add-wikipedia-references

dsebastien's avatarfrom dsebastien

Add Wikipedia reference links to concepts that don't have one. Searches for relevant Wikipedia articles and adds them to the references array.

⭐1stars🔀0forks📁View on GitHub🕐Updated Jan 3, 2026⬇️1downloads
[Knowledge Base]

When & Why to Use This Skill

This Claude skill automates the enrichment of structured datasets and documentation by identifying concepts lacking citations and programmatically appending verified Wikipedia reference links. By leveraging advanced web search and browser automation, it streamlines the process of maintaining an authoritative, interconnected, and SEO-optimized knowledge base, ensuring all key terms are backed by credible external sources.

Use Cases

  • Automated Documentation Enrichment: Scan technical glossaries or concept files to automatically add Wikipedia links, providing users with instant access to authoritative background information.
  • Knowledge Base Maintenance: Periodically audit large datasets of terms to ensure all entries are updated with relevant external references, significantly improving data integrity and depth.
  • SEO and Authority Building: Enhance the search engine authority of content-heavy websites by ensuring every key concept is linked to high-trust domains like Wikipedia.
  • Data Validation and Fact-Checking: Use the skill to verify the existence and relevance of concepts against Wikipedia's database before finalizing entries in a production environment.
nameadd-wikipedia-references
descriptionAdd Wikipedia reference links to concepts that don't have one. Searches for relevant Wikipedia articles and adds them to the references array.
allowed-toolsBash, Read, Edit, Grep, Glob, WebSearch, WebFetch, mcp__playwright__browser_navigate, mcp__playwright__browser_snapshot, mcp__playwright__browser_close

Add Wikipedia References

Add Wikipedia links to concepts missing them.

Find Concepts Without Wikipedia

# List concepts without Wikipedia
grep -L '"url": "https://en.wikipedia.org' src/data/concepts/*.json | xargs -n1 basename | sed 's/.json$//'

# Count
grep -L '"url": "https://en.wikipedia.org' src/data/concepts/*.json | wc -l

# First 20 for batch processing
grep -L '"url": "https://en.wikipedia.org' src/data/concepts/*.json | head -20 | xargs -n1 basename | sed 's/.json$//'

Workflow Per Concept

  1. Read concept - get name, aliases
  2. Search Wikipedia - use concept name, then aliases if needed
  3. Verify relevance - article must match concept's meaning/domain
  4. Add reference:
{
  "references": [
    {
      "title": "Article Name - Wikipedia",
      "url": "https://en.wikipedia.org/wiki/Article_Name",
      "type": "website"
    }
  ]
}

Search Strategy

  1. Primary: exact concept name + "wikipedia"
  2. Fallback: aliases, broader terms, concept with context
  3. Handle disambiguation pages: choose most relevant article
  4. Use final URL after redirects

Skip When

  • No relevant Wikipedia article exists
  • Concept too niche (proprietary methods, very recent concepts)
  • Article doesn't match concept's domain (e.g., "Flow" in wrong field)

Reference Format

  • title: "Article Name - Wikipedia"
  • url: canonical URL with underscores (https://en.wikipedia.org/wiki/Article_Name)
  • type: "website"

Batch Processing

# Batch 1-20
grep -L '"url": "https://en.wikipedia.org' src/data/concepts/*.json | head -20

# Batch 21-40
grep -L '"url": "https://en.wikipedia.org' src/data/concepts/*.json | tail -n +21 | head -20

For large batches: spawn sub-agents (5-10 concepts each).

Verify

npm run build 2>&1 | tail -10