seed-gatherer

To gather project evidence for AI planning, you systematically filter and extract content from URLs, PDFs, and local documents into a structured, intent-aligned corpus. This ensures your downstream AI planner receives a clean dataset matching your specific project goals and constraints.

Intent-aligned evidence synthesis is the process of filtering diverse project materials against specific success criteria to create an AI-ready dataset. You need it when starting new features to ensure downstream planners work from a unified, validated corpus rather than fragmented requirements.

You validate feature feasibility using an ideation workflow that facilitates structured dialogue to crystallize abstract ideas. This process transforms rough concepts into actionable seeds complete with built-in feasibility checks before any planning begins.

Yes, evidence gathering integrates directly with git worktrees to maintain clean state management. This integration preserves audit trails for complex AI-driven development chains, ensuring your structured seed artifacts remain isolated and verifiable.

Yes, you can use yt-dlp to extract video content as part of systematic evidence gathering. The tool filters video sources alongside web and local documents to ensure all extracted data aligns with your project's intent and feasibility constraints.

The best way to structure fragmented research is through intent-filtered extraction, which automatically ingests links, competitor docs, and brainstorming notes. It filters this content based on your specific goals, outputting a unified, AI-ready seed corpus.