GitHub
ml-workflow
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
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Databases AI Skills
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GitHub
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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GitHub
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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GitHub
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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GitHub
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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GitHub
deployment-paradigms
ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.
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GitHub
real-time-systems
WebSocket, Server-Sent Events, and real-time communication patterns for live features
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GitHub
testing-omega-quality
Implements comprehensive testing across all quality dimensions - accuracy, performance, security, and accessibility. Use when building test strategies or ensuring production-grade quality assurance.
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GitHub
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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GitHub
testingchaos-testing
Chaos engineering and fault injection patterns for testing system resilience, failure recovery, and graceful degradation
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GitHub
api-architecture
Enterprise API design with REST, GraphQL, gRPC patterns including versioning, pagination, and error handling
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GitHub
research-validation
Multi-source validation framework for technology decisions, best practices verification, and evidence-based implementation
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GitHub
tracing-root-causes
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.
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GitHub
building-with-shadcn
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.
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GitHub
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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GitHub
building-laravel-apis
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.
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GitHub
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
dnn-architectures
Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.
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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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GitHub
ml-systems-fundamentals
Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.
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GitHub
spring
Enterprise Spring Boot development with JPA, security, testing, and microservices patterns
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GitHub
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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GitHub
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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GitHub
test-task-generation
The agent automatically generates comprehensive test tasks from feature requirements, ensuring every implementation task has corresponding test coverage with proper acceptance criteria.
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