If you have been building AI agents lately, you have probably hit a familiar wall. Your agent works flawlessly for simple tasks, but when complexity grows, so does the chaos. Context windows overflow, critical instructions get buried, and your once-reliable assistant starts hallucinating or forgetting what you told it three turns ago.
This is not a model problem; it is a context engineering problem. And now, there is a comprehensive open-source resource designed specifically to solve it: Agent Skills for Context Engineering is a curated collection of skills and patterns that teaches AI agents how to manage their own cognitive limitations.
With over 5,300 stars and 420 forks in just weeks since its December 2025 launch, this repository has quickly become one of the most-watched resources in the AI agent development community.
Created by Murat Can Koylan, an AI Agent Systems Manager specializing in prompt design and multi-agent architectures, it represents a shift in how we think about building intelligent systems. The premise is elegantly simple: instead of hoping larger context windows solve memory problems, we should treat context as the precious, finite resource.
The Context Window Bottleneck
Every language model operates within a context window which is the total collection of tokens it can attend to when generating responses. This includes system prompts, tool definitions, retrieved documents, message history, and tool outputs.
The common assumption is that bigger context windows mean fewer problems, but empirical evidence tells a different story. As Anthropic's engineering team explains, models exhibit predictable degradation patterns as context grows: the lost-in-the-middle phenomenon where information in the center receives less attention, U-shaped attention curves, and what researchers call context rot. Processing n tokens creates n-squared pairwise relationships that the model must compute and store. The attention budget is not infinite and it depletes with every token added.
This creates a fundamental engineering challenge. Production data shows that multi-agent systems consume roughly 15 times more tokens than simple chat interactions, and tool outputs alone can account for nearly 84 percent of total context usage. When your agent is drowning in accumulated history, retrieved documents, and verbose tool responses, it loses the sharp focus needed for complex reasoning.
A Structured Approach to Context Management
Agent Skills for Context Engineering offers a solution through ten carefully designed skills organized into four categories. The foundational skills establish core understanding: context-fundamentals teaches the anatomy of context and attention mechanics; context-degradation helps recognize failure patterns; and context-compression provides strategies for long-running sessions.
The architectural skills cover system design: multi-agent-patterns for supervisor, swarm, and hierarchical architectures; memory-systems for short-term, long-term, and graph-based memory; and tool-design for building tools agents can actually use effectively.
Operational skills address runtime concerns including context-optimization for compaction, masking, and caching; evaluation for building test frameworks; and advanced-evaluation for LLM-as-a-Judge techniques. Finally, project-development covers the meta-level practices for building LLM-powered projects from ideation through deployment.
Why This Repository Stands Out to Me
What makes this collection particularly interesting is its philosophical commitment to progressive disclosure, a pattern where agents load only skill names and descriptions at startup. Full content loads only when activated for relevant tasks.
This mirrors how humans operate; we do not memorize entire libraries but maintain indexes to retrieve information on demand. The skills are also deliberately platform-agnostic, designed to work across Claude Code, Cursor, Codex, and any agent platform that supports custom instructions. The patterns transfer because they address fundamental constraints rather than vendor-specific quirks.
The repository also includes production-ready examples that demonstrate these principles in action. The Digital Brain skill is a complete personal operating system for founders and creators with six modules and four automation scripts.
The X-to-Book system shows how to design a multi-agent architecture that monitors social media accounts and generates synthesized books. The LLM-as-Judge skills provide TypeScript implementations for evaluation with 19 passing tests. Each example maps architectural decisions back to specific skill principles, creating a traceable learning path from theory to practice.
Key Features
- 10 Comprehensive Skills: From context fundamentals to advanced evaluation techniques, covering every aspect of context engineering for production agent systems.
- Progressive Disclosure Architecture: Skills structured for efficient context use with metadata loading at startup and full content on-demand activation.
- Claude Code Plugin Marketplace: Direct installation via plugin commands for seamless integration with Claude Code environments.
- Production Examples: Complete system designs including Digital Brain, X-to-Book, and LLM-as-Judge with detailed skills mapping.
