Skill UI

DiffDock is a state-of-the-art, diffusion-based deep learning tool for predicting 3D binding poses of drug-like ligands to protein targets. It supports both PDB files and amino acid sequences for proteins, and accepts diverse ligand formats (SMILES, SDF, MOL2). It is designed for structure-based drug discovery, lead optimization, and high-throughput virtual screening campaigns, providing predicted poses and confidence scores.

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

PyTDC is a specialized Python package designed to interact with the Therapeutics Data Commons. It facilitates the discovery of machine learning tasks, loading approved datasets, generating task-appropriate data splits, and evaluating predictions for drug discovery research. The tool strongly emphasizes performing metadata-only discovery and planning before initiating resource-intensive data downloads or network operations.

RDKit is a comprehensive cheminformatics library providing robust Python APIs for molecular structure manipulation and analysis. It supports tasks ranging from molecular I/O (SMILES, SDF) and calculating physical descriptors (MW, LogP) to advanced substructure searching, 3D coordinate generation, and chemical reaction modeling. It is an essential toolkit for drug discovery, computational chemistry, and academic cheminformatics research.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

This comprehensive guide covers advanced fuzzing methodology, a core technique in exploit development. Learn how to use coverage-guided fuzzing with powerful tools like AFL++ and libFuzzer to automatically discover critical security vulnerabilities, such as memory corruption bugs. Ideal for setting up robust fuzz campaigns, optimizing corpus generation, and triaging complex vulnerability outputs.

A comprehensive toolkit for advanced web intelligence workflows. It facilitates deep research, structured data enrichment, URL extraction, entity discovery, and continuous web monitoring. Ideal for scientific topics, academic source discovery, and generating exhaustive, evidence-based reports.

XcodeBuildMCP is a unified skill set designed to streamline the entire mobile and desktop development lifecycle. It replaces raw command-line tools like xcodebuild, xcrun, and simctl, providing structured workflows for iOS, macOS, watchOS, tvOS, and visionOS. Capabilities include project discovery, managing simulator and physical device workflows, advanced UI automation (gestures, view inspection), robust logging, and deep debugging using LLDB. It simplifies complex build, test, and run operations across multiple platforms.

This skill conducts a comprehensive audit to identify and prioritize automation opportunities within a user's existing workflow. It guides users through a structured intake (covering roles, repetitive tasks, and pain points) and systematically scans connected tools (like Gmail, Slack, Calendar, and CRMs) to surface actionable, ranked quick wins, minimizing implementation effort and maximizing impact.

This meta-skill orchestrates a comprehensive research pipeline for sales professionals. It accepts initial lead requests and systematically performs web searches, reviews public source evidence, and drafts actionable materials. The final output includes a detailed account brief, pain hypotheses, stakeholder maps, personalized outreach emails, and discovery call questions, enabling deep preparation before any sales interaction.

This tool analyzes and maps the downstream dependencies of a semantic model across multiple Fabric workspaces. It is crucial for performing impact assessments before making modifications, auditing connected reports, or mapping cross-workspace data flows. It automates the lineage discovery process, providing comprehensive visibility into which reports rely on a specific data source.

This skill conducts deep, structured competitor analysis using Browserbase's advanced discovery and enrichment tools. It generates a comprehensive HTML report that includes market overviews, detailed per-competitor deep dives (covering marketing, social, news, and technical aspects), side-by-side feature/pricing matrices, and comparative mentions feeds. Ideal for market researchers, product managers, and strategists needing actionable competitive intelligence.