addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.
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/addyosmani/agent-skills/context-engineeringnpx skills add addyosmani/agent-skills --skill context-engineeringgit clone --depth 1 https://github.com/addyosmani/agent-skillsWrote 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/addyosmani/agent-skills/context-engineering)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/context-engineering"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/context-engineering.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.02342 |
| Opus 5 | $0.00022 | $0.01171 |
| Sonnet 5 | $0.00009 | $0.00468 |
| Haiku 4.5 | $0.00004 | $0.00234 |
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 5d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- context-engineering — 100% identical, 4 lines differ
- context-engineering — 100% identical, 0 lines differ
- context-engineering — 100% identical, 52 lines differ
- context-engineering — 100% identical, 0 lines differ
- context-engineering — 100% identical, 0 lines differ
- context-engineering — 100% identical, 0 lines differ
- context-engineering — 100% identical, 0 lines differ
- context-engineering — 100% identical, 0 lines differ
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.
Context Engineering
Overview
Feed agents the right information at the right time. Context is the single biggest lever for agent output quality — too little and the agent hallucinates, too much and it loses focus. Context engineering is the practice of deliberately curating what the agent sees, when it sees it, and how it's structured.
When to Use
- Starting a new coding session
- Agent output quality is declining (wrong patterns, hallucinated APIs, ignoring conventions)
- Switching between different parts of a codebase
- Setting up a new project for AI-assisted development
- The agent is not following project conventions
The Context Hierarchy
Structure context from most persistent to most transient:
┌─────────────────────────────────────┐
│ 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
└─────────────────────────────────────┘
Level 1: Rules Files
Create a rules file that persists across sessions. This is the highest-leverage context you can provide.
CLAUDE.md (for 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]
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.
- 5d ago First seen · 290 lines · 43 tokens per session scan A ff9d4e5706bd
context-engineering is a skill published in the GitHub repository addyosmani/agent-skills (92,284 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 2,342 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
优化 agent 上下文设置。当开始新会话、agent 输出质量下降、在任务之间切换，或需要为项目配置规则文件和上下文时使用。.
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.
memory-manager
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
project-memory-keeper
Analyzes the last session to log progress, extract lessons learned, and save them to the project memory instructions.
self-evolving-memory-graph
Grants the AI long-term episodic memory. The agent autonomously documents the user's coding preferences, past mistakes to avoid, and architectural decisions into a persistent learning graph.