RRepoGEO

REPOGEO REPORT · LITE

starpig1129/DATAGEN

Default branch main · commit db0b0188 · scanned 5/24/2026, 6:58:26 AM

GitHub: 1,739 stars · 237 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface starpig1129/DATAGEN, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README introduction to explicitly counter miscategorization

    Why:

    CURRENT
    DATAGEN is a powerful brand name that represents our vision of leveraging artificial intelligence technology for data generation and analysis. The name combines "DATA" and "GEN"(generation), perfectly embodying the core functionality of this project - automated data analysis and research through a multi-agent system.
    COPY-PASTE FIX
    DATAGEN is an AI-driven multi-agent research assistant that automates hypothesis generation, data analysis, and report writing. **It is not a tool for synthetic data generation.** The name DATAGEN represents our vision of leveraging artificial intelligence technology for *insight generation from data* and analysis, perfectly embodying the core functionality of this project - automated data analysis and research through a multi-agent system.
  • mediumhomepage#2
    Add a homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    Add a valid URL (e.g., a GitHub Pages site, a dedicated project website, or a link to detailed documentation within the repo) to the 'Homepage' field in your repository settings.
  • mediumreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to your README, for example, '## Comparison with other Multi-Agent Systems' or '## Why DATAGEN?', that briefly explains how DATAGEN differentiates itself from or complements tools like LangChain, CrewAI, or AutoGPT, focusing on its specific niche of automated data analysis and research report generation.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface starpig1129/DATAGEN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. AutoGPT · recommended 1×
  3. CrewAI · recommended 1×
  4. AgentVerse · recommended 1×
  5. MetaGPT · recommended 1×
  • CATEGORY QUERY
    What multi-agent AI systems exist for automating data analysis and research report generation?
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. CrewAI
    3. AgentVerse
    4. MetaGPT
    5. LangChain
    6. Open Interpreter

    AI recommended 6 alternatives but never named starpig1129/DATAGEN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to automate hypothesis generation and data analysis using large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Anthropic's Claude
    5. Google's Gemini
    6. MindsDB
    7. Auto-GPT
    8. BabyAGI
    9. DeepMind's AlphaFold

    AI recommended 9 alternatives but never named starpig1129/DATAGEN. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of starpig1129/DATAGEN?
    pass
    AI named starpig1129/DATAGEN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts starpig1129/DATAGEN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named starpig1129/DATAGEN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo starpig1129/DATAGEN solve, and who is the primary audience?
    pass
    AI named starpig1129/DATAGEN explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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starpig1129/DATAGEN — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite