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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
Retrieval-Augmented Generation - chunking strategies, embedding, vector search, hybrid retrieval, reranking, query transformation. Use when building RAG pipelines, knowledge bases, or context-augmented applicat...
AI agent practices test-first development with the Red-Green-Refactor cycle for confident, well-designed code. Use when implementing features, fixing bugs, or establishing testing practices.
Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.
AI agent generates diverse solutions through structured divergent thinking and systematic exploration frameworks. Use when exploring options, solving problems creatively, or generating alternatives.
AI agent completes development branches with comprehensive quality gates, clean git history, and thorough PR preparation. Use when wrapping up features, preparing for merge, or finalizing PRs.
Methods for evaluating AI model outputs - exact match, semantic similarity, LLM-as-judge, comparative evaluation, ELO ranking. Use when measuring AI quality, building eval pipelines, or comparing models.
End-to-end AI system evaluation - model selection, benchmarks, cost/latency analysis, build vs buy decisions. Use when selecting models, designing eval pipelines, or making architecture decisions.
AI agent applies a 5-phase systematic framework for tackling complex problems when conventional approaches fail. Use when stuck, blocked, or troubleshooting issues.
AI agent performs database administration tasks including backup/restore, monitoring, replication, security hardening, and maintenance operations. Use when managing production databases, troubleshooting perform...
AI agent structures complex reasoning through numbered thought sequences with explicit dependencies. Use when facing multi-step problems, complex debugging, or architectural decisions.
AI agent creates structured implementation plans with task breakdown, dependency mapping, risk assessment, and file-level detail. Use when planning features, projects, or complex implementations.
Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.
Create beautiful infographics based on the given text content. Use this when users request creating infographics.
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.