What problem does it solve?
Prevents wasted effort by ensuring developers research existing libraries, MCP capabilities, and OSS patterns before writing new custom code, reducing duplication and dependency bloat.
Core Features & Use Cases
- Parallel discovery: Searches npm, PyPI, internal MCP servers, and GitHub to surface existing implementations and packages.
- Evaluation matrix: Scores candidates by functionality, maintenance, documentation, license, and dependency impact to recommend adopt/extend/compose/build actions.
- Agent integration: Invokes a researcher agent for structured, repeatable research workflows when planning features or adding dependencies.
- Use Case: When adding an HTTP client wrapper, run the skill to discover proven libraries, evaluate trade-offs, and produce an adoption plan that minimizes custom code.
Quick Start
Launch the researcher agent to compare npm, PyPI, MCP servers, and GitHub for existing implementations of the requested functionality and return a recommended action with reasoning.