browsing-history

Query and summarize Chrome history from a local SQLite database.

3|Updated Feb 6, 2026

Search Tags:#SQLite #Chrome #data-extraction #multi-device #browsing-history #LLM-categorization

Ready to run

npx skills add https://github.com/pinion05/skills --skill browsing-history

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: browsing-history
Source: https://github.com/pinion05/skills/tree/main/browsing-history
Command: npx skills add https://github.com/pinion05/skills --skill browsing-history

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill helps you extract and summarize your Chrome browsing history across all synced devices, turning scattered links into organized insights.

Core Features & Use Cases

Natural-language queries: Filter by date ranges like yesterday, last week, or custom windows.
Device-aware search: Query history by iPhone, iPad, Mac, desktop, or all devices.
Export formats: Output results as Markdown or JSON for note-taking or dashboards.
Optional categorization: Use an LLM to classify pages into content categories.
Use case example: Imagine compiling a reading log of AI articles read last week and exporting to Obsidian.

Quick Start

Run: python3 ~/.claude/skills/browsing-history/browsing_query.py "yesterday" --limit 50
Or: python3 ~/.claude/skills/browsing-history/browsing_query.py "last week" --device desktop --format json --output history.json

Frequently Asked Questions about browsing-history

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I query Chrome browsing history across multiple synced devices?▼

Query multi-device Chrome browsing history by running a Python script against a local SQLite database, filtering by natural-language time ranges and specific devices like iPhone or desktop to return organized results.

Can I export browsing history to Markdown or JSON for Obsidian?▼

Yes, you can export browsing history to Markdown or JSON formats for Obsidian workflows, outputting filtered and grouped results directly to files for note-taking or dashboard integration.

What is needed to extract and categorize Chrome browsing data with an LLM?▼

Extracting and categorizing Chrome browsing data requires a local SQLite database file and a Python query script, while optional LLM categorization needs the llm CLI enabled to classify pages into content categories.

Does this browsing history extraction tool support natural-language date ranges?▼

Yes, the browsing history extraction tool supports natural-language date ranges such as yesterday or last week, enabling quick temporal filtering without writing complex SQL queries manually.

What are the limitations of summarizing browsing history from a local SQLite database?▼

The main limitation is the strict dependency on a local SQLite database at a specific path, meaning it cannot query cloud history directly and requires synced data to be present locally beforehand.