Create implementation plans and task breakdowns from feature requests by analyzing codebases. Generates structured documentation with requirements, numbered tasks, dependencies, and testing guidance for systematic development workflows.
Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.
Enables AI-guided spec-driven development workflow that transforms ideas into implementation through structured stages: goal collection, requirements gathering in EARS format, technical design documentation, task planning, and systematic code execution.
A proof-of-concept Model Context Protocol server that enables LLM applications to interact with Uber Eats, allowing AI agents to browse and order food through natural language.