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 | $0.02556 |
| Opus 5 | $0.00020 | $0.01278 |
| Sonnet 5 | $0.00008 | $0.00511 |
| Haiku 4.5 | $0.00004 | $0.00256 |
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 12d 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 — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
上下文工程
概览
在正确的时间给 agent 提供正确的信息。上下文是影响 agent 输出质量的最大杠杆：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，以及这些信息如何组织。
何时使用
- 开始新的编码会话
- Agent 输出质量正在下降（使用错误模式、幻觉 API、忽略约定）
- 在代码库的不同部分之间切换
- 为 AI 辅助开发设置新项目
- Agent 没有遵循项目约定
上下文层级
从最持久到最临时来组织上下文：
┌─────────────────────────────────────┐
│ 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：规则文件
创建一个能跨会话保留的规则文件。这是你能提供的最高杠杆上下文。
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]
其他工具的等效文件：
.cursorrules或.cursor/rules/*.md（Cursor）.windsurfrules（Windsurf）.github/copilot-instructions.md（GitHub Copilot）AGENTS.md（OpenAI Codex）
第 2 层：规格与架构
开始一个功能时，加载相关的规格章节。如果只涉及其中一节，不要加载整份规格。
有效：“这是我们规格中的认证章节：[auth spec content]”
浪费：“这是我们完整的 5000 字规格：[full spec]”（实际只在做 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.
- 12d 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 (31 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 2,556 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 2026-08-30.
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.
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…
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
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.
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.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.