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ECC 指南

引导用户了解和使用 Everything Claude Code (ECC)。

ECC 指南 是什么?

该技能旨在帮助开发者理解、导航、安装和选择 Everything Claude Code (ECC) 的各个组件。它就像一个智能向导,能够根据用户的问题(例如“ECC 包含什么?”、“如何做 X?”、“哪个组件适合我的项目?”)提供精确且基于当前文件系统状态的回答。该技能强调从实际代码库中检索最新信息,避免使用陈旧的硬编码数据,并推荐使用 `catalog.js`、`install-plan.js` 等脚本来动态获取技能、命令、代理的列表和安装方案。它支持多种 AI 编码平台(如 Claude, Cursor, Codex 等),并详细描述了如何通过托管安装路径、项目初始化(`/project-init`)和工作流发现来高效地使用 ECC。其核心原则是“从当前文件中回答”,确保给出的建议始终是最新且可操作的。

兼容平台✓Claude Code✓Codex CLI✓Cursor✓Gemini CLI✓OpenCode
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/ecc-guide

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

ECC 指南 是做什么的?

当用户需要帮助理解、导航、安装或选择 Everything Claude Code 的组件时,请使用本技能。

使用时机

当用户:

  • 询问 ECC 包含哪些内容
  • 需要帮助查找技能、命令、代理、钩子、规则或安装配置
  • 是仓库新手,需要引导路径
  • 询问“如何使用 ECC 做 X?”
  • 询问哪些 ECC 组件适合其项目
  • 需要一个关于命令、技能、代理、钩子和规则如何相互关联的简洁解释
  • 对安装路径、重复安装、重置/卸载或选择性安装选项感到困惑

核心原则

基于当前文件回答,而非记忆。ECC 变化很快,硬编码的目录计数、功能列表和安装说明很快就会过时。

当 ECC 仓库可用时,在给出具体答案前,先检查相关文件:

node scripts/ci/catalog.js --json
find skills -maxdepth 2 -name SKILL.md | sort
find commands -maxdepth 1 -name '*.md' | sort
find agents -maxdepth 1 -name '*.md' | sort
node scripts/install-plan.js --list-profiles
node scripts/install-plan.js --list-components --json

使用满足用户问题所需的最少读取操作。

仓库地图

  • README.md: 安装路径、卸载/重置指南、公开定位、常见问题
  • AGENTS.md: 贡献者指南和项目结构
  • agent.yaml: 导出的 gitagent 界面和命令列表
  • commands/: 维护的斜杠命令兼容层
  • skills/*/SKILL.md: 可重用工作流和领域剧本
  • agents/*.md: 委派子代理的角色提示
  • rules/: 语言和工具规则
  • hooks/README.md, hooks/hooks.json, scripts/hooks/: 钩子行为和安全性确保
  • manifests/install-*.json: 选择性安装模块、组件、配置和目标支持
  • docs/: 工具指南、架构说明、翻译文档、发布文档

回复风格

先给出答案,再给出下一步操作。大多数用户不需要完整的目录转储。

良好的首要回复结构:

  1. 使用什么
  2. 为什么它适合
  3. 要检查的确切文件或命令
  4. 一个下一步命令或问题

避免:

  • 默认列出每个技能或命令
  • 重复大段 README 内容
  • 在已有技能优先路径时推荐已退役的命令兼容层
  • 未经检查文件系统就声称某个组件存在
  • 在托管安装程序支持目标平台时,用手动复制命令替换安装指南

常见任务

新用户入门

提供一个简短菜单:

  • 安装或重置 ECC
  • 为项目挑选技能
  • 理解命令 vs 技能
  • 检查钩子和安全行为
  • 运行工具审计
  • 查找特定工作流

安装/重置请参考 README.md,特定项目的入门请参考 /project-init。

功能发现

对于“我应该用 X 做什么?”:

  1. 搜索 skills/, commands/, 和 agents/。
  2. 优先将技能作为主要工作流界面。
  3. 仅在命令是维护的兼容层或用户明确需要斜杠命令行为时使用命令。
  4. 当委派有用时提及代理。

有用的搜索:

rg -n "<query>" skills commands agents docs
find skills -maxdepth 2 -name SKILL.md | sort

安装指南

使用托管安装路径:

node scripts/install-plan.js --list-profiles
node scripts/install-plan.js --profile minimal --target claude --json
node scripts/install-apply.js --profile minimal --target claude --dry-run

针对特定技能安装:

node scripts/install-plan.js --skills <skill-id> --target claude --json
node scripts/install-apply.js --skills <skill-id> --target claude --dry-run

警告用户不要混合使用插件安装和完整的手动/配置安装,除非他们有意想要重复的界面。

项目初始化

当用户希望为目标仓库配置 ECC 时,使用 /project-init。预期的顺序是:

  1. 从项目文件检测技术栈
  2. 解析一个模拟安装计划
  3. 检查现有的 CLAUDE.md 和设置文件
  4. 应用前询问确认
  5. 保持生成的指南简洁并与仓库特定相关

故障排除

首先询问目标工具和安装路径,然后检查:

  • 插件安装元数据
  • .claude/, .cursor/, .codex/, .gemini/, .opencode/, .codebuddy/, .joycode/, 或 .qwen/
  • hooks/hooks.json
  • 安装状态文件
  • 相关的命令/技能文件

对于仓库健康检查,建议:

npm run harness:audit -- --format text
npm run observability:ready
npm test

输出模板

简短建议

使用 <技能或命令>。因为它 <理由>。

规范文件:<路径>
使用以下命令验证:<命令>
下一步:<一个具体操作>

搜索结果

最佳匹配:
- <路径>: <为什么重要>
- <路径>: <为什么重要>

建议:<首先使用哪个以及为什么>

安装计划摘要

检测到:<技术栈证据>
目标:<工具>
计划:<配置/模块/技能>
模拟运行:<命令>
将更改:<路径>
应用前需要批准:<是/否>

相关界面

  • /project-init: 针对目标仓库的技术栈感知入门计划
  • /harness-audit: 确定性准备就绪记分卡
  • /skill-health: 技能质量审查
  • /skill-create: 从本地 git 历史生成新技能
  • /security-scan: 检查 Claude/OpenCode 配置安全性

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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