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, compression strategies, memory architectures, multi-agent patterns, evaluation, tool design, and project development.
What this skill does
# 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 ## 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 clear descriptions (what, when, inputs, returns) 5. Optimize for tokens-per-task, not tokens-per-request 6. Validate with probe-based evaluation 7. Monitor token usage in production 8. Start minimal, add complexity only when proven necessary ## Skill Coordination When multiple skills are active: - Load only relevant skill content - Use skill metadata for discovery - Avoid loading full skill definitions unless needed - Reference skills by pattern detection, not direct names ## References For detailed guidance, see: - `references/fundamentals.md` - Context anatomy, attention mechanics - `references/degradation.md` - Debugging failures, lost-in-middle, poisoning - `references/optimization.md` - Compaction, masking, caching, partitioning - `references/compression.md` - Long sessions, summarization strategies - `references/memory.md` - Cross-session persistence, knowledge graphs - `references/multi-agent.md` - Coordination patterns, context isolation - `references/evaluation.md` - Testing agents, LLM-as-Judge, metrics - `references/tool-design.md` - Tool consolidation, description engineering
Related in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.