Under the Hood: Architecture and Design
Each skill follows a consistent structure: a SKILL.md file containing YAML frontmatter with name and description fields, followed by markdown instructions. Optional directories include scripts for executable code, references for documentation, and assets for templates. The SKILL.md body is kept under 500 lines for optimal performance, with additional details split into separate files using progressive disclosure patterns.
skill-name/
SKILL.md # Required: instructions + metadata
scripts/ # Optional: executable code
references/ # Optional: documentation
assets/ # Optional: templates, resources
The repository leverages Python for demonstration scripts, though the concepts are language-agnostic. The context-fundamentals skill, for instance, includes a complete context manager implementation showing how to build optimized context for agent tasks with priority-based section management and usage reporting.
The multi-agent-patterns skill provides detailed code for handoff protocols, forward message mechanisms that solve the telephone game problem where supervisor agents incorrectly paraphrase sub-agent responses, and coordination patterns for different architectural choices.
The Multi-Agent Architecture Insight
Perhaps the most counterintuitive insight from this collection is that sub-agents exist primarily to isolate context, not to anthropomorphize role division. When single-agent context limits constrain task complexity, multi-agent architectures partition work across multiple context windows.
Each agent operates in a clean context focused on its subtask. The supervisor pattern offers centralized control with clear decomposition. The swarm pattern enables flexible handoffs without single points of failure. Hierarchical patterns organize agents into strategic, planning, and execution layers. The critical design principle remains consistent: choose architecture based on coordination needs, not organizational requirements.
Production benchmarks reveals some trade-offs. Multi-agent systems consume significantly more tokens, roughly 15 times baseline for complex research and coordination tasks. However, research on the BrowseComp evaluation found that token usage accounts for 80 percent of performance variance, validating the multi-agent approach of distributing work across agents with separate context windows. Critically, upgrading to better models often provides larger performance gains than simply doubling token budgets.
Real-World Applications
Theory means little without practical demonstration, and this repository delivers with four complete example systems that show context engineering principles in action. Each example includes detailed PRDs with architecture decisions, skills mappings that trace design choices back to specific principles, and implementation guidance that bridges the gap between understanding and building.
The Digital Brain example stands out as a complete personal operating system designed for founders, creators, and builders. Imagine having an AI assistant that truly understands your voice, remembers your contacts, tracks your goals, and helps create content that sounds authentically like you. Digital Brain makes this possible through careful context architecture. The system implements three-level progressive loading: the main SKILL.md file provides high-level guidance, module-specific files like IDENTITY.md or CONTENT.md offer domain instructions, and data files contain the actual information. This layered approach means the agent never loads your entire life story into context; it retrieves only what the current task requires.
Six independent modules partition the system into distinct domains. The identity module stores your voice patterns, brand positioning, and bio variants for different platforms. The content module manages ideas, drafts, and a publishing calendar. The knowledge module tracks bookmarks, learning goals, and research notes. The network module functions as a relationship CRM with contacts, interaction history, and introduction tracking.
The operations module handles tasks, OKRs, meeting notes, and metrics. Finally, the agents module contains four consolidated automation scripts: weekly review generation, content ideation from your knowledge base, stale contact detection for relationship maintenance, and idea-to-draft scaffolding. All persistent data uses append-only JSONL files with schema-first lines, ensuring agents can parse the structure immediately while preserving complete history for pattern analysis.
The X-to-Book system tackles a different challenge: monitoring social media accounts and synthesizing daily books from their content. This multi-agent architecture demonstrates how context isolation enables complex coordination. A supervisor agent maintains high-level planning while specialized sub-agents handle tweet collection, content analysis, chapter writing, and book assembly. Each sub-agent operates in a clean context focused solely on its task, returning condensed summaries rather than raw data. The file system serves as the coordination mechanism, avoiding the context bloat that would occur if agents passed complete state to each other. A temporal knowledge graph tracks how positions evolve over time, enabling the system to synthesize coherent narratives from fragmented social media content.
