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SKILL FILE

Scrape Wikipedia with AI

Extract Wikipedia articles, infobox data, categories, and structured content using Apify and Claude Code.

1.5B+ Wikipedia users or records
2,000 items scraped per minute
$0.05 per 1,000 pages
Download Skill File ↓
CROSS-DEPARTMENT FLOW

How scraped Wikipedia data flows across your company

One scrape generates intelligence for every department — automatically

Scrape Wikipedia Wikipedia articles, infobox data, categories, and structured content
1 Configure Targets
2 Apify Actor Runs
3 Data Processed
4 Stored in CRM
Sales
  • → Identify prospects from scraped data
  • → Track competitor activity
  • → Source outreach targets
  • → Build lead lists
Marketing
  • → Content research and ideation
  • → Competitor strategy analysis
  • → Trend monitoring
  • → Audience insights
Growth
  • → Market sizing and analysis
  • → Engagement benchmarking
  • → Growth opportunity identification
  • → Platform trend tracking
CRM
  • → Data records stored
  • → Engagement metrics indexed
  • → Source attribution tagged
  • → Historical data tracked
Lead List
Research Report
Trend Analysis
Market Report
Events Tracked
Wikipedia data collected
Patterns identified
Benchmarks established
Replaces Custom development
$10/mo $0.50/mo
$114/yr saved
Scrape Wikipedia Wikipedia articles, infobox data, categories, and structured content
1
Configure Targets Wikipedia URLs, keywords, or filters defined
2
Apify Actor Runs Scraper extracts data — $0.05/1,000 pages
3
Data Processed Records cleaned, scored, and categorized
4
Stored in CRM Intelligence pushed to Neon database with attribution
Sales
  • → Identify prospects from scraped data
  • → Track competitor activity
  • → Source outreach targets
  • → Build lead lists
Marketing
  • → Content research and ideation
  • → Competitor strategy analysis
  • → Trend monitoring
  • → Audience insights
Growth
  • → Market sizing and analysis
  • → Engagement benchmarking
  • → Growth opportunity identification
  • → Platform trend tracking
CRM
  • → Data records stored
  • → Engagement metrics indexed
  • → Source attribution tagged
  • → Historical data tracked
Content Outputs
Research Report from marketing
Lead List from sales
Trend Analysis from marketing
Market Report from growth
Everything Tracked
Wikipedia data collected
Patterns identified
Benchmarks established
Replaces Custom development
$10/mo → $0.50/mo
$114/yr saved
REPLACES

Cancel your Custom development subscription

CANCEL THIS

Custom development

$10/mo
  • × Subscription fees
  • × Data locked in their dashboard
  • × Per-seat pricing
  • × Export limits
vs
BUILD THIS

SoloStack + Claude Code

$0.50/mo
  • ✓ Pay-per-use, no subscription
  • ✓ Your data in your repo
  • ✓ Zero vendor lock-in
  • ✓ Unlimited exports
Save $114/year
WHAT YOU GET

What this skill file teaches Claude

Drop one markdown file into your repo. Claude Code learns how to run this entire workflow.

1

Data Extraction

Pull key data points from Wikipedia including profiles, content, and metadata.

2

Search & Filter

Search by keywords, categories, or specific URLs to target exactly what you need.

3

Engagement Metrics

Capture engagement signals — views, likes, shares, and comments for every item.

4

Bulk Processing

Process hundreds or thousands of records in a single run with automatic pagination.

5

Export & Integration

Output clean JSON ready for CRM import, analysis, or integration with other tools.

Apify Actor: apify/wikipedia-scraper · ~$0.05 per 1,000 pages
IN ACTION

Build it with plain English

Tell Claude Code what to do. It handles the rest.

claude — solostack/
you: | 
Processing Wikipedia data...

✓ Data extracted successfully
✓ 234 records collected
✓ Cleaned and deduplicated
✓ Ready for CRM import

Data saved to scrape-wikipedia-results.json
you: | 
Processing Wikipedia data...

✓ Data extracted successfully
✓ 567 records collected
✓ Cleaned and deduplicated
✓ Ready for CRM import

Data saved to scrape-wikipedia-results.json
you: | 
Processing Wikipedia data...

✓ Data extracted successfully
✓ 89 records collected
✓ Cleaned and deduplicated
✓ Ready for CRM import

Data saved to scrape-wikipedia-results.json
USE CASES

What you can build with this

Knowledge base building

Extract structured data from Wikipedia to build knowledge bases about companies, people, or topics.

Entity enrichment

Enrich CRM records with Wikipedia data — company descriptions, founding dates, headquarters, etc.

Content research

Pull comprehensive background information on topics for content creation and research.

Competitive mapping

Extract company infoboxes for competitors to build comparison databases.

IMPORTANT

Things to know

!

Wikipedia content is CC-BY-SA licensed. Attribution required if republishing.

!

Wikipedia data quality varies by article. High-traffic articles are generally reliable.

!

Wikipedia infobox structure varies between articles. Parsing requires flexibility.

COMPLETE SKILL FILE

Get the full skill file

Everything above is 80% of the skill file. Download the complete version with full implementation details, agent prompts, and ready-to-run scripts.

FAQ

Common questions

Scraping publicly available data from Wikipedia is a gray area. Most courts have upheld that public data can be accessed for research purposes. Always respect the platform's ToS, use data for internal research only, and comply with GDPR/CCPA when handling personal information.
For trend monitoring, weekly scrapes capture meaningful changes. For competitive analysis, bi-weekly to monthly is sufficient. The optimal frequency depends on how quickly data changes on the platform.
The Apify actor uses residential proxies and request throttling to minimize blocks. If you experience issues, reduce request volume, increase delays between requests, and consider running scrapes during off-peak hours.
Yes. The output is clean JSON that can be directly imported into Neon (Postgres), Airtable, or any CRM with an API. Use the TypeScript integration code in the skill file to automate the pipeline.
Apify charges ~$0.05 per 1,000 pages. A typical research run costs $1-5 depending on volume. Compare that to SaaS alternatives at $10/mo — you save $114/yr saved.
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