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代理工程

以评估为先的执行、任务分解和成本感知模型路由来运作代理工程。在规划或执行由AI代理端到端承担的工程工作时使用。

代理工程 是什么?

Agentic Engineering 技能专为AI代理主导实施、人工负责质量和风险控制的工程工作流设计。其核心原则包括:在执行前定义完成标准,将工作分解为代理可独立处理的单元,根据任务复杂度选择合适的模型层级,并通过评估和回归检查进行度量。该技能强调“评估优先循环”:先定义能力和回归评估,运行基线并捕获失败特征,然后实施代码,最后重新运行评估并对比差异。任务分解遵循15分钟单元规则,每个单元需独立可验证、具有单一主导风险并明确完成条件。模型路由方面,Haiku用于分类和样板转换,Sonnet用于实现和重构,Opus用于架构和根因分析。该技能支持Claude Code等AI代理平台,并提供会话策略(紧密耦合单元继续会话、阶段转换后开启新会话、里程碑完成后压缩会话)和成本追踪(按任务记录模型、令牌估算、重试次数等),帮助用户高效、可控地利用AI代理完成工程任务。

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

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

代理工程 是做什么的?

使用此技能处理AI代理执行大部分实施工作,而人工负责质量和风险控制的工程工作流。

操作原则

  1. 在执行前定义完成标准。
  2. 将工作分解为代理可处理的单元。
  3. 根据任务复杂度路由模型层级。
  4. 通过评估和回归检查进行度量。

评估优先循环

  1. 定义能力评估和回归评估。
  2. 运行基线并捕获失败特征。
  3. 执行实施。
  4. 重新运行评估并比较差异。

任务分解

应用15分钟单元规则:

  • 每个单元应独立可验证
  • 每个单元应具有单一主导风险
  • 每个单元应明确完成条件

模型路由

  • Haiku:分类、样板转换、窄范围编辑
  • Sonnet:实施和重构
  • Opus:架构、根因分析、多文件不变量

会话策略

  • 对于紧密耦合的单元,继续当前会话。
  • 在主要阶段转换后,开始新的会话。
  • 在里程碑完成后压缩,不要在活跃调试期间压缩。

AI生成代码的审查重点

优先审查:

  • 不变量和边界情况
  • 错误边界
  • 安全和认证假设
  • 隐藏耦合和发布风险

当自动格式化/lint已经强制风格时,不要在仅风格分歧上浪费审查周期。

成本纪律

按任务追踪:

  • 模型
  • 令牌估算
  • 重试次数
  • 实际时间
  • 成功/失败

仅当下层模型因明确的推理差距失败时,才升级模型层级。

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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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