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ECC 指南

引導使用者瞭解、導航、安裝及選擇 Everything Claude Code 的各項功能。

ECC 指南 是什麼?

此技能「ECC 指南」旨在為使用者提供有關 Everything Claude Code (ECC) 的全面引導。對於需要瞭解 ECC 包含哪些內容、尋找特定技能、命令、代理、鉤子、規則或安裝檔案的使用者,此技能尤為實用。它主要用於新手上路、功能探索、安裝指導以及專案初始化。其核心原則是依據當前文件而非記憶進行回答,因為 ECC 更新頻繁。技能會引導使用者通過目錄與命令(如 catalog.js、install-plan.js)來檢索實際檔案,並以提供具體行動建議的簡潔回覆風格來回應。支援的平台包括 Claude、Cursor、Codex、Gemini、OpenCode 等主要 AI 輔助開發平台。此技能透過檢查倉儲中的實際檔案結構(包括 skills/、commands/、agents/ 等目錄)來運作,提供精確且不依賴過時快取資料的答案。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

ECC 指南 是做什麼的?

當使用者需要協助來理解、導航、安裝或選擇 Everything Claude Code 的各個部分時,使用此技能。

使用時機

當使用者遇到以下情況時,使用此技能:

  • 詢問 ECC 包含哪些內容
  • 需要協助尋找技能、命令、代理、鉤子、規則或安裝設定檔
  • 是倉儲的新手,需要引導路徑
  • 詢問「如何使用 ECC 做 X?」
  • 詢問哪些 ECC 元件適合其專案
  • 需要一個輕量化的解釋來說明命令、技能、代理、鉤子和規則之間的關係
  • 對於安裝路徑、重複安裝、重設/解除安裝或選擇性安裝選項感到困惑

核心原則

從當前檔案中回答,而非依賴記憶。ECC 變化很快,因此硬編碼的目錄計數、功能列表和安裝說明容易過時。

當 ECC 倉儲可用時,在給出具體答案之前,請先檢查相關檔案:

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

針對使用者的問題,使用最少的必要讀取。

倉儲結構

  • README.md:安裝路徑、解除安裝/重設指南、公開定位、常見問答
  • AGENTS.md:貢獻者指南與專案結構
  • agent.yaml:匯出的 gitagent 表面與命令列表
  • commands/:維護的斜線命令相容性墊片
  • skills/*/SKILL.md:可重複使用的工作流程與領域手冊
  • agents/*.md:委派的子代理角色提示
  • rules/:語言與框架規則
  • hooks/README.md, hooks/hooks.json, scripts/hooks/:鉤子行為與安全閘門
  • manifests/install-*.json:選擇性安裝模組、元件、設定檔與目標支援
  • docs/:框架指南、架構說明、翻譯文件、發行說明

回覆風格

直接以答案開頭,然後給出下一步行動。大多數使用者不需要完整的目錄轉儲。

良好的第一回覆架構:

  1. 使用什麼
  2. 為什麼適合
  3. 要檢查的具體檔案或命令
  4. 一個下一步命令或問題

避免:

  • 預設列出每個技能或命令
  • 重複大段的 README 內容
  • 當技能優先的路徑存在時,推薦已淘汰的命令墊片
  • 未經檢查檔案系統就聲稱某個元件存在
  • 當受管理的安裝程式支援該目標時,用複製命令來取代安裝指導

常見任務

新手上路

提供一個簡短選單:

  • 安裝或重設 ECC
  • 為專案挑選技能
  • 理解命令 vs 技能
  • 檢查鉤子與安全行為
  • 執行框架稽核
  • 尋找特定工作流程

指向 README.md 以取得安裝/重設資訊,指向 /project-init 以取得專案特定上手指南。

功能探索

對於「我該用什麼來做 X?」:

  1. 搜尋 skills/、commands/ 和 agents/。
  2. 偏好以技能作為主要的工作流程介面。
  3. 僅在命令是維護中的相容性墊片或使用者明確想要斜線命令行為時,才使用命令。
  4. 當委派有用時,提及代理。

有用的搜尋:

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

安裝指導

使用受管理的安裝路徑:

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

針對特定技能的安裝:

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

警告使用者,除非他們有意想要重複的介面,否則不要疊加外掛安裝與完整的手動/設定檔安裝。

專案初始化

當使用者想要為目標倉儲配置 ECC 時,使用 /project-init。預期流程是:

  1. 從專案檔案偵測技術棧
  2. 解析出一個試運行安裝計畫
  3. 檢查現有的 CLAUDE.md 與設定檔
  4. 應用變更前先詢問
  5. 保持生成的指導精簡且針對特定倉儲

疑難排解

首先詢問目標框架與安裝路徑,然後檢查:

  • 外掛安裝中繼資料
  • .claude/, .cursor/, .codex/, .gemini/, .opencode/, .codebuddy/, .joycode/ 或 .qwen/
  • hooks/hooks.json
  • 安裝狀態檔案
  • 相關的命令/技能檔案

對於倉儲健康狀況,建議執行:

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

輸出範本

簡短建議

使用 <技能或命令>。它適合是因為 <原因>。

標準檔案:<路徑>
驗證方式:<命令>
下一步:<一個具體行動>

搜尋結果

最佳匹配:
- <路徑>:<為何重要>
- <路徑>:<為何重要>

建議:<先使用哪一個及其原因>

安裝計畫摘要

偵測到:<技術棧證據>
目標:<框架>
計畫:<設定檔/模組/技能>
試運行:<命令>
將變更:<路徑>
應用前需批准:<是/否>

相關介面

  • /project-init:基於技術棧的目標倉儲上手指南
  • /harness-audit:確定性就緒狀態卡
  • /skill-health:技能品質審查
  • /skill-create:從本地 git 歷史生成新技能
  • /security-scan:檢查 Claude/OpenCode 設定安全性

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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