Skill UI

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

Reference for building Graph Neural Networks with PyTorch Geometric, covering data objects, datasets, transforms, convolution layers, lazy initialization, and heterogeneous support so you can quickly tackle node, link, and graph tasks.

Transforms natural-language prompts into Excalidraw JSON diagrams, supporting flowcharts, relationships, mind maps, architecture, DFD, swimlane, class, sequence, and ER views; ideal when users ask to visualize processes or system structures and need editable Excalidraw files.

Creates and validates Mermaid diagrams across 20+ types, renders SVG/PNG/ASCII, enforces curated styling, and supports batch rendering with themes; ideal for architecture, flow, and ERD visualization requests.

Guides teams through defining page hierarchies, navigation, URL patterns, and internal linking so websites stay intuitive for users while supporting SEO goals and future expansion.

This skill provides comprehensive access to X (Twitter) data via the Xquik platform. It enables advanced data tasks such as searching tweets, retrieving detailed user profiles and engagement metrics (likes, retweets), performing bulk follower extraction, running giveaway draws, and setting up real-time account monitoring via webhooks. It supports over 19 extraction tools and API integration, making it a powerful social media intelligence tool.

This powerful tool helps translate natural language requirements into robust, normalized relational database schemas. It can generate comprehensive schema designs, complete with relationships, constraints, and advanced architectural concerns like multi-tenancy, soft deletes, and audit trails. Furthermore, it automates the generation of database migrations (supporting Prisma, TypeORM, Alembic, etc.), corresponding type definitions (TypeScript/Python), seed data, and Entity-Relationship Diagrams (ERD) using Mermaid.

A powerful tool designed to scaffold and generate production-ready MCP servers directly from OpenAPI specifications. It treats OpenAPI as the single source of truth, automatically converting API paths and operations into standardized tool definitions. Supports both Python and TypeScript implementations, ensuring schema consistency, robust validation, and safe versioning for exposing internal APIs to LLM agents and other services.

Query BindingDB for measured drug-target affinities (Ki, Kd, IC50, EC50) via REST endpoints or downloaded TSVs to support target-centric drug discovery, SAR analysis, lead profiling, and benchmarking datasets.

Access DepMap data to fetch CRISPR Chronos dependency scores, drug sensitivity readouts, and gene effect profiles for cancer cell lines, supporting target validation, biomarker discovery, and synthetic-lethality research via API or downloads.

This expert skill reviews the architecture and design of the Bitcoin Lightning Network protocol. It analyzes complex Layer 2 scaling tradeoffs, compares various channel factory approaches, and evaluates critical technical aspects—including trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness guarantees, and watchtower support. Use this when assessing the cryptographic and systemic soundness of L2 blockchain protocols.