Skill UI

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.

Combines ESM3 and ESM C protein language models to generate sequences, predict structures, condition functions, and extract embeddings via local or Forge API inference for protein design and engineering workflows.

Audit, validate, and enhance GEO structured data by detecting existing JSON-LD/microdata/RDFa, checking schema.org compliance, and generating ready-to-use JSON-LD to boost AI discoverability.

Use the esm Python SDK with ESM3 and ESMC for protein sequence generation, embeddings, function conditioning, structure prediction, inverse folding, and Forge/Biohub inference workflows.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

A comprehensive guide for authors submitting to computer vision conferences, detailing how to structure, organize, and utilize supplementary material. It clearly distinguishes between core claims (which must be in the main body) and supportive evidence, such as full ablation grids, detailed derivations, and video results, which belong in the supplement. The guide covers best practices for handling anonymity, video evidence, and avoiding common submission bugs.

This skill processes verbose, stream-of-consciousness voice dictations (from tools like macOS Dictation or Wispr Flow) and transforms them into highly structured, token-efficient prompts. It removes filler words, repetitions, and tangents, organizing the core intent into standard sections (Context, Objective, Constraints) suitable for advanced LLMs like Claude Code.

Restructure oversized single-file SKILL.md or scattered project rules into a well-organized Skill directory with progressive rigor, routing, validation, and drift-resistant maintenance for AI agent harnesses.