Context Engineering

Concept of the skill

Context engineering is the discipline of compiling the smallest sufficient, highest-signal working set for an LLM at each step: instructions, memory, retrieved facts, tool outputs, examples, conversation history, and task metadata.

Coverage

• Core principle: the model is a reasoning engine that reasons over whatever is in its context window — wrong context produces correct reasoning over false premises
• The five-layer context stack: system prompt, persistent memory, always-loaded rules, injected skills, agent prompt — what each layer does and how each can fail
• The four context failure modes: missing, stale, wrong, overwhelming — diagnostic questions for each, table of symptoms, and prevention strategies
• Four context quality metrics: injection precision, injection recall, context utilization, freshness score — definitions, healthy ranges, and how to measure each
• Context-compilation levers: selection, structuring, sequencing, compaction, memory integration, retrieval, provenance, and tool-result clearing
• Frequent Intentional Compaction (FIC): proactive compaction at task boundaries, target utilization range, and the difference between planned and forced compaction
• Subagent delegation pattern: when to delegate context-heavy investigation to a subagent so the main agent receives a summary instead of raw evidence
• Debugging decision tree: how to diagnose any agent failure by walking from missing-context through overwhelming-context before blaming the model
• The verification checklist: gates a context-engineering review must pass before declaring the pipeline healthy

Philosophy of the skill

The model is a reasoning engine that reasons over whatever is in its context window. If the context is wrong, the reasoning is correct but the conclusion is wrong. This means most agent failures are context failures, not model failures.