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継続エージェントループ

品質ゲート、評価、回復制御を備えた継続的で自律的なエージェントループのパターン。自己チェック、評価によるゲート、障害からの回復が必要なエージェントループを実行する際に使用します。

継続エージェントループ とは？

このスキルは、エージェントが自律的にタスクを繰り返し実行する「継続的エージェントループ」の設計パターンを提供します。v1.8以降の標準ループスキルであり、従来の`autonomous-loops`の後継として、互換性を保ちながら拡張された機能を提供します。主な特徴は、ループ選択フローを通じて、CI/PR制御が必要な場合は`continuous-pr`、RFC分解が必要な場合は`rfc-dag`、探索的並列生成が必要な場合は`infinite`、デフォルトは`sequential`というように、ユースケースに適したループパターンを選択できる点です。また、RFC分解、品質ゲート、評価ループ、セッション永続化を組み合わせた推奨プロダクションスタックを提供し、ループチャーン、繰り返しの再試行、マージキューの停滞、コストドリフトといった障害モードに対処するための回復手順も含んでいます。Claude CodeやCursor、CodexなどのAIエージェントプラットフォームで利用でき、品質を自動チェックしながら効率的にタスクを進めたいエンジニアに特に有用です。

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/continuous-agent-loop
オリジナルを見る→すべてのスキルを見る

お気に入りのAIに質問する

このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

ドキュメント

継続エージェントループ は何をしますか？

これは v1.8 以降の標準ループスキル名です。autonomous-loops を置き換えるもので、1リリースの間互換性を維持します。

ループ選択フロー

開始
  |
  +-- 厳格なCI/PR制御が必要ですか？ -- はい --> continuous-pr
  |
  +-- RFC分解が必要ですか？ -- はい --> rfc-dag
  |
  +-- 探索的並列生成が必要ですか？ -- はい --> infinite
  |
  +-- デフォルト --> sequential

結合パターン

推奨プロダクションスタック:

  1. RFC分解 (ralphinho-rfc-pipeline)
  2. 品質ゲート (plankton-code-quality + /quality-gate)
  3. 評価ループ (eval-harness)
  4. セッション永続化 (nanoclaw-repl)

障害モード

  • 測定可能な進捗を伴わないループチャーン
  • 同じ根本原因での繰り返し再試行
  • マージキューの停滞
  • 無制限のエスカレーションによるコストドリフト

回復

  • ループをフリーズ
  • /harness-audit を実行
  • スコープを失敗している単体に縮小
  • 明示的な受入基準で再実行

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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