Skill UI

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

A comprehensive reference architecture for deploying meeting intelligence platforms (like Granola) across large organizations. This blueprint details the core data capture pipeline, integration topology using middleware (Zapier), multi-workspace structure (Sales, HR, Engineering), and robust security layers (SSO, AES-256) to ensure automated workflows from meeting to action item across all departments.

This comprehensive guide details advanced automation patterns for Granola, covering three key integration channels: Zapier workflows (connecting Granola to over 8,000 apps), the robust Enterprise API (REST access), and native integrations (e.g., Slack, Notion). Use these patterns to automate meeting note processing, extract action items, and build multi-step follow-up pipelines across your entire tech stack.

Configure robust CI/CD pipelines for Groq API integrations using GitHub Actions. This guide covers setting up unit tests (mocked), live integration tests, and automated model deprecation checks. It ensures that your LLM-powered application remains stable and compatible as models evolve.

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

A comprehensive guide to building scalable, event-driven architectures using the Groq API. Learn advanced patterns including Server-Sent Events (SSE) for real-time streaming, batch processing with job queues (e.g., BullMQ), and processing incoming webhook events for robust LLM pipelines.

This skill automates and configures robust Continuous Integration/Continuous Deployment (CI/CD) pipelines specifically for Guidewire applications. It handles the entire development lifecycle, including Gosu code compilation, running GUnit unit tests, building configuration packages, and deploying changes via the GCC API. It supports separating pipelines based on whether changes are in code or configurations.

High-performance Rust-backed tokenization toolkit with Python bindings, offering BPE/WordPiece/Unigram, custom vocabulary training, alignment tracking, and production-ready integrations for NLP pipelines.

This comprehensive guide details how to set up robust CI/CD pipelines for projects integrating with Ideogram. It covers automated unit testing, optional integration tests using GitHub Secrets, and crucial prompt validation logic directly within GitHub Actions workflows, ensuring high code quality and reliable API interaction.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

This comprehensive guide provides a multi-platform deployment solution for Ideogram-powered image generation APIs. It includes detailed code and setup steps for Vercel, Google Cloud Run, and self-hosted Docker containers. The solution addresses key production concerns, including secure secret management (API keys), handling generation timeouts, and ensuring generated images are persisted to durable cloud storage (like S3) with CDN integration for reliable service operation. Use this when migrating Ideogram prototypes to a production environment.

A comprehensive reference architecture for implementing large-scale AI image generation using Ideogram. This system guides developers through building robust pipelines, including structured prompt templating (for brand consistency), utilizing multiple API endpoints (Generate, Edit, Remix), and implementing full asset workflow management from generation to final CDN delivery. Ideal for establishing brand asset systems and scalable AI integrations.