Skill UI

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

DiffDock is a cutting-edge deep learning tool leveraging diffusion models to predict the 3D binding poses of small molecule ligands to protein targets. It supports structure-based drug discovery, enabling single-complex docking, batch virtual screening, and lead optimization. Users can input protein structures (PDB) or sequences, along with diverse ligand formats (SMILES, SDF, MOL2). The tool outputs predicted poses and confidence scores, making it crucial for identifying potential drug candidates, though it does not predict binding affinity.

The ESM skill provides comprehensive tools for computational biology, leveraging ESM3 and ESM-C models. It enables sophisticated protein sequence generation, structure prediction (sequence to 3D structure), inverse folding (structure to sequence design), and generating function-conditioned proteins. Use it for de novo protein design, feature embedding, and advanced structural analysis in bioinformatics workflows.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

Open Notebook is a self-hosted, open-source alternative to major AI research tools. It allows users to ingest diverse content—including PDFs, videos, and web pages—and organize them into private notebooks. Key features include context-aware AI chat, automated summarization, multi-speaker podcast generation, and full programmatic access via a comprehensive REST API. By running on your own infrastructure, it ensures complete data privacy and supports 16+ AI providers.

PufferLib is a high-performance, scalable RL framework designed for massive parallel training and vectorized simulation. Featuring PuffeRL (optimized PPO+LSTM) and the PufferEnv API, it supports multi-agent systems, custom environment creation, and achieves industry-leading speeds (up to millions of steps/second). Ideal for advanced research and large-scale AI experimentation.

A comprehensive framework designed to structure and automate PyTorch deep learning workflows. It simplifies boilerplate code by organizing models into LightningModules, automating training loops via the Trainer, and providing robust support for multi-GPU/TPU scaling (DDP, FSDP, DeepSpeed), data handling (DataModules), and best-in-class experiment logging (W&B, MLflow). Ideal for professional, scalable deep learning project development.

A powerful AI tool designed to transform complex scientific concepts into publication-quality visual schematics. Utilizing Nano Banana 2 AI for generation and Gemini 3.1 Pro Preview for rigorous quality review, the skill specializes in creating architectural diagrams, neural network structures, biological pathways, and complex flowcharts. It employs smart iterative refinement, ensuring the output meets predefined quality thresholds for academic journals, posters, and reports.

A comprehensive Python framework utilizing deep generative models and variational inference for advanced single-cell genomics analysis. It supports the integration and analysis of diverse multi-modal data types, including single-cell RNA-seq, ATAC-seq, CITE-seq, and spatial transcriptomics. Key applications include probabilistic batch correction, cell type annotation, and multi-omics integration.

Stable Baselines3 is a robust, PyTorch-based library providing reliable implementations of state-of-the-art reinforcement learning algorithms (PPO, SAC, DQN, etc.). It is designed for standard RL experiments, quick prototyping, and developing complex single-agent systems. The library supports training agents, creating custom Gymnasium environments, and running vectorized simulations for accelerated learning.

PyTorch Geometric (PyG) is the definitive standard library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for handling diverse graph types, including heterogeneous graphs. The library offers over 60 GNN layer implementations (such as GCN, GAT, GraphSAGE), full support for dataset loading (Planetoid, TUDataset, OGB), and necessary data transformations. It is essential for deep learning tasks involving node classification, graph classification, and link prediction.

A comprehensive guide to diagnosing and fixing performance bottlenecks when using Qdrant. Covers optimizing slow uploads, resolving stuck optimizers, and tuning HNSW parameters, including best practices for sharding, bulk loading, and multi-tenant indexing to ensure efficient vector search performance.