ai-llm-patterns
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
162 skills found
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Seamlessly toggle between live and mocked external dependencies using the Model Context Protocol (MCP) for autonomous development environments.
Guide for integrating and managing custom Model Context Protocol (MCP) servers within the Cursor IDE environment.
Standardized debugging and diagnostic guidelines for AI coding agents.
Frontend coding conventions for Preact and Tailwind. Use for web UI components in cluster applications.
Generate TestBox BDD test specs for Wheels models, controllers, and integration tests. Supports validations, associations, and workflow testing.
Controls a local or remote headless browser for automated web navigation, data extraction, form interaction, and testing from sandboxed environments.
Audit and synchronize the supported LLM model list in assets.py against the authoritative litellm registry.
Standardized React UI patterns for loading states, error handling, and data fetching to ensure consistent UX and robust component architecture.
Integrates browser-native Proofreader API into web applications for AI-powered text correction, grammar checking, and language support with managed model lifecycle.
Enriches vague prompts by performing codebase research and asking targeted questions to clarify user intent before execution.
A CLI tool that automates the discovery and symlinking of agent skills distributed via npm packages, simplifying integration for AI-powered coding agents.