Skill UI

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

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.

This guide provides comprehensive steps to install and configure authentication for the BambooHR REST API. It utilizes HTTP Basic Authentication, requiring users to set up environment variables with their API key and company domain. The content includes setup instructions for Node.js and Python, along with full code examples for verifying a successful connection and retrieving employee directory data.

A comprehensive diagnostic reference for common Bright Data proxy and API errors, including 407, 502, SSL Certificate errors, and rate limit issues. This guide provides specific causes, detailed fixes, and command-line solutions (cURL, Python, JavaScript) to ensure uninterrupted web scraping and API integration.

This guide provides comprehensive, step-by-step instructions for installing and authenticating the Anthropic SDK. It covers setup for both Python and Node.js environments, detailing how to securely configure API keys using environment variables. Essential for developers starting new projects that integrate with Claude models.

This guide provides advanced methods to significantly boost Clari API performance and data pipeline efficiency. It covers parallelizing multi-period data exports using Python's concurrency features, implementing a robust local caching mechanism with Time-To-Live (TTL), and optimizing data warehouse loading using SQL MERGE statements. Use this when facing slow exports, high API call volume, or requiring fast data synchronization in multi-forecast environments.

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 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.

A comprehensive guide providing minimal working examples for core Cohere API v2 endpoints, including Chat completion, text embedding, and search reranking. This resource details basic setup, usage patterns, and includes runnable code examples in both TypeScript and Python, making it perfect for new AI integrations or learning advanced LLM functionality.

Provides production-ready client patterns for interacting with the Fathom API, implemented in both Python and TypeScript. This SDK wrapper is designed for building reusable client services, implementing robust meeting data pipelines, or integrating Fathom's core features (like transcripts and summaries) into specialized applications.

This guide provides production-ready patterns for integrating with the Grammarly API using both TypeScript and Python. It covers essential architectural aspects, including typed client implementation, robust token management (OAuth flow), and efficient text chunking for processing large documents. Use this when building scalable, reliable Grammarly-powered features into your application.