Communitygithub.com

inherit-legacy-style

Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent

inherit-legacy-style 是什麼?

此技能旨在防止 AI 在維護舊有專案時產生程式碼風格偏移。它會自動掃描程式碼庫,從檔案結構、狀態與控制流程、基礎設施以及錯誤處理這四個元架構維度中,發掘隱含的慣例。接著,它會以一次一個的方式與使用者協商解決衝突,並將共識紀錄到一份可強制執行的 .ai-style-rules.md 檔案中。此技能完全與語言和框架無關,適用於任何類型的專案,並支援首次完整掃描與增量偵測兩種模式。

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

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

繼承舊有風格

防止 AI 在舊有專案中產生程式碼風格偏移,透過掃描程式碼庫在 4 個元架構維度中的隱含慣例,逐一與使用者解決衝突,並將共識凝聚成一份可強制執行的 .ai-style-rules.md。完全語言與框架無關。

何時啟用

  • 使用者輸入 /inherit-legacy-style
  • 使用者提到將 AI 引入一個手寫的舊有專案
  • 使用者擔心 AI 生成的程式碼會偏離現有的專案慣例
  • 使用者想要提取並整理專案的隱含編碼規則

使用時機

當你需要保留舊有專案風格並防止 AI 風格偏移時使用此技能。請參閱上方何時啟用以了解觸發條件。

前置需求

  • Git(建議;非 Git 專案會回退使用檔案時間戳記進行增量模式)
  • 對專案根目錄的讀取/寫入權限(會產生 .ai-style-rules.md 和選擇性的 CLAUDE.md)

工作流程

步驟 0 — 自動偵測模式

在專案根目錄靜默檢查 .ai-style-rules.md 是否存在:

檔案存在?模式
否分支 A — 首次完整掃描
是分支 B — 增量嗅探

用一行文字宣布模式並繼續 — 永遠不要讓使用者選擇。

分支 A — 首次完整掃描

1. 衡量規模,選擇掃描層級

git ls-files | grep -cE '\.(js|ts|jsx|tsx|vue|py|go|rs|java|kt|rb|php|cs|swift|c|cpp|h)$'
層級原始檔案數策略
小型≲ 50完整細讀每個原始檔
中型50–500基礎設施層 = 完整讀取;業務層 = 每個維度取 2–3 個取樣
大型≳ 500嚴格的取樣 + 預算上限;先彙整 --stat 摘要,再進行目標式讀取

2. 從 4 個維度掃描

  1. 檔案結構 — 檔案內的宣告順序(匯入 → 類型 → 主要邏輯 → 輔助函式 → 匯出)
  2. 狀態與控制流程 — 非同步狀態、分頁、旗標的命名慣例
  3. 基礎設施 — 跨橫切面工具(攔截器、格式化器、中介軟體)的存放位置
  4. 錯誤處理 — try/catch 與全域攔截器與 Result 回傳的取捨;null 檢查習慣

3. 應用訊號閾值的噪聲降低

在打斷使用者之前,先評估訊號強度:

  • 弱訊號 → 自動抑制:少數派 < 5% 且數量 < 10 → 多數派獲勝,少數派列入禁止事項
  • 強訊號 → 需要詳細審查:幾乎對半分,或是在核心維度上有語義分歧
  • 小專案例外:原始檔 ≲ 50,

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.

相關技能