tests verts tout au long → révision de code
→ engager refactor:  [PORTE 2 : confirmer]

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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