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orch-refinar-código

Orquestra una refactorización que preserva el comportamiento: confirmar que las pruebas están en verde, reestructurar sin cambiar comportamiento, mantener las pruebas en verde, revisar y realizar un commit con compuerta. Úsalo cuando la estructura deba mejorar pero el comportamiento no deba cambiar.

¿Qué es orch-refinar-código?

La habilidad **orch-refinar-código** permite refactorizar código preservando su comportamiento original: primero verifica que las pruebas existentes estén en verde, luego reestructura en pasos pequeños (extraer módulos, eliminar duplicación, renombrar, etc.) y ejecuta las pruebas después de cada cambio para garantizar que la funcionalidad no se altere. Es útil para mejorar la estructura interna del código sin riesgo de introducir errores, distinguiéndose de habilidades como `orch-change-feature` o `orch-fix-defect` que sí cambian el comportamiento. Funciona principalmente con el motor `orch-pipeline` y puede delegar tareas de limpieza de código muerto/duplicación al agente `refactor-cleaner`. Se detiene en dos compuertas: una para aprobar el plan de reestructuración y otra antes del commit, el cual se realiza con mensaje `refactor:` para indicar que el diff es neutro en comportamiento.

Compatible con✓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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Documentación

¿Qué hace orch-refinar-código?

Actor · acción · objetivo: orch · refinar · código. Envoltura ligera del motor compartido en orch-pipeline.

Cuándo usarlo

  • Mismo comportamiento, mejor estructura: extraer módulos, eliminar duplicación, eliminar código muerto, reducir anidamiento, renombrar para mayor claridad.
  • Diferenciar de habilidades hermanas: si el comportamiento debe cambiar en absoluto, esta es la habilidad incorrecta (orch-change-feature / orch-fix-defect).

Configuración de operación

  • Límite de tamaño por defecto: estándar — las reestructuraciones tocan múltiples archivos.
  • Máscara de fase: 0 → 2 (planificar reestructuración) → 4 (mantener verde) → 5 → 6. No se escriben nuevas pruebas de comportamiento — el conjunto existente es la red de seguridad.
  • Primer movimiento (fase 4): confirmar que las pruebas relevantes existen y están en verde antes de tocar el código; si la cobertura es delgada, agregar primero pruebas de caracterización. Luego reestructurar en pasos pequeños, volviendo a ejecutar las pruebas después de cada uno.

Cómo funciona

  1. Ejecutar el motor orch-pipeline con la configuración anterior.
  2. Para barridos de código muerto / duplicación, delegar al agente refactor-cleaner (ejecuta knip / depcheck / ts-prune y elimina de forma segura).
  3. Detenerse en la Compuerta 1 (plan de reestructuración) y la Compuerta 2 (pre-commit).
  4. Confirmar como refactor: — el diff debe ser neutro en comportamiento.

Ejemplo

orch-refinar-código: extraer el cliente HTTP de NWS del archivo poller.py
→ confirmar pruebas en verde → planificar extracción  [COMPUERTA 1: aprobar]
→ mover en pasos pequeños, pruebas en verde en todo momento → revisión de código
→ confirmar refactor:  [COMPUERTA 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/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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