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Instructions

  • Use existing documents: 使用现有的操作程序、支持脚本或政策文档来创建 LLM 友好的 routines.
  • Prompt agents to break down tasks: 提供更小、更清晰的步骤有助于最大限度地减少歧义, 并帮助模型更好地遵循指令.
  • Define clear actions: 确保 routine 中的每一步都对应一个特定的行动或输出.
  • Capture edge cases: 实际交互通常会产生决策点, 一个健壮的 routine 会预测常见的变化, 并包含关于如何通过条件步骤或分支来处理它们的指令, e.g. 在缺少所需信息时提供替代步骤.
您是 LLM 智能体指令编写专家.
请将以下帮助中心文档转换为一组清晰的指令, 以编号列表形式编写.
该文档将成为 LLM 遵循的政策. 确保没有歧义, 并且指令是以智能体的指示形式编写的.
要转换的帮助中心文档如下 {{help_center_doc}}

How to write a great AGENTS.md lessons from over 2500 repositories:

  1. States a clear role: Defines who the agent is (expert technical writer), what skills it has (Markdown, TypeScript), and what it does (read code, write docs).
  2. Executable commands: Gives AI tools it can run (npm run docs:build and npx markdownlint docs/). Commands come first.
  3. Project knowledge: Specifies tech stack with versions (React 18, TypeScript, Vite, Tailwind CSS) and exact file locations.
  4. Real examples: Shows what good output looks like with actual code. No abstract descriptions.
  5. Three-tier boundaries: Set clear rules using always do, ask first, never do. Prevents destructive mistakes.
tip

Role -> Tool -> Context -> Example -> Boundary

Vibe Coding​

  1. Spec the work:
    • 目标: picking next highest-leverage goal
    • 分解: breaking the work into small and verifiable slice (pull request)
    • 标准: writing acceptance criteria, e.g. inputs, outputs, edge cases, UX constraints
    • 风险: calling out risks up front, e.g. performance hot-spots, security boundaries, migration concerns
  2. Give agents context:
    • 仓库: Repository conventions
    • 组件: Component system, design tokens and patterns
    • 约束: Defining constraints: what not to touch, what must stay backward compatible
  3. Direct agents what, not how:
    • 工具: Assigning right tools
    • 文件: Pointing relevant files and components
    • 约束: Stating explicit guardrails, e.g. don't change API shape, keep this behavior, no new deps
  4. Verification and code review:
    • 正确性 (correctness): edge cases, race conditions, error handling
    • 性能 (performance): N+1 queries, unnecessary re-renders, over-fetching
    • 安全性 (security): auth boundaries, injection, secrets, SSRF
    • 测试 (tests): coverage for changed behaviors
  5. Integrate and ship:
    • Break big work into tasks agents can complete reliably
    • Merge conflicts
    • Verify CI
    • Stage roll-outs
    • Monitor regressions
tip

Spec → Onboard → Direct → Verify → Integrate

System​

OpenAI Codex system prompts:

  • Instructions.
  • Git instructions.
  • AGENTS.md spec.
  • Citations instructions.

Coding​

Writing good AGENTS.md:

  • AGENTS.md should define your project's WHY, WHAT, and HOW.
  • Less is more. Include as few instructions as reasonably possible in the file.
  • Keep the contents of your AGENTS.md concise and universally applicable.
  • Use Progressive Disclosure. Don't tell Agent all the information to know, tell Agent when to needs, how to find and use it.
  • Agent is not a linter. Use linters and code formatters, and use other features like Hooks and Slash Commands.
  • AGENTS.md is the highest leverage point of the harness, so avoid auto-generating it. You should carefully craft its contents for best results.

Pull Request​

GitHub copilot to debug issues faster:

You are an experienced engineer working on this codebase.
Always ground your answers in the linked docs and sources in this space.
Before writing code, produce a 3–5 step plan that includes:

- The goal
- The approach
- The execution steps

Cite the exact files that justify your recommendations.
After I approve a plan, use the Copilot coding agent to propose a PR.

Testing​

Create a test agent for this repository. It should:

- Have the persona of a QA software engineer.
- Write tests for this codebase
- Run tests and analyzes results
- Write to “/tests/” directory only
- Never modify source code or remove failing tests
- Include specific examples of good test structure

Research​

AI agents powered by tricky LLMs prompting: