Agent Skills for Context Engineering

A comprehensive, open collection of Agent Skills focused on context engineering and harness engineering principles for building production-grade AI agent systems. These skills teach the art and science of curating context, designing agent operating loops, and evaluating agent behavior across any agent platform.

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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."

1. Meta Context Engineering via Agentic Skill Evolution, Peking University State Key Laboratory of General Artificial Intelligence (2025)
2. Agent Harness Engineering: A Survey, CMU, Yale, JHU, NEU, Tulane, UAB, OSU, Virginia Tech, and Amazon (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
harness-engineering	Design autonomous agent harnesses with locked metrics, durable logs, novelty gates, rollback, and human approval boundaries

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 15 skills in a single plugin. Skills are activated automatically based on your task context.

Skill Activation Scenarios

Skill	Activate When
context-fundamentals	Establishing context-window mental models, planning agent architecture, or explaining how context components affect model behavior
context-degradation	Diagnosing attention failures, context poisoning, lost-in-middle behavior, or degraded agent performance across long sessions
context-compression	Preserving useful state while reducing conversation, tool-output, or trajectory size under context pressure
context-optimization	Improving token efficiency, retrieval precision, prefix reuse, masking, partitioning, or budget allocation for agent systems
latent-briefing	Sharing orchestrator trajectory with workers via task-guided KV cache compaction when the worker runtime is controllable and the models are compatible
multi-agent-patterns	Choosing coordination patterns, isolating context across agents, designing handoffs, or evaluating whether parallel agents are justified
memory-systems	Persisting cross-session knowledge, tracking entities over time, choosing memory frameworks, or designing retrieval and update semantics
tool-design	Defining agent-tool contracts, consolidating tool surfaces, improving descriptions, or making tool errors actionable
filesystem-context	Moving large or durable context into files, creating scratchpads, supporting just-in-time discovery, or coordinating agents through shared artifacts
hosted-agents	Running coding agents in remote sandboxes, background environments, warm pools, or multiplayer agent infrastructure
evaluation	Creating deterministic checks, rubrics, regression suites, production monitoring, or quality gates for agent behavior
advanced-evaluation	Using LLM judges, pairwise comparison, calibration, bias mitigation, or human-aligned quality assessment
harness-engineering	Designing autonomous loops with locked evaluators, editable surfaces, durable logs, novelty gates, rollback, and approval boundaries
project-development	Deciding whether an LLM is appropriate, shaping batch pipelines, creating staged artifacts, or estimating operational cost
bdi-mental-states	Modeling beliefs, desires, intentions, rational action traces, or neuro-symbolic state transformations for agents

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, harness-engineering, 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
interleaved-thinking	Reasoning trace optimizer that captures, analyzes, and converts agent failure patterns into generated skills	evaluation, advanced-evaluation, context-degradation, harness-engineering

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.

Researcher Operating System

The researcher directory is a file-based operating system for turning external r