user-feedback
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.
Discover Research skills, compare source repositories and read the instructions for each workflow.
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.
The agent implements chaos engineering practices for building resilient systems. Use when testing fault tolerance, designing game days, or validating system recovery.
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
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.
ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.
WebSocket, Server-Sent Events, and real-time communication patterns for live features
Implements comprehensive testing across all quality dimensions - accuracy, performance, security, and accessibility. Use when building test strategies or ensuring production-grade quality assurance.
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...
Chaos engineering and fault injection patterns for testing system resilience, failure recovery, and graceful degradation
Enterprise API design with REST, GraphQL, gRPC patterns including versioning, pagination, and error handling
Multi-source validation framework for technology decisions, best practices verification, and evidence-based implementation
AI agent performs systematic root cause analysis using 5 Whys, Fishbone diagrams, and evidence-based investigation. Use when debugging, conducting post-mortems, or investigating incidents.
Claude builds accessible React UIs using shadcn/ui components with Radix primitives and React Hook Form integration. Use when creating forms, dialogs, or composable UI systems.
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.
Builds enterprise Laravel applications with Eloquent, API Resources, Sanctum auth, and queue processing. Use when creating PHP backends, REST APIs, or full-stack Laravel applications.
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.
Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.
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.
Enterprise Spring Boot development with JPA, security, testing, and microservices patterns
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.
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.