ml-pipeline
Design an ML system for a problem
What this skill does
# Design ML Pipeline Design an end-to-end ML system architecture for a given problem. ## Arguments `$ARGUMENTS` - The ML problem to design for (e.g., "recommendation system", "fraud detection", "search ranking", "content moderation") ## Workflow 1. **Clarify requirements** by understanding: - What predictions are being made? - What is the latency requirement? (real-time vs batch) - What is the scale? (QPS, data volume) - Who are the consumers of predictions? 2. **Load relevant skills** based on the problem: - Core ML architecture → `ml-system-design` - LLM-based systems → `llm-serving-patterns` - RAG systems → `rag-architecture` - Inference optimization → `ml-inference-optimization` - Vector search → `vector-databases` 3. **Spawn the ml-systems-designer agent** for comprehensive design: - Use Task tool with subagent_type="ml-systems-designer" - Provide full problem context and requirements - Request end-to-end architecture 4. **Design the complete pipeline**: - Data ingestion and processing - Feature engineering and feature store - Model training infrastructure - Model serving and inference - Monitoring and observability - A/B testing and experimentation 5. **Address cross-cutting concerns**: - Training-serving skew prevention - Feature consistency - Model versioning and rollback - Cost optimization ## Example Usage ```bash /sd:ml-pipeline recommendation system for 100M users /sd:ml-pipeline real-time fraud detection for payments /sd:ml-pipeline search ranking for e-commerce with 10M products /sd:ml-pipeline content moderation for social media /sd:ml-pipeline ad click prediction at 1M QPS /sd:ml-pipeline customer churn prediction /sd:ml-pipeline demand forecasting for inventory ``` ## Problem Categories | Category | Key Considerations | | -------- | ------------------ | | Recommendations | Cold start, real-time signals, A/B testing | | Fraud/Risk | Low latency (<100ms), rules + ML hybrid, feedback loops | | Search/Ranking | Multi-stage ranking, personalization, position bias | | NLP/LLM | Inference cost, caching, streaming responses | | Computer Vision | GPU inference, batching, edge deployment | | Time Series | Feature freshness, windowing, seasonal patterns | ## Output A comprehensive ML system architecture including: - High-level architecture diagram (component-based) - Data flow from sources to predictions - Technology stack recommendations - Trade-offs and alternatives considered - Phased implementation approach - Cost and scale considerations
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.