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代理工程

以評估優先執行、任務分解與成本感知模型路由,作為代理工程師運作。適用於規劃或執行由 AI 代理從頭到尾負責的工程工作。

代理工程 是什麼?

代理工程技能專為 AI 代理主導實作工作的工程流程而設計,同時讓人類專注於品質與風險控管。此技能的核心是「評估優先循環」,要求先定義能力評估與回歸評估,執行基準測試並記錄失敗特徵,再進行實作,最後重新執行評估以比較差異。任務分解採用「15 分鐘單元規則」,確保每個單元可獨立驗證、具單一主要風險,並有明確的完成條件。模型路由根據任務複雜度分配不同層級:Haiku 用於分類與範本轉換,Sonnet 用於實作與重構,Opus 用於架構設計與根本原因分析。此技能支援任何具備模型路由與評估能力的代理平台(如 Claude Code、Cursor、Codex),並提供會話策略(如緊耦合單元持續同一會話、重大階段切換後開啟新會話)以及審查重點(專注於不變量、邊界條件、安全假設等),同時強調成本紀律,追蹤每次任務的模型、代幣估算、重試次數等指標。

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

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

代理工程 是做什麼的?

使用此技能處理 AI 代理執行大部分實作工作、人類負責品質與風險控管的工程流程。

運作原則

  1. 在執行前定義完成標準。
  2. 將工作分解為代理規模的單元。
  3. 根據任務複雜度路由模型層級。
  4. 透過評估與回歸檢查進行衡量。

評估優先循環

  1. 定義能力評估與回歸評估。
  2. 執行基準測試並記錄失敗特徵。
  3. 執行實作。
  4. 重新執行評估並比較差異。

任務分解

應用 15 分鐘單元規則:

  • 每個單元應可獨立驗證
  • 每個單元應具單一主要風險
  • 每個單元應暴露明確的完成條件

模型路由

  • Haiku:分類、範本轉換、狹隘編輯
  • Sonnet:實作與重構
  • Opus:架構、根本原因分析、多檔案不變量

會話策略

  • 對於緊耦合的單元,持續進行同一個會話。
  • 在重大階段轉換後,開啟新的會話。
  • 在里程碑完成後進行壓縮,不要在主動除錯期間進行。

AI 生成程式碼的審查重點

優先審查:

  • 不變量與邊界情況
  • 錯誤邊界
  • 安全性與驗證假設
  • 隱藏耦合與上線風險

當自動格式化/lint 已強制執行風格時,不要浪費審查循環在僅風格不一致的項目上。

成本紀律

每項任務追蹤:

  • 模型
  • 代幣估算
  • 重試次數
  • 實際耗時
  • 成功/失敗

僅在較低階層級因明確的推理差距而失敗時,才升級模型層級。

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-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.

api-connector-builder

Build a new API connector or provider by matching the target repo

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