Skill UI

Automates Klaviyo email/SMS marketing via Rube MCP, letting you list campaigns, inspect messages, manage tags, and track send jobs while respecting schema discovery and pagination patterns.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

Automates updating a markdown document with an index/table listing files from a designated folder, handling discovery, structure selection, and syntax validation to keep documentation in sync.

Automates Zoho CRM operations through Rube MCP's Composio toolkit, letting you search, create, update, convert, and tag records by calling the outlined workflows while respecting schema discovery and authentication steps.

Transforms the AI into a highly personalized virtual tutor, adopting a defined persona, teaching style, and emotional relationship. This skill uses the Socratic method and advanced psychological principles (like Self-Determination Theory and memory scaffolding) to guide users through discovery learning. It doesn't just deliver answers; it fosters genuine engagement, maintains continuity across sessions, and motivates the user to learn by simulating the experience of a caring, challenging human mentor.

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

This comprehensive skill guides users through the entire lifecycle management of KubeSphere extensions. It covers extension discovery, dependency resolution, version verification, and executing controlled installations, upgrades, or uninstalls using the InstallPlan mechanism. It is essential for maintaining a stable and complex multi-component Kubernetes cluster environment.

A powerful utility for performing semantic searches across a large knowledge base of 105K+ skill cards. It allows users to find the most relevant skills based on natural language queries, supporting both Python API integration and Command Line Interface (CLI). This tool is zero-dependency and highly efficient for skill discovery and knowledge retrieval in development workflows.

This skill provides integration with the Adaptyv Bio Foundry API, enabling the automated design, submission, and analysis of protein experiments. Users can submit amino acid sequences for various high-throughput assays, including binding kinetics (BLI/SPR), thermostability, and expression yield. It guides the entire research workflow, from sequence input to final results retrieval, accelerating biopharma discovery.

Datamol provides a simplified, Pythonic abstraction layer over RDKit, making complex molecular operations accessible for chemical data science. It supports core functionalities including SMILES parsing, structure standardization, calculation of molecular descriptors (e.g., LogP, MW), 3D conformer generation, and efficient parallel batch processing. It is ideal for streamlining molecular data handling in drug discovery and chemical research pipelines.

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.