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hookify-rules

此技能用於使用者要求建立 hookify 規則、編寫 hook 規則、配置 hookify、新增 hookify 規則,或需要 hookify 規則語法與模式的指導時。

hookify-rules 是什麼?

Hookify 規則是以 YAML 前置元資料 (frontmatter) 為基礎的 Markdown 檔案,用於定義監控模式以及模式匹配時要顯示的訊息。這些規則儲存在 `.claude/hookify.{規則名稱}.local.md` 檔案中,支援 `bash`、`file`、`stop`、`prompt` 與 `all` 等事件類型。透過正規表示式 (regex) 或進階條件 (如 `regex_match`、`contains` 等運算子) 來觸發警告或封鎖操作。此技能廣泛應用於 Claude Code、Cursor、Codex 等 AI 代理開發工具,協助開發者自動檢測危險指令、除錯代碼、安全風險與工作流程強制執行。提供 `/hookify`、`/hookify-list`、`/hookify-configure` 與 `/hookify-help` 等指令來管理規則,並支援 `.gitignore` 以保護本地規則檔案。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/hookify-rules

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

編寫 Hookify 規則

概述

Hookify 規則是以 YAML 前置元資料 (frontmatter) 為基礎的 Markdown 檔案,用於定義要監控的模式以及模式匹配時要顯示的訊息。規則儲存在 .claude/hookify.{規則名稱}.local.md 檔案中。

規則檔案格式

基本結構

---
name: rule-identifier
enabled: true
event: bash|file|stop|prompt|all
pattern: regex-pattern-here
---

當此規則觸發時要顯示給 Claude 的訊息。
可以包含 Markdown 格式、警告、建議等。

前置元資料欄位

欄位必填值說明
name是kebab-case 字串唯一識別碼 (動詞優先:warn-, block-, require-*)
enabled是true/false在不刪除的情況下切換開關
event是bash/file/stop/prompt/all觸發此規則的掛鉤事件
action否warn/blockwarn (預設) 顯示訊息;block 阻止操作
pattern是*regex 字串要匹配的模式 (*或使用 conditions 進行複雜規則)

進階格式 (多個條件)

---
name: warn-env-api-keys
enabled: true
event: file
conditions:
  - field: file_path
    operator: regex_match
    pattern: \.env$
  - field: new_text
    operator: contains
    pattern: API_KEY
---

你正在將 API 金鑰新增到 .env 檔案中。請確保此檔案已加入 .gitignore!

依事件分類的條件欄位:

  • bash:command
  • file:file_path、new_text、old_text、content
  • prompt:user_prompt

運算子: regex_match、contains、equals、not_contains、starts_with、ends_with

所有條件必須都符合才能觸發規則。

事件類型指南

bash 事件

匹配 Bash 指令模式:

  • 危險指令:rm\s+-rf、dd\s+if=、mkfs
  • 權限提升:sudo\s+、su\s+
  • 權限問題:chmod\s+777

file 事件

匹配編輯/寫入/多重編輯操作:

  • 除錯代碼:console\.log\(、debugger
  • 安全風險:eval\(、innerHTML\s*=
  • 敏感檔案:\.env$、credentials、\.pem$

stop 事件

完成檢查與提醒。模式 .* 永遠匹配。

prompt 事件

匹配使用者提示內容以強制執行工作流程。

模式編寫技巧

正規表示式基礎

  • 跳脫特殊字元:. 變 \.、( 變 \(
  • \s 空白字元、\d 數字、\w 字詞字元
  • + 一個或多個、* 零個或多個、? 可選
  • | OR 運算子

常見陷阱

  • 過於寬泛:log 會匹配

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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