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optimizing-tokens
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.
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tools llm ai AI Skills
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implementing-defense-in-depth
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...
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ml-serving-optimization
ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.
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dataset-engineering
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
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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.
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requesting-code-reviews
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.
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verifying-before-completion
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...
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ai-engineering
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...
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inference-optimization
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
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coding-with-omega-leverage
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...
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debugging-systematically
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.
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ai-agents
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.
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GitHub
finetuning
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.
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ml-systems-fundamentals
Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.
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managing-omega-sprints
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.
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guardrails-safety
Protecting AI applications - input/output guards, toxicity detection, PII protection, injection defense, constitutional AI. Use when securing AI systems, preventing misuse, or ensuring compliance.
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ai-integration
AI/ML model integration including vision, audio, embeddings, and RAG implementation patterns
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rag-systems
Retrieval-Augmented Generation - chunking strategies, embedding, vector search, hybrid retrieval, reranking, query transformation. Use when building RAG pipelines, knowledge bases, or context-augmented applicat...
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developing-test-driven
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.
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foundation-models
Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.
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brainstorming-ideas
AI agent generates diverse solutions through structured divergent thinking and systematic exploration frameworks. Use when exploring options, solving problems creatively, or generating alternatives.
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finishing-development-branches
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.
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evaluation-methodology
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.
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ai-system-evaluation
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.
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