context-engineering-collection

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.

Foundational Skills

These skills establish the foundational understanding required for all subsequent context engineering work.

| Skill | Description |

|-------|-------------|

| [context-fundamentals](skills/context-fundamentals/) | Understand what context is, why it matters, and the anatomy of context in agent systems |

| [context-degradation](skills/context-degradation/) | Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash |

| [context-compression](skills/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](skills/multi-agent-patterns/) | Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures |

| [memory-systems](skills/memory-systems/) | Design short-term, long-term, and graph-based memory architectures |

| [tool-design](skills/tool-design/) | Build tools that agents can use effectively |

| [filesystem-context](skills/filesystem-context/) | Use filesystems for dynamic context discovery, tool output offloading, and plan persistence |

| [hosted-agents](skills/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](skills/context-optimization/) | Apply compaction, masking, and caching strategies |

| [evaluation](skills/evaluation/) | Build evaluation frameworks for agent systems |

| [advanced-evaluation](skills/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](skills/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](skills/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 |

Progressive Disclosure

Each skill is structured for efficient context use. At startup, agents load only skill names and descriptions.