Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Browser Bookmarks Cleanup
Analyze, organize, and clean browser bookmarks on macOS using on-disk bookmark and history files. Detects duplicates, stale bookmarks, tracking parameters, a...
skill openclawclawhub Free
Analyze, organize, and clean browser bookmarks on macOS using on-disk bookmark and history files. Detects duplicates, stale bookmarks, tracking parameters, a...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- references/plan-schema.md, scripts/apply_plan.py, scripts/discover.py, scripts/_common.py, scripts/browser_bookmarks.py, scripts/analyze.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- makkoncept
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
Discover browser profiles. Run read-only analysis and share findings. Propose cleanup plan with preview. Get explicit user approval before any writes. Dry-run first, then write with automatic backup. 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
Exact and semantic duplicate URLs Tracking-parameter variants (utm_*, gclid, fbclid, etc.) Subdomain and domain concentration HTTP links that should be HTTPS Empty, singleton, deep, and oversized folders Weak bookmark names Old bookmarks by age 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.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Scripts1 Docs
- references/plan-schema.md
- scripts/_common.py
- scripts/analyze.py
- scripts/apply_plan.py
- scripts/browser_bookmarks.py
- scripts/discover.py