Back to list
lukeslp

data-fetch

by lukeslp

Claude Code skills plugin for dr.eamer.dev workflows - session management, quality audits, UX design, and data visualization

1🍴 0📅 Jan 15, 2026

SKILL.md


name: data-fetch description: Fetch and aggregate data from 17 external APIs including Census, arXiv, NASA, Wikipedia, PubMed, and GitHub. version: 1.0.0

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

SourceClientBest For
Academic
arXivarxivResearch papers, preprints
Semantic Scholarsemantic_scholarAcademic citations, papers
PubMedpubmedMedical/biomedical research
Government
Census BureaucensusDemographics, economic data
FECfecCampaign finance
JudiciaryjudiciaryCourt records, cases
Web/News
WikipediawikipediaGeneral knowledge
News APIsnewsCurrent events
Archive.orgarchiveHistorical web content
Tech
GitHubgithubRepositories, code
YouTubeyoutubeVideo content, transcripts
Scientific
NASAnasaSpace, astronomy data
Wolfram AlphawolframComputational answers
Other
FinancefinanceStock data, markets
WeatherweatherWeather forecasts
OpenLibraryopenlibraryBooks, authors
MyAnimeListmyanimelistAnime/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 TypeRecommended Sources
Academic researcharxiv, semantic_scholar, pubmed
Current eventsnews, wikipedia
Technical/codegithub, stackoverflow
Demographicscensus
Historicalarchive, wikipedia
Scientific factsnasa, wolfram
Books/literatureopenlibrary

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

Score

Total Score

70/100

Based on repository quality metrics

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

+10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

0/5

Reviews

💬

Reviews coming soon