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.

DeepWiki: Learn more here

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."

— Meta Context Engineering via Agentic Skill Evolution, Peking University State Key Laboratory of General Artificial Intelligence (2026)

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
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
latent-briefing	Share task-relevant orchestrator state with workers via task-guided KV cache compaction when the worker runtime is controllable
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:

/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering

Step 2: Install the Plugin

Option A - Browse and install:

1. Select Browse and install plugins
2. Select context-engineering-marketplace
3. Select context-engineering
4. Select Install now

Option B - Direct install via command:

/plugin install context-engineering@context-engineering-marketplace

This installs all 14 skills in a single plugin. Skills are activated automatically based on your task context.

Skill Triggers

Skill	Triggers On
context-fundamentals	"understand context", "explain context windows", "design agent architecture"
context-degradation	"diagnose context problems", "fix lost-in-middle", "debug agent failures"
context-compression	"compress context", "summarize conversation", "reduce token usage"
context-optimization	"optimize context", "reduce token costs", "implement KV-cache"
latent-briefing	"KV cache compaction between agents", "worker KV memory handoff", "latent briefing", "share trajectory without summarization"
multi-agent-patterns	"design multi-agent system", "implement supervisor pattern"
memory-systems	"implement agent memory", "build knowledge graph", "track entities"
tool-design	"design agent tools", "reduce tool complexity", "implement MCP tools"
filesystem-context	"offload context to files", "dynamic context discovery", "agent scratch pad", "file-based context"
hosted-agents	"build background agent", "create hosted coding agent", "sandboxed execution", "multiplayer agent", "Modal sandboxes"
evaluation	"evaluate agent performance", "build test framework", "measure quality"
advanced-evaluation	"implement LLM-as-judge", "compare model outputs", "mitigate bias"
project-development	"start LLM project", "design batch pipeline", "evaluate task-model fit"
bdi-mental-states	"model agent mental states", "implement BDI architecture", "transform RDF to beliefs", "build cognitive agent"

For Cursor (Open Plugins)

This repository is listed on the Cursor Plugin Directory.

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

Available skills: context-fundamentals, context-degradation, context-compression, context-optimization, latent-briefing, multi-agent-patterns, memory-systems, tool-design, filesystem-context, hosted-agents, evaluation, advanced-evaluation, project-development, bdi-mental-states

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
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)
• Module Isolation: 6 independent modules (identity, content, knowledge, network, operations, agents)
• Append-Only Memory: JSONL files with schema-first lines for agent-friendly parsing
• Automation Scripts: 4 consolidated tools (weekly_review, content_ideas, stale_contacts, idea_to_draft)

Includes detailed traceability in HOW-SKILLS-BUILT-THIS.md mapping every architectural decision to specific skill principles.

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

Book SFT Pipeline Example

The book-sft-pipeline example demonstrates training small models (8B) to write in any author's style:

• Intelligent Segmentation: Two-tier chunking with overlap for maximum training examples
• Prompt Diversity: 15+ templates to prevent memorization and force style learning
• Tinker Integration: Complete LoRA training workflow with $2 total cost
• Validation Methodology: Modern scenario testing proves style transfer vs content memorization

Integrates with context engineering skills: project-development, context-compression, multi-agent-patterns, evaluation.

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:

1. Follow the skill template structure
2. Provide clear, actionable instructions
3. Include working examples where appropriate
4. Document trade-offs and potential issues
5. 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.