Discover research llm ai skills, compare source repositories and read the instructions for each workflow.
AI agent builds full-stack applications with Supabase PostgreSQL, authentication, Row Level Security, Edge Functions, and real-time subscriptions. Use when building apps with Supabase, implementing RLS policies...
AI agent orchestrates parallel task execution through concurrent specialized agents for maximum efficiency. Use when tasks can run independently, multi-concern reviews, or parallel exploration.
Creates Model Context Protocol servers with tools, resources, and prompts for AI assistant integration. Use when extending Claude with custom capabilities, building AI tool integrations, or exposing APIs to AI...
Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.
AI agent designs production-grade database schemas with proper normalization, indexing strategies, and data modeling patterns. Use when creating new databases, designing tables, modeling relationships, or revie...
Automatic design system context injection for UI consistency
AI agent handles code review feedback professionally by categorizing, responding appropriately, and extracting learnings. Use when PR feedback arrives, addressing reviewer comments, or improving from feedback.
AI agent maximizes efficiency and minimizes costs through strategic token usage while maintaining output quality. Use when managing AI interactions, designing prompts, or reducing costs.
AI agent designs layered security architecture with multiple independent protective barriers ensuring no single point of failure. Use when building security systems, reviewing architecture, or hardening applica...
ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
Collecting and using user feedback - explicit/implicit signals, feedback analysis, improvement loops, A/B testing. Use when improving AI systems, understanding user satisfaction, or iterating on quality.
AI agent requests effective code reviews through proper preparation, clear descriptions, and appropriate reviewer selection. Use when creating PRs, requesting feedback, or submitting for review.
AI agent validates work through evidence-based verification with comprehensive checklists, automated validation, and proof-of-work documentation. Use when completing tasks, preparing for review, or ensuring qua...
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI s...
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
Applies AI-first development patterns for 10-100x productivity through specification-driven generation and leverage multiplication. Use when developing features with AI assistance or optimizing development work...
AI agent follows a 5-phase debugging process with reproduction, isolation, hypothesis testing, and root cause resolution. Use when investigating bugs, troubleshooting issues, or hunting errors.
Building AI agents - tool use, planning strategies (ReAct, Plan-and-Execute), memory systems, agent evaluation. Use when building autonomous AI systems, tool-augmented apps, or multi-step workflows.
Finetuning Foundation Models - when to finetune, LoRA, QLoRA, PEFT techniques, memory optimization, model merging. Use when adapting models to specific domains, reducing costs, or improving performance.
Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.
Orchestrates AI-native sprint management with autonomous agent coordination and continuous delivery. Use when running development sprints with AI agent teams or coordinating parallel task execution.
Protecting AI applications - input/output guards, toxicity detection, PII protection, injection defense, constitutional AI. Use when securing AI systems, preventing misuse, or ensuring compliance.
AI/ML model integration including vision, audio, embeddings, and RAG implementation patterns
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.