claude-plugins / context-engineering
CLAUDE.md2 starsChanged 5 months agoMITView on GitHub
Agent Skills for Context Engineering — an open collection of 14 Agent Skills teaching context engineering principles for production AI agent systems. Skills are platform-agnostic (Claude Code, Cursor, GitHub Copilot, any Open Plugins-conformant tool). Context engineering is the discipline of curating everything that enters a model's context window (system prompts, tool definitions, retrieved documents, message history, tool outputs) to maximize signal within limited attention budget. No top-level build system. Individual example projects have their own tooling: When creating or editing…
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- LiamClarke1/claude-skillsword for word, first seen Apr 16, 2026
Other versions1 project uses a version of this.
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- LiamClarke1/claude-skills★ 0100% same
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How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repositoryOne other file it gives its agents.
The file
We split this CLAUDE.md into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 24
No top-level build system.
- Distinctive: line 56
When creating or editing skills:
- Distinctive: line 65
Update root README.md: when adding new skills
- Very specific: line 66
Update marketplace/plugin manifests: when adding skills (.claude-plugin/marketplace.json, .plugin/plugin.json)
- Very specific: line 70
All 14 skills are distributed as a single plugin (context-engineering) in the marketplace manifest.
≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
Agent Skills for Context Engineering — an open collection of 14 Agent Skills teaching context engineering principles for production AI agent systems. Skills are platform-agnostic (Claude Code, Cursor, GitHub Copilot, any Open Plugins-conformant tool).
Context engineering is the discipline of curating everything that enters a model's context window (system prompts, tool definitions, retrieved documents, message history, tool outputs) to maximize signal within limited attention budget.
Repository Structure
skills/— 14 skill directories, each containing aSKILL.mdwith YAML frontmatter (name,description) and optionalreferences/andscripts/subdirectoriesexamples/— 5 complete demonstration projects (digital-brain-skill, llm-as-judge-skills, book-sft-pipeline, x-to-book-system, interleaved-thinking)docs/— Research materials and reference documentationresearcher/— Research output examplestemplate/SKILL.md— Canonical skill template (use when creating new skills)SKILL.md(root) — Collection-level metadata and skill map.claude-plugin/marketplace.json— Claude Code marketplace manifest (5 bundled plugins).plugin/plugin.json— Open Plugins format manifest (v2.0.0)
Build & Test Commands
No top-level build system. Individual example projects have their own tooling:
examples/llm-as-judge-skills (TypeScript, Node >= 18)
cd examples/llm-as-judge-skills
npm install
npm run build # tsc
npm test # vitest (19 tests)
npm run lint # eslint
npm run format # prettier
npm run typecheck # tsc --noEmitexamples/interleaved-thinking (Python >= 3.10)
cd examples/interleaved-thinking
pip install -e ".[dev]"
pytest # pytest + pytest-asyncio
ruff check . # linting (100 char line length)examples/digital-brain-skill (Node.js)
cd examples/digital-brain-skill
npm run setup
npm run weekly-review
npm run content-ideas
npm run stale-contactsSkill Authoring Rules
When creating or editing skills:
- SKILL.md must stay under 500 lines — move detailed content to
references/directory - YAML frontmatter is required — must include
nameanddescriptionfields - Folder naming: lowercase with hyphens (e.g.,
context-fundamentals) - Write in third person — descriptions are injected into system prompts; inconsistent POV causes discovery issues
- Platform-agnostic — no vendor-locked examples or platform-specific tool names without abstraction
- Token-conscious — challenge each paragraph: "Does Claude really need this?" Assume advanced audience
- Include a Gotchas section — experience-derived failure modes are the highest-signal content in any skill
- Update root README.md when adding new skills
- Update marketplace/plugin manifests when adding skills (
.claude-plugin/marketplace.json,.plugin/plugin.json)
Plugin Architecture
All 14 skills are distributed as a single plugin (context-engineering) in the marketplace manifest. This avoids cache duplication — Claude Code caches each plugin's source directory separately, so multiple plugins pointing to source: "./" would each cache a full copy of the repo.
Progressive disclosure pattern: only skill names/descriptions load at startup; full content loads on activation.
Key Design Principles
- Context quality over quantity — attention scarcity and lost-in-middle phenomenon mean more context is not always better
- Sub-agents isolate context — they exist to manage attention budget, not simulate org roles
- Skills reference each other — use plain text skill names (not links) in Integration sections to avoid cross-directory reference issues
- Examples use Python pseudocode — conceptual demonstrations that work across environments, not production-ready implementations
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

