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 skills add vinvcn/addyosmani-agent-skills-zh --skill context-engineeringgit clone --depth 1 https://github.com/vinvcn/addyosmani-agent-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/context-engineering)<a href="https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/context-engineering"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/context-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/context-engineering"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00040 | $0.03699 |
| Opus 5.5 | $0.00016 | $0.01480 |
| Sonnet 5.5 | $0.00008 | $0.00740 |
| Haiku 4.5 | $0.00004 | $0.00370 |
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 1mo 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 — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
概述
在正确的时间给 agent 提供正确的信息。上下文是影响 agent 输出质量的最大单一杠杆:太少,agent 会幻觉;太多,它会失去焦点。上下文工程就是有意识地策划 agent 看到什么、何时看到,以及内容如何组织。
何时使用
- 开始新的编码会话
- Agent 输出质量正在下降(模式错误、幻觉 API、忽略约定)
- 在代码库的不同部分之间切换
- 为新项目配置 AI 辅助开发
- Agent 没有遵循项目约定
上下文层级
按从最持久到最短暂的顺序组织上下文:
┌─────────────────────────────────────┐
│ 1. Rules Files (CLAUDE.md, etc.) │ ← Always loaded, project-wide
├─────────────────────────────────────┤
│ 2. Spec / Architecture Docs │ ← Loaded per feature/session
├─────────────────────────────────────┤
│ 3. Relevant Source Files │ ← Loaded per task
├─────────────────────────────────────┤
│ 4. Error Output / Test Results │ ← Loaded per iteration
├─────────────────────────────────────┤
│ 5. Conversation History │ ← Accumulates, compacts
└─────────────────────────────────────┘
第 1 层:Rules 文件
创建一个跨会话持久存在的 rules 文件。这是你能提供的杠杆率最高的上下文。
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:Specs 与架构
开始一个 feature 时加载相关的 spec 章节。如果只有一节适用,就不要加载整份 spec。
高效: "这是我们 spec 中的认证章节:[auth spec content]"
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.
- 1mo ago Changed · +64 lines 4f2ecd5c7c67
- 1mo ago First seen · 290 lines · 40 tokens per session scan A 3eb4d236c225
context-engineering is a skill published in the GitHub repository vinvcn/addyosmani-agent-skills-zh (44 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 3,699 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
context-loading
Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.
handoff
Compact the current conversation into a handoff document for a fresh agent.
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.