An open collection of Agent Skills focused on context engineering and harness engineering principles for AI engineers. The collection addresses language model attention degradation and provides structural patterns for orchestrator, peer-to-peer, and hierarchical multi-agent configurations.
Project overview
Provides documented approaches for managing constrained context windows, specifically addressing attention scarcity and lost-in-the-middle patterns.
Project type
AI Agent
Use cases
Automation
Deployment
Refer to project documentation
License
MIT
Best for
AI engineers focused on ai-agent-automation who need to address context window constraints and apply structural design patterns.
Key capabilities
Skills for understanding context, recognizing context failure patterns like lost-in-middle and poisoning, and designing compression strategies for long-running sessions.
Skills covering patterns and structures for building effective agent systems, such as orchestrator, peer-to-peer, and hierarchical multi-agent patterns.
Limitations and risks
Hardware specifications, operating systems, database requirements, external services, and GPU dependencies are not documented.
Getting started
No setup instructions, deployment scripts, or package managers are documented, requiring users to determine implementation paths independently.
Alternatives and comparisons
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Creates and improves skills from experience while maintaining context across sessions. Runnable from a $5 VPS to a GPU cluster.
GitHub project description: A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems.
README: A comprehensive, open collection of Agent Skills focused on context engineering and harness engineering principles for building production-grade AI agent systems.
README: As context length increases, models exhibit predictable degradation patterns: the "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity.
README: This repository is cited in academic research as foundational work on static skill architecture:
README: [context-degradation](skills/context-degradation/) | Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash |