Skill UI

Firecrawl provides a comprehensive API for web data harvesting. It enables users to perform single-page scraping, multi-page crawling, site mapping (URL discovery), and advanced structured data extraction using Large Language Models (LLMs). Ideal for integrating data pipelines, building web monitoring tools, or conducting market research.

Automate OpenAI API workflows through the Composio MCP integration, covering text and multimodal responses with structured outputs, embeddings, image generation, and model discovery.

Retrieve AlphaFold’s AI-predicted protein models via UniProt IDs, download CIF/PDB coordinates, inspect confidence metrics, and feed them into structural analysis or drug-discovery pipelines.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

Query the Monarch Initiative API to explore disease-gene-phenotype associations across species, enabling rare disease gene discovery, phenotype-to-gene mapping, and cross-species modeling via integrated HPO and model organism data.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

This tool provides comprehensive asset discovery and inventory management for Operational Technology (OT) and Industrial Control Systems (ICS) environments using the Claroty xDome platform. It utilizes passive monitoring and active querying to achieve full visibility into critical assets across Purdue Model levels, including PLCs, RTUs, and HMIs. Ideal for compliance assessments (e.g., IEC 62443) and onboarding brownfield sites.

This skill provides an interactive, guided onboarding tour for the Context-Matic MCP server. It addresses the challenge of general AI models relying on outdated or incorrect public code by acting as a live, version-aware grounding layer. The tour walks users through the server's core function, demonstrates key tools (like fetching APIs, searching models, and finding endpoints), and guides them through selecting and exploring available APIs for their specific project language.

DeepChem is a comprehensive Python library designed for applying machine learning to complex fields like chemistry, materials science, and biology. It enables users to perform molecular property prediction (e.g., ADMET, toxicity), train models using specialized graph neural networks (GNNs), and process diverse molecular formats (SMILES, SDF, FASTA). It provides extensive featurization options, making it foundational for drug discovery and computational chemistry research.

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.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.