Skill UI

PyOpenMS provides Python bindings for OpenMS to drive complete mass spectrometry workflows, covering data IO, spectral processing, feature/peptide-protein identification, quantitation, and metabolomics pipelines.

Cloud-native Rowan orchestrates medicinal chemistry and molecular modeling pipelines via a Python API, offering pKa prediction, docking, descriptors, dynamics, and AI-driven structure workflows for scalable batch screening without local HPC.

This skill is designed to streamline the process of market research. It helps users gather comprehensive data on target markets, analyze industry trends, and identify key consumer insights. Use it to inform product development, refine business strategies, and make data-driven decisions.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

An AI-driven utility that guides users through selecting the most suitable Apify Actor for complex web scraping tasks. It provides comprehensive coverage across major social media platforms and websites, including Instagram, Facebook, TikTok, YouTube, and Google Maps, ensuring a unified workflow for efficient data extraction.

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Detailed guidance for a strict test-driven development cycle: write a failing test first, confirm the failure, add minimal code to pass, refactor cleanly, and repeat so every feature, fix, or refactor ships with confidence.

Guides authors through adapting test-driven development to agent skills documentation, covering when to create skills, required background, directory structure, and how to run pressure scenarios to ensure compliance before publishing.

Guides product and growth teams through evaluating whether to scale, test, or kill acquisition channels using unit economics (CAC, LTV, payback), customer quality (retention, NRR), and scalability indicators for data-driven go-to-market budgeting.

Guides teams through an empathy-driven problem framing that captures the persona, desired outcome, blockers, root causes, and feelings before jumping into solutions.