What problem does it solve?
It prevents incorrect or outdated framework-specific code by forcing every implementation decision to be validated against official documentation instead of relying on memory or stale training data.
Core Features & Use Cases
- Version-aware stack detection: Reads dependency files to identify the exact framework/library versions before making any recommendations.
- Authoritative documentation fetching: Uses Context7 MCP (or official-doc web fallbacks) to retrieve the relevant patterns directly from primary sources.
- Conflict surfacing and citation enforcement: Requires full, linkable citations and explicitly highlights mismatches between existing code patterns and current docs.
- Unverified-by-default behavior: Flags anything not found in authoritative sources rather than guessing.
Use cases: implementing framework-specific forms, routing, data fetching, state management, and authentication patterns when correctness depends on current “documented” guidance.
Quick Start
Ask the AI to implement your feature using source-driven development and include official doc citations for every framework-specific pattern, starting by detecting your stack versions from your dependency files.