Expert guidance on Swift Testing best practices, patterns, and implementation. Use when developers mention: (1) Swift Testing, @Test, #expect, #require, or @Suite, (2) "use Swift Testing" or...
Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
Generate feature specifications by analyzing existing source code.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...
Use when building MCP servers in TypeScript, Python, or C#; when implementing tools, resources, or prompts; when configuring Streamable HTTP transport; when migrating from SSE; when adding OAuth...
Generate daily work summaries from Slack messages, GitHub PRs, AI conversations, and Obsidian notes for a specified date range.
Comprehensive GitHub repository analysis for engineering managers with contribution stats, code quality review, team health metrics, and actionable management outputs
Build type-safe LLM applications with DSPy.rb - Ruby's programmatic prompt framework with signatures, modules, agents, and optimization
Model Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage...
Build high-quality Model Context Protocol (MCP) servers to integrate external APIs and services. Use when creating MCP tools, resources, or prompts.
Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.
Guides creation of Product Requirements Prompts (PRPs) - comprehensive requirement documents that serve as the foundation for AI-assisted development
Analyze a GitHub repo and generate Obsidian learning notes
Expert guide for building command-line interfaces with Node.js (Commander, Inquirer, Ora) or Python (Click, Typer, Rich). Use when creating CLI tools, terminal UX, argument parsing, or interactive prompts.
π― AI Orchestrator - Ultimate natural language to expert prompt translator. Analyzes intent with UltraThink, routes to specialized agents, and verifies execution. Use for: orchestrate, translate...