Context Engineering Collection

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    "name": "Muratcan Koylan",
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    "description": "Context engineering and harness engineering skills plus a file-based autonomous research-to-skill operating system with measured router-benchmark results across four frontier models",
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{
  "name": "context-engineering",
  "description": "Context engineering and harness engineering skills for production-grade AI agent systems: fundamentals, degradation patterns, compression, optimization, latent briefing (KV cache sharing between agents), multi-agent coordination, memory systems, tool design, evaluation, autonomous harnesses, and a file-based researcher operating system with deterministic gates, a continuous loop, and measured router-benchmark results across four frontier models.",
  "version": "2.3.0",
  "author": {
    "name": "Muratcan Koylan"
  }
}



# Don't index SpecStory auto-save files, but allow explicit context inclusion via @ references
.specstory/**


---
name: context-engineering-collection
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.
---

# 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 comp