context-engineering

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.

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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."

— 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