Skill UI

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

This guide provides a comprehensive setup for a fast and reliable local development loop for applications powered by the Anthropic Claude API. It covers environment management, implementing hot reloading, tracking API usage costs, and utilizing mock responses to ensure a smooth, rapid iteration cycle for AI developers.

A comprehensive guide for configuring and managing Claude API access across disparate environments (Development, Staging, Production). This skill details how to implement isolated API keys, model selection strategies, and crucial spend controls using Anthropic Workspaces, ensuring secure and reliable deployment pipelines for AI applications.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.

Provides comprehensive security guidelines for integrating Anthropic Claude API. Covers essential best practices including secure API key management (rotation, isolation), defending against prompt injection attacks by separating system instructions from user input, robust input sanitization, and output validation to prevent accidental data leakage. Essential for building production-grade, secure AI applications.

This guide teaches developers how to implement robust, event-driven architecture patterns with the Anthropic Claude API. It covers two key methods: Server-Sent Events (SSE) for real-time streaming responses, and the Message Batches API for high-volume, asynchronous bulk processing. Use this when building real-time chat features or processing large datasets offline.

Configure robust CI/CD pipelines for Apify Actor builds and deployments using GitHub Actions. This guide covers setting up unit tests on pull requests, running deep integration tests against the live Apify API, and automating the production deployment process upon merge. Ideal for maintaining reliable, high-quality scraping and automation projects.

A comprehensive diagnostic reference for resolving common technical failures encountered when running web scraping and automation tasks on the Apify platform. Covers issues such as Actor run failures, API rate limits (429), authentication errors (401), anti-bot blocks, and memory allocation issues, providing structured fixes and best practices for robust scraping pipelines.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

A comprehensive tool designed to collect all diagnostic evidence for troubleshooting failed or problematic Apify Actor runs. This bundle gathers run metadata, detailed logs, dataset samples, usage statistics, and environment information, making it essential for preparing detailed support tickets or performing in-depth debugging.

This comprehensive checklist guides developers through the entire process of moving an Apify Actor from a local development environment to a stable, production-ready state. It covers crucial stages including configuration validation, setting up scheduled runs, implementing robust webhooks for monitoring, establishing cost controls, and executing proper rollback procedures to ensure reliable live operations.

This comprehensive guide outlines essential security best practices for users operating within the Apify ecosystem. Learn how to properly manage API tokens, implement environment separation (dev/staging/prod), secure proxy credentials, and sanitize sensitive data (PII) before storage. Essential for hardening data pipelines and ensuring compliance.