StarDash

StarDash is an AI GitHub starred repository organizer that transforms saved repos into a searchable, curated developer workspace for software developers who use stars as a research backlog or evaluation queue. GitHub's native starred page records which repos you clicked but not why you starred them, which version you evaluated, or what you intended to do with them — StarDash solves that gap by syncing up to 5,000 repositories per pass and layering on tags, collections, notes, six workflow statuses, and a 24-hour client-side cache for fast repeat access.

The Discover feature treats search as intent-first: instead of matching on repo names, it parses natural-language queries like "prod-ready CLI frameworks," runs them against GitHub, and returns ranked results with evidence. Repo Intel provides a health score (displayed as a number out of 100) alongside maintenance, community sentiment, release cadence, and adoption signals — all surfaced in a single panel without opening external tabs.

The Contribute workspace scans your active starred repos for open issues, ranks opportunities by fit using a blend of labels, body context, recency, comments, and repo activity, and generates a structured AI contribution brief. That brief includes a summary, why the issue fits your profile, first steps, likely files, questions to ask, and a ready-to-copy coding assistant prompt compatible with Codex, Claude Code, and similar tools. StarDash is best for developers who actively use GitHub stars as a personal knowledge base and want a purpose-built tool to recover context and act on it.

StarDash is primarily used by software developers to organize and act on their GitHub star library — recovering context behind saved repositories, discovering relevant projects through intent-based search, and identifying open-source contribution opportunities through ranked issue triage.