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持续智能体循环

用于持续自主智能体循环的模式,包含质量门控、评估和恢复控制。适用于需要自我检查、基于评估进行门控以及从故障中恢复的智能体循环场景。

持续智能体循环 是什么?

该技能(`continuous-agent-loop`)为构建持续自主运行的 AI 智能体循环提供了一套标准化模式,重点关注质量保证与故障恢复。它通过明确的循环选择流程,帮助开发者根据具体需求(如 CI/PR 控制、RFC 分解、并行探索)选择最合适的循环策略(`continuous-pr`、`rfc-dag`、`infinite`、`sequential`)。技能推荐了生产级组合方案,包括使用 RFC 流水线进行任务分解、集成代码质量门控(`quality-gates`)进行质量检查、使用评估框架(`eval-harness`)进行循环内评估,以及利用会话持久化工具(`nanoclaw-repl`)保持状态。它还能识别并处理循环空转、重复错误等典型故障模式,并通过冻结循环、执行审计、缩小范围并基于明确验收标准重放等方式进行恢复。该技能适用于 v1.8+ 版本,并向后兼容旧版 `autonomous-loops`。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/continuous-agent-loop

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文档

持续智能体循环 是做什么的?

这是 v1.8+ 版本的规范循环技能名称。它取代了 autonomous-loops,同时在一个版本内保持兼容。

循环选择流程

开始
  |
  +-- 需要严格的 CI/PR 控制?-- 是 --> continuous-pr
  |
  +-- 需要 RFC 分解?-- 是 --> rfc-dag
  |
  +-- 需要探索性并行生成?-- 是 --> infinite
  |
  +-- 默认 --> sequential

组合模式

推荐的生产环境组合:

  1. RFC 分解 (ralphinho-rfc-pipeline)
  2. 质量门控 (plankton-code-quality + /quality-gate)
  3. 评估循环 (eval-harness)
  4. 会话持久化 (nanoclaw-repl)

故障模式

  • 循环空转而无实质性进展
  • 使用相同根本原因重复重试
  • 合并队列停滞
  • 因无限制升级导致成本偏离

恢复

  • 冻结循环
  • 运行 /harness-audit
  • 将范围缩小到失败的单元
  • 根据明确的验收标准重放

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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