What problem does it solve?
It reduces wasted engineering effort by forcing a systematic search for already-existing solutions (inside the repo, in the ecosystem, or via tools) before implementing anything custom.
Core Features & Use Cases
- Project-first discovery: performs semantic grep for relevant functions/components and checks documented patterns in the repository’s known tech directories, plus existing utilities in common code folders.
- Ecosystem and tooling search: evaluates maintained third-party libraries and checks MCP/skills/available CLIs that could cover the need.
- Decision matrix with justification: classifies outcomes as Reuse, Adopt, Extend, Compose, or Build, and requires a short research report before coding.
Quick Start
Ask the AI to evaluate your intended feature implementation using Search-First, produce a concise search report, and recommend whether to reuse, adopt, extend, compose, or build.