Skill UI

This skill provides robust mechanisms to interact with the Clari API, specifically designed to handle rate limiting (HTTP 429 errors) and manage asynchronous bulk export jobs. It implements exponential backoff and sequential scheduling, ensuring reliable data retrieval and forecasting exports without hitting API limits. Ideal for automated data pipelines and background job processing.

A collection of production-ready client implementation patterns for interacting with the Clari v4 API, provided in both Python and TypeScript. This guide covers essential functionalities like listing and exporting forecast data, implementing job polling, and managing complex data transformation pipelines, ensuring reliable and reusable integration for development teams.

This guide provides advanced strategies for optimizing API usage and managing costs when integrating with ClickUp. It covers techniques such as aggressive caching of workspace structures, efficient pagination, batching multiple updates into single calls, and switching from constant polling to webhook-based event handling. Learning these methods helps developers stay within rate limits and maximize API efficiency.

This bundle collects comprehensive diagnostic information for troubleshooting ClickUp API v2 issues. It generates a redacted, safe bundle containing authentication status, rate limit details, workspace enumeration, and API latency measurements. Use this when preparing support tickets or investigating persistent connectivity problems.

Provides comprehensive guides and code examples for deploying ClickUp-powered applications to various cloud environments like Vercel, Fly.io, and Google Cloud Run. It covers secure secret management, API token handling, setting up health checks, and processing webhooks, ensuring robust and scalable SaaS integrations.

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.

A comprehensive, production-ready patterns library for interacting with the ClickUp API v2. It provides typed wrappers, robust error handling, rate limit management, and implements singleton/multi-tenant support, making complex ClickUp integrations reliable and scalable in TypeScript projects.

This guide provides a comprehensive architectural pattern (Adapter Pattern) for migrating code between different versions of the ClickUp API (v2 to v3). It addresses breaking changes, endpoint structural shifts, and terminology changes (e.g., 'Team' vs 'Workspace'), ensuring smooth and robust integration during large-scale platform upgrades.

This script automates the collection of comprehensive diagnostic evidence for Cohere API v2 troubleshooting. It gathers environment details (Node/Python versions), SDK versions, API connectivity tests, and detailed request/response logs, packaging everything into a single, support-ready bundle for efficient issue resolution.

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 to diagnose and resolve common CoreWeave infrastructure errors. It covers issues related to GPU scheduling, Pod status (e.g., Pending), CUDA Out of Memory errors, NCCL timeouts, and Kubernetes resource allocation within AI/ML deployment pipelines.