What problem does it solve? Developers often reinvent the wheel by writing custom utilities and integrations without checking whether maintained open-source packages, MCP servers, or existing skills already solve the problem, leading to unnecessary code and dependency bloat. ## Core Features & Use Cases - Structured Research Workflow: Runs a five-stage pipeline covering need analysis, parallel search across npm/PyPI, MCP servers, and GitHub, candidate evaluation, and an adopt/extend/build decision. - Decision Matrix: Scores candidates on functionality, maintenance, community, documentation, license, and dependencies to choose between adopting, extending, composing, or building custom. - Agent Integration: Launches a researcher subagent for non-trivial functionality and integrates with planner and architect agents before architecture decisions. - Use Case: When asked to add dead link checking to markdown files, the skill searches npm, finds textlint-rule-no-dead-link, and recommends adopting it directly instead of writing a custom checker. ## Quick Start Ask the AI to research existing tools and libraries for the functionality you are about to implement before writing any custom code.
