Total 44,223 skills, AI & Machine Learning has 7033 skills
Showing 12 of 7033 skills
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
This skill should be used when the user asks to "predictive intelligence", "machine learning", "ML", "classification", "similarity", "clustering", "prediction", "AI", or any ServiceNow Predictive Intelligence development.
News Briefing + Verification Workflow. This workflow applies when users request news, headlines, daily briefings, "today's news", "latest updates", breaking news, or specific current figures/events that demand reliable sources and timestamps. Source links and local publication time are mandatory; NEVER fabricate any content.
Analyze Claude Code sessions via Braintrust
Claude Code Agent Teams - default team-based development with strict TDD pipeline enforcement
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Use when setting up, deploying, or operating vLLM Studio (env keys, controller/frontend startup, Docker services, branch workflow, and release checklists).
Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget planning, or cost optimization strategies.