RRepoGEO

REPOGEO REPORT · LITE

gmpetrov/databerry

Default branch main · commit 8e3667b0 · scanned 5/23/2026, 2:22:42 PM

GitHub: 2,941 stars · 425 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
0 pass · 2 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 gmpetrov/databerry, 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

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

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) to the repository root.
  • mediumreadme#2
    List key features and benefits in the README

    Why:

    COPY-PASTE FIX
    Add a "Features" section to the README, listing capabilities like "no-code interface," "custom data integration (RAG)," "LLM agent building," and "simplified deployment."

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 gmpetrov/databerry
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ChatGPT Plus
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ChatGPT Plus · recommended 1×
  2. Voiceflow · recommended 1×
  3. botpress/botpress · recommended 1×
  4. Google Dialogflow CX · recommended 1×
  5. Microsoft Azure Bot Service · recommended 1×
  • CATEGORY QUERY
    How can I build custom AI chatbot agents without writing much code?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT Plus
    2. Voiceflow
    3. Botpress (botpress/botpress)
    4. Google Dialogflow CX
    5. Microsoft Azure Bot Service
    6. Power Virtual Agents
    7. ManyChat

    AI recommended 7 alternatives but never named gmpetrov/databerry. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the easiest ways to develop and deploy intelligent language model applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Inference Endpoints
    3. Hugging Face Spaces
    4. OpenAI API
    5. Google Cloud Vertex AI
    6. Generative AI Studio
    7. LangChain (langchain-ai/langchain)
    8. AWS Lambda
    9. AWS ECS
    10. Google Cloud Run
    11. Azure Container Apps
    12. Gradio (gradio-app/gradio)
    13. Streamlit (streamlit/streamlit)
    14. Replicate

    AI recommended 14 alternatives but never named gmpetrov/databerry. 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
    warn

    Suggestion:

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 gmpetrov/databerry?
    pass
    AI named gmpetrov/databerry explicitly

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

  • If a team adopts gmpetrov/databerry in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gmpetrov/databerry 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 gmpetrov/databerry solve, and who is the primary audience?
    pass
    AI named gmpetrov/databerry explicitly

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

Embed your GEO score

Drop this badge into the README of gmpetrov/databerry. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/gmpetrov/databerry.svg)](https://repogeo.com/en/r/gmpetrov/databerry)
HTML
<a href="https://repogeo.com/en/r/gmpetrov/databerry"><img src="https://repogeo.com/badge/gmpetrov/databerry.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

gmpetrov/databerry — 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