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bucle-autónomo-continuo

Patrones para bucles autónomos continuos de agentes con puertas de calidad, evaluaciones y controles de recuperación. Útil al ejecutar un bucle de agente que debe autoverificarse, pasar por evaluaciones y recuperarse de fallos.

¿Qué es bucle-autónomo-continuo?

Este skill proporciona un conjunto de patrones y prácticas para implementar bucles autónomos continuos de agentes de IA (como Claude Code, Cursor o Codex). Está diseñado para escenarios donde un agente debe ejecutarse en un ciclo continuo, autoverificando su trabajo, aplicando puertas de calidad (quality gates) para decidir si avanzar o retroceder, y recuperándose automáticamente de fallos. El skill estandariza el nombre del bucle a 'continuous-agent-loop' a partir de la versión 1.8+, reemplazando el anterior 'autonomous-loops' pero manteniendo compatibilidad. Incluye un flujo de selección para elegir el patrón de bucle adecuado según las necesidades del proyecto (control estricto de CI/PR, descomposición RFC, generación paralela exploratoria o secuencial por defecto). También detalla una pila de producción recomendada que combina descomposición RFC, puertas de calidad, bucles de evaluación y persistencia de sesión. Finalmente, documenta modos de fallo comunes y pasos de recuperación, como congelar el bucle, ejecutar una auditoría, reducir el alcance y reproducir con criterios de aceptación explícitos.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/continuous-agent-loop
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Documentación

Bucle Autónomo Continuo

Este es el nombre canónico del bucle a partir de la versión 1.8+. Reemplaza a autonomous-loops manteniendo compatibilidad durante una versión.

Flujo de Selección de Bucle

Inicio
  |
  +-- ¿Necesitas control estricto de CI/PR? -- sí --> continuous-pr
  |
  +-- ¿Necesitas descomposición RFC? -- sí --> rfc-dag
  |
  +-- ¿Necesitas generación paralela exploratoria? -- sí --> infinite
  |
  +-- por defecto --> sequential

Patrón Combinado

Pila de producción recomendada:

  1. Descomposición RFC (ralphinho-rfc-pipeline)
  2. Puertas de calidad (plankton-code-quality + /quality-gate)
  3. Bucle de evaluación (eval-harness)
  4. Persistencia de sesión (nanoclaw-repl)

Modos de Fallo

  • Churn del bucle sin progreso medible
  • Reintentos repetidos con la misma causa raíz
  • Bloqueos en la cola de merge
  • Desviación de costos por escalada sin límites

Recuperación

  • Congela el bucle
  • Ejecuta /harness-audit
  • Reduce el alcance a la unidad que falla
  • Reproduce con criterios de aceptación explícitos

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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