Ck:context Engineering

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

1. Context quality > quantity - High-signal tokens beat exhaustive content
2. Attention is finite - U-shaped curve favors beginning/end positions
3. Progressive disclosure - Load information just-in-time
4. Isolation prevents degradation - Partition work across sub-agents
5. Measure before optimizing - Know your baseline

IMPORTANT:

• Sacrifice grammar for the sake of concision.
• Ensure token efficiency while maintaining high quality.
• Pass these rules to subagents.

Quick Reference

| Topic | When to Use | Reference | |-------|-------------|-----------| | Fundamentals | Understanding context anatomy, attention mechanics | [context-fundamentals.md](./references/context-fundamentals.md) | | Degradation | Debugging failures, lost-in-middle, poisoning | [context-degradation.md](./references/context-degradation.md) | | Optimization | Compaction, masking, caching, partitioning | [context-optimization.md](./references/context-optimization.md) | | Compression | Long sessions, summarization strategies | [context-compression.md](./references/context-compression.md) | | Memory | Cross-session persistence, knowledge graphs | [memory-systems.md](./references/memory-systems.md) | | Multi-Agent | Coordination patterns, context isolation | [multi-agent-patterns.md](./references/multi-agent-patterns.md) | | Evaluation | Testing agents, LLM-as-Judge, metrics | [evaluation.md](./references/evaluation.md) | | Tool Design | Tool consolidation, description engineering | [tool-design.md](./references/tool-design.md) | | Pipelines | Project development, batch processing | [project-development.md](./references/project-development.md) | | Runtime Awareness | Usage limits, context window monitoring | [runtime-awareness.md](./references/runtime-awareness.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,

Codex Usage Limits: 5h=45%, 7d=32% Context Window Usage: 67%

**Thresholds:**
- 70%: WARNING - consider optimization/compaction
- 90%: CRITICAL - immediate action needed

**Data Sources:**
- Usage limits: OpenAI OAuth API (`https://api.openai.com/api/oauth/usage`)
- Context window: Statusline temp file (`/tmp/ck-context-{session_id}.json`)

## Scripts

- [context_analyzer.py](./scripts/context_analyzer.py) - Context health analysis, degradation detection
- [compression_evaluator.py](./scripts/compression_evaluator.py) - Compression quality evaluation

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [manhvann](https://github.com/manhvann)
- **Source:** [manhvann/codexkit](https://github.com/manhvann/codexkit)
- **License:** MIT

Install and usage instructions live in the source repository linked above.