search-first

Research existing libraries before coding by running a structured discovery workflow across npm, PyPI, MCP servers, and GitHub. This process enforces need analysis, parallel search, evaluation, and a final decision to adopt, extend, compose, or build a solution.

The best way to evaluate dependencies for a new software feature is using a decision matrix. It guides you through parallel searches across web sources and package managers, then structures your choice to adopt, extend, compose, or build the integration.

You need to search npm or PyPI before starting feature development whenever you begin a new feature inception. This prevents reinvention by identifying existing tools, libraries, or patterns and evaluating their integration viability early.

Yes, you can use a structured search workflow to find reusable coding patterns from GitHub. The workflow evaluates findings from GitHub alongside npm, PyPI, and web sources in parallel, feeding them into a decision matrix for final selection.

The limitation of searching for existing solutions before coding is that the workflow requires strict adherence to need analysis and evaluation phases. Without proper handoff criteria, you risk choosing unsuitable dependencies or overcomplicating integration decisions.

Yes, the research-before-coding workflow supports MCP servers for finding solutions. It queries MCP servers in parallel with npm, PyPI, and GitHub to evaluate existing tools and patterns before deciding whether to adopt, extend, or build.