Skill UI

A comprehensive guide and mechanism for ensuring development work occurs in an isolated environment using native worktree tools or falling back to manual git worktree creation. It guides users through mandatory steps like detecting existing isolation, prioritizing native tools, and verifying the clean state before development begins.

A comprehensive coach guiding developers through the entire SaaS lifecycle: from initial idea and scaffolding (Next.js) to building MVP features, integrating AI, setting up authentication, implementing payment systems (Stripe/LemonSqueezy), and achieving global deployment (Cloudflare/Vercel). Focuses on pragmatic, 'ship first' architecture decisions for solo developers and indie hackers.

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.

Cirq is an open-source Python framework from Google Quantum AI for designing, simulating, and executing quantum circuits. It supports multiple hardware backends (IonQ, Pasqal, etc.) and includes comprehensive tools for noise modeling, circuit optimization, and parameterized experimentation, making it ideal for NISQ device development and advanced quantum computing research.

This skill acts as an elite Art Director for generating premium, highly actionable, and structurally sound frontend design references. It moves beyond generic AI aesthetics by enforcing principles like strong hierarchy, generous spacing, and image-led composition. Ideal for creating realistic, development-ready concepts for landing pages, hero sections, and multi-section websites.

This skill enables the development of robust, production-ready Python protocols for Opentrons Flex and OT-2 robotic systems. It provides comprehensive tools for labware setup, precise liquid handling, module control, and advanced simulation, ensuring safe and highly reproducible automated biological and chemical workflows.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

Qiskit is a comprehensive SDK for developing quantum algorithms. It enables users to build, optimize, simulate, and execute quantum circuits using modern APIs (Qiskit 2.x). It supports local exact sampling, high-performance noisy simulation via Qiskit Aer, and direct execution on IBM Quantum hardware through the Runtime service, making it a powerful tool for quantum research and development.

Provides developmental, qualitative, and evidence-traceable review of scholarly works (papers, protocols, ideas). It also rigorously audits low-stakes research assessment rubrics for construct validity, fairness, and governance. Crucially, this tool is strictly forbidden from making high-impact personnel decisions, ranking individuals, or determining final outcomes.

scVelo is a leading Python package for analyzing RNA velocity in single-cell RNA-seq data. It infers cell state transitions and developmental trajectories by modeling the kinetics of mRNA splicing (unspliced/spliced ratios). This allows researchers to determine cell fate decisions, predict differentiation paths, and identify driver genes without requiring time-course experiments. It complements tools like Scanpy for comprehensive analysis.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This skill provides an interactive, comprehensive quality review for technical design documents. It systematically validates a feature's readiness for implementation by assessing critical issues, identifying strengths, and providing a clear GO/NO-GO decision with actionable next steps. Ideal for ensuring architectural soundness and catching risks early in the development lifecycle.