Agent Skills for Context Engineering: Open Source Mastery
Agent Skills for Context Engineering: Open Source Mastery
Introduction
AI agents are growing from simple scripts to complex, production‑grade systems that orchestrate multiple tools, memories, and sub‑agents. Behind every high‑performing agent lies one critical piece of knowledge: how to manage the small, but precious, context window of a large language model. The newly released Agent Skills for Context Engineering GitHub repository tackles that challenge head‑on. It ships a library of reusable, platform‑agnostic “skills” that cover everything from context fundamentals to advanced multi‑agent orchestration.
The project is fully open source, MIT‑licensed, and has already attracted over 7.2 k stars and a community of contributors. Whether you’re a researcher, developer, or hobbyist looking to build intelligent agents without reinventing the wheel, this repository gives you a hands‑on, plug‑and‑play toolkit.
What is Context Engineering?
Traditional prompt engineering focuses on crafting a single input prompt. Context engineering, by contrast, is the science of curating all content that fills a model’s attention budget:
- System instructions
- Tool definitions
- Retrieved documents
- Conversation history
- Tool outputs
In large, complex agents, simply stacking more information defeats the model. Tokens that don’t contribute valuable signals can clutter the attention mechanism, leading to phenomena such as:
- Lost‑in‑the‑middle – mid‑session context fades
- Attention scarcity – the model ignores peripheral but useful inputs
- U‑shaped attention curves – token importance dips in the center of a long context
The skills in this repo give you tried‑and‑tested strategies to design a compact, high‑signal context that maximizes the model’s effectiveness while keeping token costs low.
Skill Highlights
| Skill Category | Key Skills | What it helps you do |
|---|---|---|
| Foundational | context-fundamentals, context-degradation, context-compression |
Understand the anatomy of context, spot failures, and compress long sessions. |
| Architectural | multi-agent-patterns, memory-systems, tool-design, filesystem-context, hosted-agents |
Build robust multi‑agent systems, design memory architectures, and offload context to files or hosted agents. |
| Operational | context-optimization, evaluation, advanced-evaluation |
Apply caching, masking, and create evaluation frameworks, including LLM‑as‑a‑Judge setups. |
| Development | project-development |
Plan an end‑to‑end LLM project, from ideation to deployment. |
| Cognitive Architecture | bdi-mental-states |
Translate RDF context into agent mental states using BDI ontology patterns, enhancing explainability. |
The skills are purposely platform agnostic: they work out‑of‑the‑box with Claude Code, Cursor, Codex, and any framework that supports custom instructions or plug‑ins.
How to Use It in Claude Code
- Add the repository as a plugin source:
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering - Browse and install one or more skill packages. For example, to get all foundational skills:
You can also install individual modules such as
/plugin install context-engineering-fundamentals@context-engineering-marketplacemulti-agent-patternsoradvanced-evaluation.
The installed skills automatically register with Claude Code’s skill registry, making them available as “triggers” (e.g., “optimize context”).
Real‑World Examples
The repository ships a fully‑featured examples folder that includes production‑ready designs:
- Digital Brain Skill – A personal operating‑system for creators that demonstrates six modules (identity, content, knowledge, network, operations, agents) and uses skills such as
context-optimizationandmemory-systems. - X‑to‑Book System – A multi‑agent pipeline that monitors social media, aggregates daily stories, and generates synthetic “books” using
multi-agent-patternsandevaluation. - LLM‑as‑Judge Skills – A TypeScript test harness that applies rubric‑based scoring, pair‑wise comparisons, and bias mitigation.
- Book‑SFT Pipeline – A low‑cost (≈$2) LoRA training workflow that teaches small models to replicate an author’s style, employing
context-compressionandproject-development.
Each example includes a PRD, architectural decisions, and a mapping of which skill drove each decision. This level of transparency turns abstract concepts into actionable patterns.
Contributing and Community
The repo follows the Agent Skills open‑development model:
- Fork the repo and create a new skill following the canonical folder structure.
- Keep the
SKILL.mdunder 500 lines for optimal loading speed. - Add working examples and unit tests where possible.
- Submit a pull request with a clear description of the skill’s purpose and usage.
Contributors are invited to extend the skill set, fix bugs, and propose new patterns. Contact the maintainer, Muratcan Koylan, for direct collaboration or support.
Why You Should Use This Repository
| Benefit | Why it matters |
|---|---|
| Comprehensive Coverage | From fundamentals to advanced evaluation, you get one place to explore the entire context engineering stack. |
| Zero‑Cost, MIT License | No licensing headaches – perfect for startups, academic projects, or personal experimentation. |
| Plug‑and‑Play | Ready to drop into Claude Code or any framework that supports custom instructions. |
| High‑Quality Documentation | Each skill is accompanied by a SKILL.md, scripts, and reference diagrams. |
| Active Community | 7.2k stars, 564 forks, and an ecosystem of open contributors keep the skill set fresh. |
Whether you’re building a personal assistant, a data‑processing pipeline, or a production‑grade evaluation platform, Agent Skills for Context Engineering provides the blueprints and code snippets to jumpstart your project. Dive in, contribute, and help shape the future of intelligent agents.
Get Started – Clone the repo or add it to Claude Code today, and begin engineering contexts that let your agents truly shine.