Skill UI

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.

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.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

RDKit is a powerful, open-source cheminformatics library providing extensive Python APIs for molecular analysis and structural manipulation. It supports molecular I/O (SMILES, SDF, InChI), calculating physicochemical descriptors (MW, LogP), generating fingerprints, performing substructure searching, and analyzing stereochemistry. This toolkit is essential for advanced research in drug discovery, computational chemistry, and materials science.

A comprehensive guide for frontend engineers specializing in shadcn/ui. Learn best practices for component discovery, installation, and deep customization using Tailwind CSS and Radix UI. This resource covers project setup, component architecture, and advanced styling techniques for building robust, owned, and highly customizable applications.

TorchDrug is a comprehensive PyTorch-based ML toolbox designed for molecular and protein science. It leverages Graph Neural Networks (GNNs) to tackle complex tasks including molecular property prediction, protein structure modeling, drug-target binding prediction, knowledge graph reasoning, and de novo molecular generation. It provides robust capabilities for accelerating drug discovery research.

A comprehensive AI tutor for K-12 students covering Physics, Chemistry, and Biology. This skill employs Socratic Questioning, the 5E Model, and phenomenon-based learning to guide students toward scientific discovery. It adapts to various curricula (US, IB, Chinese) and focuses on building deep, conceptual understanding rather than simply providing answers.

This skill acts as the interactive command center for the Freshie ecosystem's CMDB. It tracks plugins, skills, and packs, providing comprehensive health checks, compliance validation, and historical trend analysis across 50 tables. Use it for running discovery scans, auditing compliance grades, querying deep inventory data, or generating detailed status reports.

This skill serves as a comprehensive entry point for initiating new development work. It systematically analyzes project metadata (specs, roadmaps) to determine the optimal work decomposition path (new spec, existing extension, or simple fix). By conducting deep context loading and guiding structured dialogue, it refines vague ideas into clearly defined, actionable scope boundaries. Finally, it proposes multiple concrete technical approaches, complete with trade-offs and estimates, ensuring the resulting project scope is well-defined and ready for implementation.

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.