Skill UI

A comprehensive guide for configuring and managing organization-level access control within the Anthropic ecosystem. It details Workspaces setup (Production, Staging, Development), defines roles (Owner, Admin, Developer), and provides best practices for API key management. Includes practical Python code examples for implementing application-level Role-Based Access Control (RBAC) to restrict model access and usage limits.

This guide details how to set up and test Apify Actors locally using the Apify CLI. It emulates the full platform environment (including datasets, key-value stores, and request queues), allowing developers to run scrapers, test data handling, and debug the entire development lifecycle before deploying to the cloud. Perfect for rapid iteration and rigorous local testing.

This setup provides a dedicated local development workflow for integrating with the AppFolio property management API. It allows developers to build and test complex integrations using sophisticated mock endpoints for rapid iteration, eliminating the need to consume live API quotas. Users can toggle between mock mode for swift iteration and sandbox mode for rigorous validation against the actual AppFolio Stack API, ensuring reliable and efficient development.

This comprehensive guide details the end-to-end process for integrating Bright Data into development projects. It covers gathering zone credentials, setting up environment variables, downloading necessary SSL certificates, and configuring both Node.js and Python environments for robust and reliable web scraping proxy authentication.

This assistant serves as a hands-on implementation partner, guiding users to build functional tools, scripts, dashboards, and prototypes. It emphasizes rapid iteration, showing working code, and applying proven development patterns such as data-to-deck pipelines, HTML presentations, and structured build practices. Use it when the goal is to transform analysis or concepts into tangible, working software or robust automation.

A comprehensive guide detailing 10 common mistakes and best practices when integrating with the Anthropic API (Claude). It covers critical technical aspects such as correctly setting `max_tokens`, using the top-level `system` parameter, managing message alternation, checking `stop_reason`, and optimizing client initialization for robust AI application development.

This guide sets up a fast, cost-effective local development loop for building applications powered by the Anthropic API (Claude). It includes instructions for hot reloading, cost-saving tips (e.g., using Haiku, token limits), and mock client implementation for robust unit testing. It supports both TypeScript/Node.js and Python environments, streamlining the AI development workflow.

Provides best practices for configuring Large Language Model (LLM) access, specifically for Anthropic Claude, across development, staging, and production environments. This guide details setting up separate API keys, selecting appropriate models (e.g., Haiku, Sonnet, Opus), and managing unique rate limits for robust, scalable, and secure LLM integration in enterprise systems.

Automates the integration of ClickUp API within Continuous Integration/Continuous Deployment (CI/CD) pipelines using GitHub Actions. This skill allows users to run comprehensive unit and integration tests against the ClickUp API v2, and critically, enables syncing the final deployment status (e.g., 'deployed') back to the corresponding ClickUp task, ensuring task tracking is always aligned with development progress.

This guide provides a comprehensive strategy for configuring ClickUp API access across development, staging, and production environments. It emphasizes the critical importance of environment isolation using separate API tokens and dedicated workspaces to prevent data leaks and accidental destructive operations in live environments. Includes best practices for secret management (Vault, AWS, GCP) and implementing environment guards.

A comprehensive guide to configuring robust CI/CD pipelines for Cohere API v2 applications. This skill covers setting up automated unit tests (mocked responses) and critical integration tests (real API calls) within environments like GitHub Actions. It ensures the stability and reliability of Cohere-powered features such as Chat, Embeddings, and Rerank across development cycles.

This guide sets up a comprehensive, fast, and reproducible local development workflow for integrating with the Cohere API v2. It utilizes mocking, vitest for unit testing, and hot reload capabilities (tsx watch) to ensure rapid iteration cycles without constant reliance on external APIs. Ideal for developers building AI applications with Cohere.