context-engineering
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mrgoonie/claudekit-skills --skill context-engineering- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads context-engineering automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Context Engineering
Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.
When to Activate
- Designing/debugging agent systems
- Context limits constrain performance
- Optimizing cost/latency
- Building multi-agent coordination
- Implementing memory systems
- Evaluating agent performance
- Developing LLM-powered pipelines
Core Principles
- Context quality > quantity - High-signal tokens beat exhaustive content
- Attention is finite - U-shaped curve favors beginning/end positions
- Progressive disclosure - Load information just-in-time
- Isolation prevents degradation - Partition work across sub-agents
- Measure before optimizing - Know your baseline
Quick Reference
| Topic | When to Use | Reference |
|---|---|---|
| Fundamentals | Understanding context anatomy, attention mechanics | context-fundamentals.md |
| Degradation | Debugging failures, lost-in-middle, poisoning | context-degradation.md |
| Optimization | Compaction, masking, caching, partitioning | context-optimization.md |
| Compression | Long sessions, summarization strategies | context-compression.md |
| Memory | Cross-session persistence, knowledge graphs | memory-systems.md |
| Multi-Agent | Coordination patterns, context isolation | multi-agent-patterns.md |
| Evaluation | Testing agents, LLM-as-Judge, metrics | evaluation.md |
| Tool Design | Tool consolidation, description engineering | tool-design.md |
| Pipelines | Project development, batch processing | project-development.md |
Key Metrics
- Token utilization: Warning at 70%, trigger optimization at 80%
- Token variance: Explains 80% of agent performance variance
- Multi-agent cost: ~15x single agent baseline
- Compaction target: 50-70% reduction, <5% quality loss
- Cache hit target: 70%+ for stable workloads
Four-Bucket Strategy
- Write: Save context externally (scratchpads, files)
- Select: Pull only relevant context (retrieval, filtering)
- Compress: Reduce tokens while preserving info (summarization)
- Isolate: Split across sub-agents (partitioning)
Anti-Patterns
- Exhaustive context over curated context
- Critical info in middle positions
- No compaction triggers before limits
- Single agent for parallelizable tasks
- Tools without clear descriptions
Guidelines
- Place critical info at beginning/end of context
- Implement compaction at 70-80% utilization
- Use sub-agents for context isolation, not role-play
- Design tools with 4-question framework (what, when, inputs, returns)
- Optimize for tokens-per-task, not tokens-per-request
- Validate with probe-based evaluation
- Monitor KV-cache hit rates in production
- Start minimal, add complexity only when proven necessary
Scripts
- context_analyzer.py - Context health analysis, degradation detection
- compression_evaluator.py - Compression quality evaluation
FAQ
What does the context-engineering skill do?
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
How do I install context-engineering?
Run npx skills add https://github.com/mrgoonie/claudekit-skills --skill context-engineering in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/context-engineering/ (personal) or .claude/skills/context-engineering/ (project).
Which AI agents can use context-engineering?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is context-engineering safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains context-engineering?
It is published by mrgoonie in mrgoonie/claudekit-skills. It has 496 recorded installs.
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