Skill UI

Ginkgo Cloud Lab provides remote access to an advanced autonomous lab infrastructure, enabling users to submit and manage complex molecular biology protocols. It supports diverse applications including protein expression and purification (cell-free, E. coli, Pichia), quantitative assays (HiBiT, A280, LabChip), nucleic acid synthesis (IVT mRNA/circRNA), and characterization techniques (Thermal Shift, Echo-MS, SPR). The platform features an AI agent (EstiMate) for custom workflow feasibility and pricing.

Stable Baselines3 is a PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms, including PPO, SAC, DQN, and TD3. It offers a scikit-learn-like API for easily training RL agents, developing custom Gymnasium environments, and managing complex training workflows. Ideal for standard RL research, rapid prototyping, and advanced model optimization.

The AI Dev Jobs MCP provides AI agents with real-time access to a massive index of AI and Machine Learning job listings across hundreds of companies. Users can search by role, location, or company, retrieve full job details, analyze salary ranges, list hiring companies, and get aggregate market statistics. It is essential for advanced job searching, talent matching, and labor market analysis workflows.

This comprehensive guide details the full plugin architecture for extending Claude Code. Learn how to build various extension points, including auto-activating skills, slash commands, specialized agents, and Model Context Protocol (MCP) servers. It serves as the definitive reference for integrating custom tools and workflows into the Claude ecosystem.

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

This workflow provides comprehensive audio processing capabilities, covering both real-time streaming transcription (for live captions and voice agents) and advanced post-processing using LeMUR. Use it for summarizing long meetings, answering questions based on transcripts, extracting action items, and running custom LLM analyses on recorded audio.

This guide demonstrates how to implement tool use (function calling) with Claude's Messages API. It covers defining structured tools using JSON Schema, handling tool calls, executing the corresponding code, and feeding the results back to create sophisticated, multi-step AI agents and complex workflows.

This skill provides a comprehensive framework for deploying Apple Notes automation as a persistent local service on macOS. It supports three distinct deployment models: using `launchd` agents for scheduled background tasks, creating Shortcuts for user-triggered actions, and includes a robust installer script. It is essential for building cross-application, system-level automation workflows within the Apple ecosystem.

This advanced guide covers core Anthropic workflow patterns, specifically focusing on tool use (function calling) and building complex, agentic loops. It provides detailed instructions on defining tools using JSON Schema, executing tool calls, and managing the iterative process required for building sophisticated, AI-powered agents using the Anthropic API.

This guide details how to use sophisticated coding agents (such as Codex, Claude Code, or Pi) through a structured bash workflow. It provides methods for complex development tasks, including building new features, refactoring large codebases, and reviewing pull requests (PRs). Key best practices covered include managing pseudo-terminals (PTY), defining focused working directories (`workdir`), and executing tasks in the background for robust, multi-step automation.

Learn how to build complex, multi-step AI agents using Cohere's Chat API v2. This skill demonstrates defining external tools (like weather APIs or database searches) and implementing the full agent loop: model calls tool -> execute tool -> feed result back -> final answer. Ideal for creating autonomous, tool-using workflows that interact with external systems.

This powerful orchestrator automates the entire GitHub issue lifecycle. It fetches issues based on criteria (labels, milestones, assignees), spawns specialized sub-agents to implement necessary code fixes, and automatically opens Pull Requests (PRs). Furthermore, it monitors open PRs, addresses review comments, and manages the remediation process, streamlining the development workflow from issue tracking to merge.