Skill UI

Implement robust human-in-the-loop (HITL) approval gates for sophisticated LangGraph workflows. This skill enables the graph to pause execution before critical, irreversible actions (like sending emails or calling tools). It manages complex state serialization, provides clear resume semantics using `Command(resume=...)`, and integrates with external UIs (e.g., Slack) to await human decision, ensuring safe and controlled workflow execution.

This skill provides implementation patterns for utilizing Runway's AI video generation and creative platform. It covers setting up the SDK, generating videos from images, and handling common API errors (like insufficient credits or unauthorized access). It is designed for developers needing to integrate secure, high-quality AI video workflows into their applications.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

Utilize Gemini's advanced AI capabilities for broad and comprehensive literature discovery. Unlike traditional keyword-based APIs (like arXiv or Semantic Scholar), this tool decomposes research topics into multiple angles, aliases, and sub-problems, providing a deep, multi-faceted survey of academic literature, including code availability and recent preprints.

Hugging Science is a curated, LLM-friendly index specializing in AI/ML applications across diverse scientific domains, including biology, chemistry, physics, and medicine. It helps researchers discover pre-filtered datasets, state-of-the-art models, and interactive demos for solving complex scientific problems, ensuring high signal quality beyond generic search.

A rigorous, Chief AI Officer-level interrogation tool designed to pressure-test any plan involving artificial intelligence. Before deployment, it forces comprehensive review across crucial dimensions: evaluation metrics, hallucination risk, EU AI Act compliance, model architecture selection (API vs. build), cost economics, and required organizational roles. Essential for maintaining AI product safety and compliance.

This tool generates an extensive, long-form cheat sheet for specialized Machine Learning or Large Language Model (LLM) topics. It provides comprehensive technical deep dives, including theoretical formulas with derivations, from-scratch PyTorch code implementations, detailed comparison tables, and 25 high-frequency interview questions spanning L1 (Must-Know) to L3 (Expert Lab) difficulty.