Data Fetch

You are fetching and aggregating data from multiple external sources using the shared library's DataFetchingFactory. This skill provides access to 17 structured API clients.

Available Data Sources

Source	Client	Best For
Academic
arXiv	arxiv	Research papers, preprints
Semantic Scholar	semantic_scholar	Academic citations, papers
PubMed	pubmed	Medical/biomedical research
Government
Census Bureau	census	Demographics, economic data
FEC	fec	Campaign finance
Judiciary	judiciary	Court records, cases
Web/News
Wikipedia	wikipedia	General knowledge
News APIs	news	Current events
Archive.org	archive	Historical web content
Tech
GitHub	github	Repositories, code
YouTube	youtube	Video content, transcripts
Scientific
NASA	nasa	Space, astronomy data
Wolfram Alpha	wolfram	Computational answers
Other
Finance	finance	Stock data, markets
Weather	weather	Weather forecasts
OpenLibrary	openlibrary	Books, authors
MyAnimeList	myanimelist	Anime/manga data

Execution Strategy

Single Source Query

from data_fetching import DataFetchingFactory
factory = DataFetchingFactory()
client = factory.create_client('arxiv')
results = await client.search("quantum computing", max_results=10)

Multi-Source Aggregation (PARALLEL)

import asyncio
sources = ['arxiv', 'wikipedia', 'news']
tasks = [factory.create_client(s).search(query) for s in sources]
results = await asyncio.gather(*tasks)

Source Selection Guide

Query Type	Recommended Sources
Academic research	arxiv, semantic_scholar, pubmed
Current events	news, wikipedia
Technical/code	github, stackoverflow
Demographics	census
Historical	archive, wikipedia
Scientific facts	nasa, wolfram
Books/literature	openlibrary

Output Format

📊 DATA FETCH RESULTS

Query: {query}
Sources: {sources_used}
Date: {timestamp}

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
          SOURCE: {source_name}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Results: {count}

1. {title}
   - {metadata}
   - URL: {url}

2. {title}
   ...

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
           AGGREGATED INSIGHTS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Cross-Source Themes:
1. {theme} - Found in: {sources}
2. {theme} - Found in: {sources}

Conflicts/Discrepancies:
- {source1} says X, {source2} says Y

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
              CITATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[1] {citation}
[2] {citation}

Integration with Orchestrators

For complex research, combine with orchestration:

/data-fetch → provides raw data
    ↓
DreamCascade → synthesizes findings
    ↓
/data-artist → visualizes results

Key Principles

1. Parallel fetching - Query multiple sources simultaneously
2. Source attribution - Always cite data origins
3. Deduplication - Merge overlapping results
4. Rate limiting - Respect API limits per client
5. Caching - Use MCP cache for repeated queries

Common Workflows

# Census demographics
/data-fetch census "housing prices by county"

# Academic research
/data-fetch arxiv,pubmed "CRISPR gene editing"

# Tech exploration
/data-fetch github "machine learning frameworks" --stars >1000

# Current events
/data-fetch news,wikipedia "climate summit 2026"

Related Skills

• /data-artist - Visualize fetched data beautifully
• /quality-audit - Verify data quality and validate findings