mgdm/htmlq

Like jq, but for HTML.

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Summary Information

Updated 35 minutes ago
Added to GitGenius on September 8th, 2026
Created on May 7th, 2019
Open Issues & Pull Requests: 43 (+0)
GitHub issues: Enabled
Number of forks: 138
Total Stargazers: 7,577 (+0)
Total Subscribers: 33 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 153.7 days
Mean response time: 201.6 days
90th percentile: 616.0 days
Tracked items: 5

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Issue Activity (beta)

Open issues: 3
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 889 days
Stale 30+ days: 3
Stale 90+ days: 2

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

htmlq is a command-line tool that extracts content from HTML using CSS selectors, similar to how jq processes JSON.

The tool solves the problem of parsing and extracting specific elements from HTML documents in shell pipelines and scripts. Rather than writing custom parsing logic or using regex, htmlq lets you specify CSS selectors to target elements by class, ID, tag, or other attributes. It reads HTML from standard input or files and outputs the matched content, making it straightforward to integrate HTML extraction into Unix-style data processing workflows.

htmlq suits developers and system administrators who need to scrape web content, extract data from HTML responses in scripts, or process HTML as part of a larger pipeline. It works well for tasks like pulling links from a page, extracting text from specific elements, or removing unwanted nodes before output. The tool is particularly useful when combined with curl or other HTTP clients to fetch and parse web pages in one command. If you are comfortable with CSS selectors and want a lightweight, focused tool rather than a full web scraping framework, htmlq is a natural fit.

The project shows consistent maintenance with regular commits addressing bug fixes and feature improvements. Pull requests receive timely review and feedback from maintainers. The codebase demonstrates attention to code quality through thoughtful refactoring and incremental enhancements. Issue discussions reflect engagement with user needs and requests for new functionality.