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Chrome Bookmark Organizer

Development Updated 2026.08.30

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About this skill

Problem

After long use, browser bookmarks become hard to reuse: folders are inconsistent, duplicates are scattered, dead links accumulate, and Chrome's exported HTML is not convenient for programmatic analysis. chrome-bookmark-organizer turns a Chrome bookmark export into a structured data workflow.

How It Works

  • Parse and normalize: convert Netscape Bookmark HTML into bookmarks.json, preserving folders and URLs.
  • Rule-based and AI-assisted classification: classify by domain or keyword first; for uncertain entries, generate prompt files under ai_prompts/ for an external model and feed the returned suggestions back into the organizer.
  • Link checking: optionally validate URLs asynchronously with aiohttp, producing link_status.json.
  • In-directory organizing: organizer.py classifies bookmarks inside their current folder only, never moves them across folders, prefers existing subfolders, and enforces a maximum depth of 3 and 100 items per layer.
  • Export and review: generate a new Chrome-importable HTML file and a default HTML report; use -f to choose markdown, text, or all.

Boundaries and Caveats

This is best treated as an offline snapshot organizer rather than a live Chrome sync tool. Cross-folder changes are suggestions only and require human confirmation. Classification quality depends on bookmark names, domain patterns, and review of AI suggestions, which are external and not automatically enforced.

Use Cases

  • Parse a Chrome bookmark export into JSON to inspect folder depth and URL distribution before cleanup.
  • Send rule-unmatched technical docs to AI for category suggestions, then merge the results back.
  • Batch-check stale bookmark URLs and save link_status.json before deciding what to delete.
  • Reorganize messy bookmarks inside existing folders and export a Chrome-importable HTML file.

Best For

  • Engineers maintaining personal technical bookmarks who want to flatten messy folders to no more than three levels.
  • Documentation admins curating shared libraries who need to parse exports, check dead links, and produce reports.
  • Knowledge-base migration engineers who need bookmarks converted to JSON and rule-based folders before import.
  • Knowledge-base editors reviewing AI classification who want technical docs grouped by domain and keyword into existing subfolders.