Skill UI

This tool generates a complete, production-ready PHP Model Context Protocol (MCP) server project. It automatically scaffolds the entire structure, including tools, resources, prompts, and comprehensive tests, utilizing the official PHP SDK. It provides a standardized, robust starting point for developers building AI-integrated backend services in PHP.

Generates detailed prompts to document end-to-end application workflows by auto-detecting architectures, stacks, entry points, services, data access, error handling, and tests across .NET, Java/Spring, React, microservices, and other tech stacks.

A comprehensive guide and toolkit for optimizing and monitoring consumption-based costs across the Adobe Creative Cloud ecosystem. It provides strategies and code snippets for tracking usage (credits, transactions) for Firefly, PDF Services, Photoshop, and Lightroom APIs. Use this when implementing usage monitoring, managing billing alerts, or reducing operational expenses related to Adobe API calls.

Comprehensive runbook for managing Adobe API integration outages, covering Firefly, PDF Services, and IMS authentication failures. Provides structured procedures for initial triage, determining severity (P1-P4), executing recovery steps (Auth/Rate Limit), implementing fallback modes, and documenting post-incident reports and communication.

A robust, production-ready architecture pattern for integrating with multiple Adobe APIs, including Firefly Services, PDF Services, and I/O Events. This pattern enforces a layered structure (API -> Service -> Client -> Infrastructure), ensuring clean separation of concerns, centralized authentication, robust error handling boundaries, and scalable workflow orchestration for enterprise applications.

This comprehensive guide outlines essential security best practices for integrating with Adobe APIs. It covers securing OAuth credentials, implementing least-privilege scoping, managing zero-downtime credential rotation, and verifying I/O Events webhook signatures using cryptographic methods like RSA-SHA256. Essential for building secure, production-ready applications connected to Adobe services.

This guide provides comprehensive instructions for setting up the AssemblyAI SDK in both Node.js and Python environments. It covers package installation, secure API key management using environment variables, and client initialization for utilizing transcription and streaming services.

A comprehensive setup guide for establishing a fast, reproducible local development environment for AssemblyAI projects. It incorporates hot reloading, local caching of transcription results, and mocked testing utilities to ensure rapid iteration cycles without relying on live API keys or incurring costs. Ideal for setting up new services or enhancing existing AI workflows.

This setup provides a comprehensive local development workflow for integrating with the ClickUp API v2. It includes mock services, unit tests, and live integration testing, enabling developers to build and test ClickUp features efficiently with hot reloading and reliable API mocking, minimizing reliance on live credentials during development.

Implements a robust, multi-tenant authorization layer for Cohere API v2. This solution enforces Role-Based Access Control (RBAC), manages individual API keys per team, and implements per-team usage limits and daily budget tracking. It is essential for large-scale applications requiring strict resource isolation and cost governance across multiple internal services.

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

A comprehensive guide and set of tools for establishing a local development workflow tailored for CoreWeave GPU deployments. This includes steps for building Docker containers, validating Kubernetes YAML manifests using dry-run, and managing the full deployment lifecycle for AI inference services before pushing to the cloud.