Extract structured data from websites using BeautifulSoup and requests - turn any webpage into usable data.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionweb-scraperExecute the skills CLI command in your project's root directory to begin installation:
Fetches web-scraper from guia-matthieu/clawfu-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate web-scraper. Access via /web-scraper in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Extract structured data from websites using BeautifulSoup and requests - turn any webpage into usable data.
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Strategic priorities |
| Synthesizes market data | Competitive positioning |
| Identifies opportunities | Resource allocation |
| Creates strategic options | Final strategy selection |
| Suggests implementation approaches | Execution decisions |
pip install beautifulsoup4 requests pandas click lxml
python scripts/main.py scrape https://example.com --selector "h1,h2,p"
python scripts/main.py scrape https://example.com --selector ".product-price"
python scripts/main.py links https://example.com
python scripts/main.py links https://example.com --internal-only
python scripts/main.py emails https://example.com
python scripts/main.py emails https://example.com --depth 2
python scripts/main.py structured https://example.com/article --schema article
python scripts/main.py structured https://example.com/product --schema product
python scripts/main.py scrape https://competitor.com/pricing --selector ".price,.plan-name"
# Output:
# Extracted 6 elements
# 1. Starter - $29/mo
# 2. Pro - $99/mo
# 3. Enterprise - Contact us
python scripts/main.py structured https://blog.example.com/post --schema article
# Output: article_data.json
# {
# "title": "How to Scale Your Startup",
# "author": "Jane Doe",
# "date": "2024-01-15",
# "content": "...",
# "word_count": 1523
# }
| Selector | Description | Example |
|---|---|---|
tag |
Element type | h1, p, div |
.class |
Class name | .price, .title |
#id |
Element ID | #main-content |
tag.class |
Tag with class | div.product |
tag[attr] |
Has attribute | a[href] |
parent > child |
Direct child | ul > li |
tag1, tag2 |
Multiple | h1, h2, h3 |
category: automation
subcategory: data-extraction
dependencies: [beautifulsoup4, requests, pandas]
difficulty: intermediate
time_saved: 5+ hours/week
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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parcadei/continuous-claude-v3
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ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
web-scraper has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in web-scraper — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: web-scraper is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for web-scraper matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in web-scraper — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
web-scraper has been reliable in day-to-day use. Documentation quality is above average for community skills.
web-scraper fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: web-scraper is focused, and the summary matches what you get after install.
web-scraper reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend web-scraper for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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