GitHub
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.
Skills IA Développement
Découvrez les skills Développement, comparez leurs dépôts et consultez leurs instructions.
Catégories: Développement
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Explorer
GitHub
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.
GitHub
chaos-engineering-and-resilience-testing
The agent implements chaos engineering practices for building resilient systems. Use when testing fault tolerance, designing game days, or validating system recovery.
GitHub
ml-workflow
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
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.
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...
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...
GitHub
inference-optimization
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
GitHub
deployment-paradigms
ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.
GitHub
real-time-systems
WebSocket, Server-Sent Events, and real-time communication patterns for live features
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.
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...
GitHub
testingchaos-testing
Chaos engineering and fault injection patterns for testing system resilience, failure recovery, and graceful degradation
GitHub
api-architecture
Enterprise API design with REST, GraphQL, gRPC patterns including versioning, pagination, and error handling
GitHub
research-validation
Multi-source validation framework for technology decisions, best practices verification, and evidence-based implementation
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.
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.
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.
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.
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.
GitHub
dnn-architectures
Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.
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.
GitHub
ml-systems-fundamentals
Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.
GitHub
spring
Enterprise Spring Boot development with JPA, security, testing, and microservices patterns
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.