A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
A comprehensive, open collection of Agent Skills focused on context engineering principles for building production-grade AI agent systems. These skills teach the art and science of curating context to maximize agent effectiveness across any agent platform.
What is Context Engineering?
Context engineering is the discipline of managing the language model's context window. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the holistic curation of all information that enters the model's limited attention budget: system prompts, tool definitions, retrieved documents, message history, and tool outputs.
The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics. As context length increases, models exhibit predictable degradation patterns: the "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity. Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.
Recognition
This repository is cited in academic research as foundational work on static skill architecture:
"While static skills are well-recognized [Anthropic, 2025b; Muratcan Koylan, 2025], MCE is among the first to dynamically evolve them, bridging manual skill engineering and autonomous self-improvement."
These skills establish the foundational understanding required for all subsequent context engineering work.
| Skill | Description |
|-------|-------------|
| context-fundamentals | Understand what context is, why it matters, and the anatomy of context in agent systems |
| context-degradation | Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash |
| context-compression | Design and evaluate compression strategies for long-running sessions |
These skills cover the patterns and structures for building effective agent systems.
| Skill | Description |
|-------|-------------|
| multi-agent-patterns | Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures |
| memory-systems | Design short-term, long-term, and graph-based memory architectures |
| tool-design | Build tools that agents can use effectively |
| filesystem-context | Use filesystems for dynamic context discovery, tool output offloading, and plan persistence |
| hosted-agents | NEW Build background coding agents with sandboxed VMs, pre-built images, multiplayer support, and multi-client interfaces |
Operational Skills
These skills address the ongoing operation and optimization of agent systems.
| Skill | Description |
|-------|-------------|
| context-optimization | Apply compaction, masking, and caching strategies |
| evaluation | Build evaluation frameworks for agent systems |
| advanced-evaluation | Master LLM-as-a-Judge techniques: direct scoring, pairwise comparison, rubric generation, and bias mitigation |
Development Methodology
These skills cover the meta-level practices for building LLM-powered projects.
| Skill | Description |
|-------|-------------|
| project-development | Design and build LLM projects from ideation through deployment, including task-model fit analysis, pipeline architecture, and structured output design |
Cognitive Architecture Skills
These skills cover formal cognitive modeling for rational agent systems.
| Skill | Description |
|-------|-------------|
| bdi-mental-states | NEW Transform external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns for deliberative reasoning and explainability |
Design Philosophy
Progressive Disclosure
Each skill is structured for efficient context use. At startup, agents load only skill names and descriptions. Full content loads only when a skill is activated for relevant tasks.
Platform Agnosticism
These skills focus on transferable principles rather than vendor-specific implementations. The patterns work across Claude Code, Cursor, and any agent platform that supports skills or allows custom instructions.
Conceptual Foundation with Practical Examples
Scripts and examples demonstrate concepts using Python pseudocode that works across environments without requiring specific dependency installations.
Usage
Usage with Claude Code
This repository is a Claude Code Plugin Marketplace containing context engineering skills that Claude automatically discovers and activates based on your task context.
Installation
Step 1: Add the Marketplace
Run this command in Claude Code to register this repository as a plugin source:
The .plugin/plugin.json manifest follows the Open Plugins standard, so the repo also works with any conformant agent tool (Codex, GitHub Copilot, etc.).
Using Individual Skills
To use a single skill without installing the full plugin, copy its SKILL.md directly into your project's .claude/skills/ directory:
# Example: add just the context-fundamentals skill
mkdir -p .claude/skills
curl -o .claude/skills/context-fundamentals.md \
https://raw.githubusercontent.com/muratcankoylan/Agent-Skills-for-Context-Engineering/main/skills/context-fundamentals/SKILL.md
Extract the principles and patterns from any skill and implement them in your agent framework. The skills are deliberately platform-agnostic.
Examples
The examples folder contains complete system designs that demonstrate how multiple skills work together in practice.
| Example | Description | Skills Applied |
|---------|-------------|----------------|
| digital-brain-skill | NEW Personal operating system for founders and creators. Complete Claude Code skill with 6 modules, 4 automation scripts | context-fundamentals, context-optimization, memory-systems, tool-design, multi-agent-patterns, evaluation, project-development |
| x-to-book-system | Multi-agent system that monitors X accounts and generates daily synthesized books | multi-agent-patterns, memory-systems, context-optimization, tool-design, evaluation |
| llm-as-judge-skills | Production-ready LLM evaluation tools with TypeScript implementation, 19 passing tests | advanced-evaluation, tool-design, context-fundamentals, evaluation |
| book-sft-pipeline | Train models to write in any author's style. Includes Gertrude Stein case study with 70% human score on Pangram, $2 total cost | project-development, context-compression, multi-agent-patterns, evaluation |
Each example includes:
Complete PRD with architecture decisions
Skills mapping showing which concepts informed each decision
Implementation guidance
Digital Brain Skill Example
The digital-brain-skill example is a complete personal operating system demonstrating comprehensive skills application:
Progressive Disclosure: 3-level loading (SKILL.md → MODULE.md → data files)
See the template folder for the canonical skill structure.
Contributing
This repository follows the Agent Skills open development model. Contributions are welcome from the broader ecosystem. When contributing:
Follow the skill template structure
Provide clear, actionable instructions
Include working examples where appropriate
Document trade-offs and potential issues
Keep SKILL.md under 500 lines for optimal performance
Feel free to contact Muratcan Koylan for collaboration opportunities or any inquiries.
License
MIT License - see LICENSE file for details.
References
The principles in these skills are derived from research and production experience at leading AI labs and framework developers. Each skill includes references to the underlying research and case studies that inform its recommendations.