
Web Scraping
- 8 installs
- 33 repo stars
- Updated April 26, 2026
- bighardperson/computer-science-skills-collection
Web Scraping is a Claude skill that guides the agent to extract structured data from websites using web_fetch for static pages and browser automation for dynamic ones.
About
Web Scraping is a workflow skill that tells the agent how to extract data from sites using the lightest reliable method first. It uses web_fetch for simple public pages and browser automation for dynamic sites needing clicks, pagination, or login state. It normalizes results into a stable schema and enforces reliability rules like not inventing missing fields and deduplicating by URL. A developer uses it to collect listings or turn pages into structured JSON or CSV.
- Picks the lightest reliable method first: web_fetch for static, browser for dynamic
- Browser pattern: open, snapshot, interact only as needed, re-snapshot, close tabs
- Normalizes output into JSON, CSV/TSV, or bullet summaries with stable keys
Web Scraping by the numbers
- 8 all-time installs (skills.sh)
- Ranked #1,522 of 2,719 Automation & Workflows skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
web-scraping capabilities & compatibility
Free; relies on the agent's built-in web_fetch, browser, and web_search tools with no API keys.
- Capabilities
- web scraping · browser automation · content extraction
- Works with
- playwright
- Use cases
- web scraping · web search · data analysis
- Pricing
- Free
What web-scraping says it does
Extract data with the lightest reliable method first.
Use `browser` when the site is dynamic, needs clicking, infinite scroll, filters, tabs, or login/session state.
Do not invent missing fields.
Deduplicate by URL or stable item id.
npx skills add https://github.com/bighardperson/computer-science-skills-collection --skill web-scrapingAdd your badge
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| Installs | 8 |
|---|---|
| repo stars | ★ 33 |
| Last updated | April 26, 2026 |
| Repository | bighardperson/computer-science-skills-collection ↗ |
What it does
Collect fields from many web pages and normalize them into JSON or CSV using web_fetch or browser automation.
Who is it for?
Collecting listings or fields from many pages into JSON or CSV
Skip if: Sites that block access, where it advises saying so and switching sources
When should I use this skill?
The user asks to scrape a site, collect listings, or turn webpage content into structured data
What you get
Structured JSON, CSV, or a bullet summary with explicit stable keys
- JSON array of objects
- CSV/TSV rows
- concise bullet summary
By the numbers
- 3-option approach selection
- 7-step default workflow
- 6-step browser scraping pattern
Files
Web Scraping
Extract data with the lightest reliable method first.
Choose the approach
1. Use web_fetch for simple public pages when the needed content is already in HTML. 2. Use browser when the site is dynamic, needs clicking, infinite scroll, filters, tabs, or login/session state. 3. Use web_search only to discover candidate pages when the target URL is unknown.
Default workflow
1. Identify the target site and exact fields to collect. 2. Test one page first. 3. Decide the extraction method:
web_fetchfor readable article/listing textbrowser snapshotfor dynamic DOM inspection
4. Normalize the output into a stable schema. 5. If scraping multiple pages, avoid tight loops and serialize requests. 6. Deduplicate by URL or stable item id. 7. Save results in the workspace when the task is larger than a quick one-off.
Browser scraping pattern
1. Open the page. 2. Take a snapshot. 3. Interact only as needed: search, click filters, pagination, expand sections. 4. Re-snapshot after each meaningful state change. 5. Extract only the fields the user asked for. 6. Close tabs when finished.
Output guidance
Prefer one of these formats:
- concise bullet summary
- JSON array of objects
- CSV/TSV when the user wants exportable rows
Use explicit keys, for example:
[
{
"title": "...",
"url": "...",
"source": "...",
"date": "...",
"summary": "..."
}
]Reliability rules
- Do not invent missing fields.
- If a site blocks access, say so and switch sources when appropriate.
- For news/results pages, capture source + title + link at minimum.
- For large jobs, checkpoint partial results to a workspace file.
- Prefer fewer larger writes over many tiny writes.
Cleanup
- Close browser tabs opened for scraping.
- If you create state/output files, store them under the workspace and name them clearly.
{
"ownerId": "kn77b3a5vyd3099hk2ce4p3hy1834zn9",
"slug": "web-scraping",
"version": "1.0.0",
"publishedAt": 1773817868323
}{
"version": 1,
"registry": "https://clawhub.ai",
"slug": "web-scraping",
"installedVersion": "1.0.0",
"installedAt": 1776068820603
}
Related skills
FAQ
When should this skill use a browser instead of web_fetch?
It uses web_fetch for simple public pages when the content is already in HTML, and switches to browser automation when the site is dynamic or needs clicking, infinite scroll, filters, tabs, or login/session state.
How does it avoid duplicate data?
It deduplicates by URL or a stable item id, and for large jobs it checkpoints partial results to a workspace file.