Skill UI

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.

A comprehensive tool designed to help developers gather all necessary evidence when facing persistent issues or API errors with Anthropic's Claude models. It collects vital data points such as the request ID, full error payloads, status codes, and provides reproducible cURL commands, ensuring developers can file high-quality support tickets.

A comprehensive guide for managing Anthropic API rate limits (RPM, TPM). Learn how to interpret rate limit headers, implement robust backoff strategies (exponential backoff with jitter), and optimize throughput using techniques like message batching and prompt caching to ensure reliable, high-volume API calls.

A comprehensive guide for developers on upgrading the Anthropic SDK versions and migrating between different Claude model generations. It covers best practices for version checking, reviewing breaking changes, updating model IDs across the codebase, running output comparison tests, and implementing gradual rollouts using environment variables to ensure stable deployment.

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 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 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.

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.