Skill UI

Tamarind Bio is a cloud platform providing managed GPU access to hundreds of open-source computational biology tools. It facilitates complex tasks such as protein structure prediction (AlphaFold), de novo protein design, antibody engineering, protein-ligand docking, and molecular dynamics simulations. Users submit jobs via a unified API and receive predicted structures and biophysical scores without needing local high-performance computing resources.

This skill validates the correctness and completeness of any API request. It rigorously checks the HTTP method, URL structure, required headers, body format, authentication details, and query parameters. Use it to debug cURL commands, verify endpoint calls, or ensure API structures adhere to best practices, providing immediate, actionable feedback on required fixes.

Acts as a Chief of Staff and strategic agent to guide founders in documenting an entire company from a raw vision. It uses a phased, interview-driven methodology (12 phases, covering strategy, market, finance, etc.) to build a comprehensive Open Knowledge Format (OKF) bundle. The resulting repository is a structured, interconnected graph of Markdown files, perfectly readable by humans and AI agents.

This advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

A comprehensive guide on creating developer-centric changelogs and release notes. Learn how to structure updates using Semantic Versioning and the Keep a Changelog format. This skill helps communicate new features, breaking changes, deprecations, and performance improvements in a way that developers genuinely care about, building trust and demonstrating product momentum.

This guide details advanced strategies for publishing technical content across major developer platforms like Dev.to and Hashnode. It covers cross-posting best practices, canonical URL management for optimal SEO, and platform-specific optimization (e.g., tagging, content performance) to maximize reach and build authority.

Learn how to transform your technical documentation from a mere resource into a powerful marketing and conversion channel. This skill covers optimizing information architecture, structuring compelling quickstarts, and applying developer-centric principles to attract, convert, and retain developers, enhancing overall product adoption.

Utilize this skill to assess the health, adoption, and performance metrics of published EAS Updates directly via the CLI. It reports critical data points including crash rates, unique user counts, install volumes, payload size, and the split between embedded and OTA users, making it essential for post-publish monitoring and regression detection.

EAS Observe is a comprehensive tool for monitoring performance metrics in production Expo applications. It tracks critical performance data, including app startup time, navigation performance across routes, and custom user events. Developers can integrate it into their Expo project using the `expo-observe` package and query detailed reports via the EAS CLI, ensuring optimal user experience and stability.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

This professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.