browser-bookmarks-cleanup

Analyze, organize, and clean browser bookmarks on macOS using on-disk bookmark and history files. Detects duplicates, stale bookmarks, tracking parameters, and folder issues. All writes are opt-in with backup and rollback.

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Install skill "browser-bookmarks-cleanup" with this command: npx skills add makkoncept/browser-bookmarks-cleanup

Browser Bookmark Cleanup

Analyze first, organize, then clean up. All writes are opt-in and reversible.

Supported browsers: Chrome and Firefox out of the box. Other Chromium-based browsers (Edge, Brave, Arc, etc.) can be handled by pointing to their profile directories, which use the same Bookmarks JSON format as Chrome.

Workflow

  1. Discover browser profiles.
  2. Run read-only analysis and share findings.
  3. Propose cleanup plan with preview.
  4. Get explicit user approval before any writes.
  5. Dry-run first, then write with automatic backup.
  6. Report changes and how to roll back.

Hard Safety Rule

Never send network requests to bookmark URLs. Analysis uses only local files.

Commands

Discover profiles:

python3 scripts/browser_bookmarks.py discover [--browser chrome|firefox]

Analyze bookmarks:

python3 scripts/browser_bookmarks.py analyze --bookmarks "<PATH>" --output /tmp/bookmark-analysis.json

Apply plan (dry-run by default, --write to commit):

python3 scripts/browser_bookmarks.py apply-plan --bookmarks "<PATH>" --plan /tmp/bookmark-plan.json [--write]

Analysis Categories

  1. Exact and semantic duplicate URLs
  2. Tracking-parameter variants (utm_*, gclid, fbclid, etc.)
  3. Subdomain and domain concentration
  4. HTTP links that should be HTTPS
  5. Empty, singleton, deep, and oversized folders
  6. Weak bookmark names
  7. Old bookmarks by age
  8. Never-visited and stale bookmarks (from browser history)

Approval Gate

Before any write: show plan preview, get explicit approval, ask user to close the browser.

Plan Format

See references/plan-schema.md for the JSON plan structure.

Rollback

Restore the timestamped backup file created before any write.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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