Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deploym...
Strategic patterns for codebase exploration using Gemini's large context window. Covers token thresholds, model routing, and exploration strategies. Use when deciding between Claude and Gemini for exploration,...
Guide creation of AI Developer Workflows (ADWs) that combine deterministic orchestration code with non-deterministic agents. Use when building automated development pipelines, designing AFK agent systems, or im...
LLM inference infrastructure, serving frameworks (vLLM, TGI, TensorRT-LLM), quantization techniques, batching strategies, and streaming response patterns. Use when designing LLM serving infrastructure, optimizi...
Choose appropriate model for custom agent tasks. Use when selecting between Haiku, Sonnet, and Opus for agents, optimizing cost vs quality tradeoffs, or matching model capability to task complexity.
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
C4 model architecture visualization and documentation
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, design...
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing cont...
Expert technical advisor with deep reasoning for architecture decisions, code analysis, and engineering guidance. Masters complex tradeoffs, system design, security architecture, performance optimization, and e...
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.
Use when user needs ML model deployment, production serving infrastructure, optimization strategies, and real-time inference systems. Designs and implements scalable ML systems with focus on reliability and per...
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or imp...
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "mode...
Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimiz...
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure. Designs scalable large language model applications with focus on performance, cost...
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
Expert technical advisor with deep reasoning for architecture decisions, code analysis, and engineering guidance. Masters complex tradeoffs, system design, security architecture, performance optimization, and e...
Run multi-LLM council for adversarial debate and cross-validation. Orchestrates Claude, GPT-4, and Gemini for production-grade implementation, code review, architecture design, research, and security analysis.
Forecast categories, weighted pipeline calculations, deal scoring models, and forecast accuracy metrics.
Query fan-out coverage for AI visibility. Covers semantic variation analysis and sub-question targeting.
Create or update the feature specification from a natural language feature description.
Ralph Wiggum persistence loop with intelligent multi-model routing (Gemini, Codex, Claude, Council)
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.