Agent Skills for Context Engineering

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.

Skills Overview

Foundational Skills

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

Architectural Skills

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

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	NEW Design and build LLM projects from ideation through deployment, including task-model fit analysis, pipeline architecture, and structured output design

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

For Claude Code

Install skills by referencing this repository or by copying skill folders into your configured skills directory. When working on context engineering tasks, activate relevant skills to load their instructions.

For Cursor & Codex & Open Code

Copy skill content into .rules or create project-specific SKills folders. The skills provide the context and guidelines that agent needs for effective context engineering and agent design.

For Custom Implementations

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
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	NEW Production-ready LLM evaluation tools with TypeScript implementation, 19 passing tests	advanced-evaluation, tool-design, context-fundamentals, evaluation

Each example includes:

Complete PRD with architecture decisions
Skills mapping showing which concepts informed each decision
Implementation guidance

LLM-as-Judge Skills Example

The llm-as-judge-skills example is a complete TypeScript implementation demonstrating:

Direct Scoring: Evaluate responses against weighted criteria with rubric support
Pairwise Comparison: Compare responses with position bias mitigation
Rubric Generation: Create domain-specific evaluation standards
EvaluatorAgent: High-level agent combining all evaluation capabilities

# Quick start
cd examples/llm-as-judge-skills
npm install
cp env.example .env  # Add OPENAI_API_KEY
npm test  # Run 19 passing tests

Star History

Structure

Each skill follows the Agent Skills specification:

skill-name/
├── SKILL.md              # Required: instructions + metadata
├── scripts/              # Optional: executable code demonstrating concepts
└── references/           # Optional: additional documentation and resources

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.

DaeAkin/agent-skills-for-context-engineering