context-engineering
Context engineering optimizes language model performance by strategically curating minimal high-signal token sets across agent systems, memory architectures, and multi-agent pipelines. Use this skill when designing complex LLM workflows, debugging context degradation issues, balancing cost against reasoning quality, or implementing memory systems and agent coordination patterns that demand precise token allocation and attention management.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/context-engineering && cp -r /tmp/context-engineering/.claude/skills/context-engineering ~/.claude/skills/context-engineeringSKILL.md
# 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 ## 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) | ## 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 1. **Write**: Save context externally (scratchpads, files) 2. **Select**: Pull only relevant context (retrieval, filtering) 3. **Compress**: Reduce tokens while preserving info (summarization) 4. **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 1. Place critical info at beginning/end of context 2. Implement compaction at 70-80% utilization 3. Use sub-agents for context isolation, not role-play 4. Design tools with 4-question framework (what, when, inputs, returns) 5. Optimize for tokens-per-task, not tokens-per-request 6. Validate with probe-based evaluation 7. Monitor KV-cache hit rates in production 8. Start minimal, add complexity only when proven necessary ## Scripts - [context_analyzer.py](./scripts/context_analyzer.py) - Context health analysis, degradation detection - [compression_evaluator.py](./scripts/compression_evaluator.py) - Compression quality evaluation
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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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Integrate Bunny.net services (CDN, Storage, Stream, DNS, Edge Scripting, Shield, Magic Containers, Optimizer, Database). Use when building with Bunny.net APIs, deploying to Bunny CDN, uploading files to Edge Storage, managing video streaming, configuring DNS zones, writing edge scripts, setting up WAF/DDoS protection, deploying containers, or optimizing images. Triggers on "bunny", "bunnycdn", "b-cdn", "pull zone", "edge storage", "bunny stream".
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
Stage all files and create a commit.