For teams building evaluation infrastructure, the LLM-as-Judge skills provide a complete TypeScript implementation with 19 passing tests. The example covers direct scoring against weighted criteria with rubric support, pairwise comparison with position bias mitigation, automated rubric generation for domain-specific evaluation, and an EvaluatorAgent that combines all capabilities. This demonstrates how context engineering principles apply beyond conversational agents to the critical challenge of measuring AI system quality.
Perhaps the most surprising example is the Book SFT Pipeline, which shows how to train small models to write in any author's distinctive style. The case study trained Qwen3-8B-Base on Gertrude Stein's experimental 1909 work Three Lives, achieving remarkable results: a 70 percent human score on Pangram's AI detector, meaning evaluators believed the output was written by a human seven times out of ten. The entire process used just 592 training examples, completed in 15 minutes, and cost two dollars total.
The secret lies in intelligent segmentation using two-tier chunking with overlap to maximize training examples, combined with 15-plus prompt templates that prevent memorization and force genuine style learning. This example applies context engineering skills including project-development for staged pipeline architecture, context-compression for the segmentation strategy, and evaluation for modern scenario testing that proves style transfer rather than content memorization.
Community and Contribution
The repository follows an open development model welcoming contributions from the broader ecosystem. Guidelines emphasize skill template structure, clear actionable instructions, working examples, documented trade-offs, and the 500-line limit for SKILL.md files. The CONTRIBUTING.md file provides detailed instructions for adding new skills, submitting changes, and reporting issues. Content guidelines stress platform agnosticism, avoiding vendor-locked examples and features specific to single agent products.
The growing star count and fork activity suggest a community hungry for structured approaches to agent development. Open issues and pull requests show active engagement with topics ranging from new skill proposals to documentation improvements. The repository includes a template folder providing the canonical skill structure for anyone looking to contribute their own context engineering patterns.
Usage and License Terms
Agent Skills for Context Engineering is released under the MIT License, granting broad permissions for use, modification, and distribution. Users can copy, modify, merge, publish, distribute, sublicense, and sell copies of the software. The only requirement is including the original copyright notice and permission notice in copies or substantial portions. This permissive licensing makes the skills suitable for both personal projects and commercial applications.
Impact and Future Potential
As language models grow more capable, the challenge shifts from crafting perfect prompts to thoughtfully curating what information enters the model's limited attention budget at each step. Context engineering represents this fundamental shift in how we build with LLMs. The techniques in this repository will continue evolving as models improve, but treating context as a precious finite resource will remain central to building reliable, effective agents.
The repository positions itself as a meta-agent knowledge base, providing a standard set of skills in markdown and code that can be fed to any agent so it understands how to manage its own cognitive constraints. This approach differs from black-box tool libraries by teaching transferable principles rather than vendor-specific implementations. As Koylan explains, AGENTS.md files act as declarative context defining project structure and rules, while Skills provide the procedural knowledge that agents can load on demand.
About the Author
Murat Can Koylan works as an AI Agent Systems Manager at 99ravens.ai, specializing in prompt design, context engineering, persona embodiment, and multi-agent architectures. Based in Toronto, Canada, he describes himself as a techno-optimist and has been active in the AI engineering space since the early GPT-4 era. His work spans from building document analysis systems with embeddings and vector stores to creating comprehensive frameworks for agent development. The Agent Skills repository represents the codification of patterns learned from production experience building real agent systems.
Conclusion
Agent Skills for Context Engineering fills a critical gap in the AI development ecosystem. While countless tutorials teach prompt engineering techniques, few resources address the holistic challenge of managing context across complex, long-running agent interactions. This repository provides both the theoretical foundation and practical patterns needed to build agents that work reliably at production scale.
Whether you are designing multi-agent architectures, implementing memory systems, or simply trying to understand why your agent loses focus after a few dozen turns, this collection offers structured guidance grounded in real-world experience. The MIT license and platform-agnostic design mean you can start applying these patterns today, regardless of which agent framework you choose.
Explore the repository at github.com/muratcankoylan/Agent-Skills-for-Context-Engineering and consider contributing your own context engineering patterns to help grow this valuable community resource.
Agent Skills for Context Engineering: The Open Playbook for Building Production-Grade AI Agents