# Context Engineering Collection

A comprehensive collection of Agent Skills for context engineering, harness...

- Repository: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering (18272 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-12
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection -g -a claude-code -y`
Codex: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection -g -a codex -y`
Cursor: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection -g -a cursor -y`
Gemini CLI: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering`

## Description

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Agent Skills for Context Engineering

This collection provides structured guidance for building production-grade AI agent systems through effective context engineering.

## When to Activate

Activate these skills when:
- Building new agent systems from scratch
- Optimizing existing agent performance
- Debugging context-related failures
- Designing multi-agent architectures
- Creating or evaluating tools for agents
- Implementing memory and persistence layers
- Designing autonomous research or evaluation harnesses

## Skill Map

### Foundational Context Engineering

**Understanding Context Fundamentals**
Context is not just prompt text, it is the complete state available to the language model at inference time, including system instructions, tool definitions, retrieved documents, message history, and tool outputs. Effective context engineering means understanding what information truly matters for the task at hand and curating that information for maximum signal-to-noise ratio.

**Recognizing Context Degradation**
Language models exhibit predictable degradation patterns as context grows: the "lost-in-middle" phenomenon where information in the center of context receives less attention; U-shaped attention curves that prioritize beginning and end; context poisoning when errors compound; and context distraction when irrelevant information overwhelms relevant content.

### Architectural Patterns

**Multi-Agent Coordination**
Production multi-agent systems converge on three dominant patterns: supervisor/orchestrator architectures with centralized control, peer-to-peer swarm architectures for flexible handoffs, and hierarchical structures for complex task decomposition. The critical insight is that sub-agents exist primarily to isolate context rather than to simulate organizational roles.

The rest: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/blob/HEAD//SKILL.md