Skill UI

Provides minimal working examples and comprehensive instructions for querying the Fireflies.ai GraphQL API. Users can learn how to list workspace users, fetch recent meeting transcripts, and retrieve detailed summaries, including action items and keywords, using code examples in TypeScript, Python, and Bash. Ideal for initial API setup, integration testing, or learning GraphQL patterns for meeting data.

This skill provides a comprehensive guide and multiple code examples (cURL, Node.js, Python) for generating presentations using the Gamma API. It implements the standard asynchronous 'Generate-Poll-Retrieve' pattern: first POST to create the generation job, then GET to poll the status until complete, and finally retrieve the viewable and exportable URLs (PDF/PPTX).

This skill outlines the complete workflow for capturing and structuring meeting notes using Granola. It captures audio, transcribes the content, and intelligently merges live user notes with the full transcript. The output is structured into key sections like Summaries, Decisions, and Action Items. Users can also interact with Granola Chat for post-meeting Q&A, making it an essential tool for professional meeting documentation and follow-up.

A comprehensive guide to using Groq's high-speed LPU for chat completion. This resource provides code examples in TypeScript and Python, covering basic chat interactions, streaming responses, and advanced multimodal features. Ideal for developers building high-performance, low-latency AI applications using the Groq API.

This skill provides foundational examples for interacting with Guidewire's core cloud APIs, including PolicyCenter, ClaimCenter, and BillingCenter. It demonstrates how to execute initial RESTful API calls, query resources (like accounts and claims), and understand common API response structures, pagination, and error handling patterns. Ideal for connectivity testing, learning Guidewire's REST structure, and prototyping basic insurance integrations.

A comprehensive guide demonstrating how to generate your first AI image using the Ideogram API. It provides runnable examples in cURL, TypeScript, and Python, covering both legacy and V3 endpoints. Users will learn essential API parameters, such as prompt structuring, style types, and aspect ratios, making it ideal for initial integration testing and setup.

This minimal working example demonstrates core API interactions with Instantly.ai v2. It shows how to list active campaigns, check email account health/vitals, and pull detailed campaign analytics (open rate, reply rate). Ideal for developers setting up new integrations or testing API setup.

This skill demonstrates how to use the Juicebox SDK to perform advanced people searches and profile enrichment. Users can search for individuals using natural language queries (e.g., "senior ML engineer at FAANG with PhD") and retrieve detailed contact information, skills, and education data for professional outreach and market research. Supports both TypeScript and Python.

A comprehensive guide for developers on utilizing the Kling AI Text-to-Video REST API. This skill provides minimal working examples in Python and Node.js, demonstrating the complete workflow: submitting a text prompt, polling the task status, and retrieving the final generated video URL. Ideal for integrating advanced AI video generation capabilities into development projects.

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.