Skill UI

This expert tool orchestrates the complete onboarding experience for new team members in technical or remote-first organizations. It provides a structured roadmap covering pre-arrival preparation (access setup, documentation), Day 1 orientation, technical stack immersion (CI/CD, codebases), and cultural integration (buddy system). Use this when you need a comprehensive, step-by-step guide to rapidly transition new hires into productive, integrated team members.

Documented Go testing workflow covering TDD, table-driven cases, subtests, helpers, mocking, and HTTP handler verification so teams can ensure robust unit and integration coverage for their Go services.

Automates Google Address Validation workflows through Composio’s Rube MCP interface, guiding tool discovery, connection checks, and compliant execution using the latest schemas.

Automates Google Maps workflows via the Rube MCP (Composio) toolkit, letting you geocode addresses, search places, fetch directions, compute route matrices, and pull place details or autocomplete data while respecting live tool schemas.

Automates Gosquared operations via Composio’s Rube MCP, guiding you to add the MCP endpoint, discover live tool schemas, confirm active Gosquared connections, and safely execute multi-tool workflows while reusing sessions.

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.

A comprehensive guide for implementing standardized, robust error handling in modern APIs. It utilizes RFC 7807 Problem Details, centralized middleware, and typed error classes to convert diverse failure types (validation, auth, database, upstream service) into consistent, machine-readable responses with correct HTTP status codes. Essential for building production-grade, resilient backends.

HuggingFace Accelerate adds distributed support to any PyTorch script with just four lines, providing a unified API for DDP, DeepSpeed, FSDP, and Megatron, plus automatic device placement, mixed precision, and simple launch/config commands for fast prototyping.

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.

This skill guide provides advanced techniques for optimizing Ideogram API usage. It covers selecting optimal model tiers (e.g., Turbo vs. Standard) based on desired quality and speed, implementing prompt-based caching to reduce API calls and costs, and managing parallel generation with concurrency limits for high throughput. Additionally, it demonstrates CDN integration for rapid asset delivery.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.