ai-ml

combined

27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows. AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

When to use

Building LLM-powered applications
Implementing RAG (Retrieval-Augmented Generation)
Creating AI agents
Developing ML pipelines
Adding AI features to applications
Setting up AI observability

When not to use

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The two copies are different routing documents for the same broad AI/ML domain. The first is a production-oriented workflow bundle that orchestrates named skills across LLM integration, RAG, agents, ML pipelines, observability, and security, and it includes explicit selection/completion constraints and checklists. The second is a research-oriented index of 27 AI/ML skills focused on papers, benchmarks, frameworks, and model training, with a simple select-and-read instruction. Neither copy is a reproduction of the other, and crowning one would hide a real alternative: production workflow orchestration versus research skill discovery.