add-wikipedia-references
Add Wikipedia reference links to concepts that don't have one. Searches for relevant Wikipedia articles and adds them to the references array.
Best use case
add-wikipedia-references is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Add Wikipedia reference links to concepts that don't have one. Searches for relevant Wikipedia articles and adds them to the references array.
Teams using add-wikipedia-references should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/add-wikipedia-references/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How add-wikipedia-references Compares
| Feature / Agent | add-wikipedia-references | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Add Wikipedia reference links to concepts that don't have one. Searches for relevant Wikipedia articles and adds them to the references array.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Add Wikipedia References
Add Wikipedia links to concepts missing them.
## Find Concepts Without Wikipedia
```bash
# 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**:
```json
{
"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
```bash
# 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
```bash
npm run build 2>&1 | tail -10
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