Skill UI

Skillshare is a comprehensive CLI tool designed to manage, sync, and organize AI CLI skills and agents across numerous AI platforms (e.g., Claude, Cursor). It supports both global and project-local modes, allowing users to initialize, install, and synchronize skills, rules, and custom prompts from a single source. Use it for setting up AI development environments, continuous synchronization, discovering new skills, and managing team collaboration (push/pull).

Detects system CPU, GPU, memory, and disk availability before heavy scientific tasks, writing `.claude_resources.json` with strategic recommendations (parallelism, out-of-core, GPU acceleration) so analysts choose appropriate compute approaches.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.

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.

A guide detailing four validated architectural patterns for integrating Claude APIs, catering to diverse scale and use cases. Choose between Serverless, Streaming Microservice, Queue-Based Pipelines, or complex Orchestrators to optimize performance, cost, and latency for your application, whether it's a real-time chatbot or large-scale batch processing.

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.

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 scaling Claude usage in high-throughput, enterprise-level applications. Learn four advanced strategies, including Message Batches for bulk processing, using p-limit for concurrency control, managing rate limit tier upgrades, and selecting the optimal model (e.g., Haiku) to balance speed and quality.