Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/233i/agent-skills/context-engineeringnpx skills add 233i/agent-skills --skill context-engineeringgit clone --depth 1 https://github.com/233i/agent-skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00043 | $0.02643 |
| Opus 5 | $0.00022 | $0.01321 |
| Sonnet 5 | $0.00009 | $0.00529 |
| Haiku 4.5 | $0.00004 | $0.00264 |
Grade A, and why
context-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
上下文工程
概览
在正确的时间，把正确的信息喂给 agent。上下文是影响 agent 输出质量的最大杠杆之一：给得太少，它会幻觉；给得太多，它会失焦。Context engineering 就是有意识地策划 agent 看到什么、什么时候看到，以及这些信息如何组织。
何时使用
- 开启新的编码会话
- Agent 输出质量下降，例如模式不对、API 幻觉、忽视项目约定
- 在代码库不同区域之间切换
- 为 AI 辅助开发搭建新项目
- Agent 没有遵循项目约定
上下文层级
把上下文从最持久到最临时进行分层：
┌─────────────────────────────────────┐
│ 1. Rules Files (CLAUDE.md, etc.) │ ← 始终加载，项目级
├─────────────────────────────────────┤
│ 2. Spec / Architecture Docs │ ← 按功能 / 会话加载
├─────────────────────────────────────┤
│ 3. Relevant Source Files │ ← 按任务加载
├─────────────────────────────────────┤
│ 4. Error Output / Test Results │ ← 按迭代加载
├─────────────────────────────────────┤
│ 5. Conversation History │ ← 持续累积，可压缩
└─────────────────────────────────────┘
第 1 层：规则文件
创建一个能跨会话持续存在的规则文件。这是你能提供的最高杠杆上下文之一。
CLAUDE.md（用于 Claude Code）：
# Project: [Name]
## Tech Stack
- React 18, TypeScript 5, Vite, Tailwind CSS 4
- Node.js 22, Express, PostgreSQL, Prisma
## Commands
- Build: `npm run build`
- Test: `npm test`
- Lint: `npm run lint --fix`
- Dev: `npm run dev`
- Type check: `npx tsc --noEmit`
## Code Conventions
- Functional components with hooks (no class components)
- Named exports (no default exports)
- colocate tests next to source: `Button.tsx` → `Button.test.tsx`
- Use `cn()` utility for conditional classNames
- Error boundaries at route level
## Boundaries
- Never commit .env files or secrets
- Never add dependencies without checking bundle size impact
- Ask before modifying database schema
- Always run tests before committing
## Patterns
[One short example of a well-written component in your style]
其他工具的等价文件：
.cursorrules或.cursor/rules/*.md（Cursor）.windsurfrules（Windsurf）.github/copilot-instructions.md（GitHub Copilot）AGENTS.md（OpenAI Codex）
第 2 层：Spec 与架构文档
开始一个功能时，只加载相关的 spec 片段。不要因为手里有完整 spec，就整个全塞进去。
高效： “这是我们 spec 里的认证章节：[auth spec content]”
浪费： “这是我们整份 5000 字 spec：[full spec]”，但当前只是在做 auth
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 288 lines · 43 tokens per session scan A 4d78dea7b68e
context-engineering is a skill published in the GitHub repository 233i/agent-skills (6 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 2,643 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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