REPOGEO REPORT · LITE

deepklarity/jupyter-text2code

Default branch master · commit 5a58b58f · scanned 5/21/2026, 12:08:13 AM

GitHub: 2,088 stars · 359 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

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 deepklarity/jupyter-text2code, 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

highabout#1

Refine the 'About' description for clarity and audience

Why:

CURRENT

A proof-of-concept jupyter extension which converts english queries into relevant python code

COPY-PASTE FIX

A Jupyter extension for data scientists and analysts that generates Python code from natural language queries, accelerating data analysis workflows directly within notebooks.

hightopics#2

Add more specific topics to improve categorization

Why:

CURRENT

jupyter-notebook, machine-learning, python

COPY-PASTE FIX

jupyter-notebook, machine-learning, python, code-generation, natural-language-processing, jupyter-extension, data-analysis, text-to-code

mediumreadme#3

Add a 'Key Features' section to the README

Why:

COPY-PASTE FIX

## Key Features

- **Jupyter-Native Code Generation:** Seamlessly generate Python code from natural language descriptions directly within your Jupyter notebooks.
- **Data Analysis Focus:** Specifically designed to assist data scientists and analysts in accelerating their data exploration and manipulation tasks.
- **Intuitive Workflow:** Convert English queries into executable Python code without leaving your notebook environment.

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 deepklarity/jupyter-text2code

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

GitHub Copilot

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

GitHub Copilot · recommended 2×
Tabnine · recommended 2×
jupyterlab/jupyter-ai · recommended 1×
OpenAI's GPT-4 Code Interpreter · recommended 1×
Google's Gemini Advanced · recommended 1×

CATEGORY QUERY
How can I generate Python code from natural language descriptions directly within Jupyter notebooks?
you: not recommended
AI recommended (in order):
1. Jupyter AI (jupyterlab/jupyter-ai)
2. OpenAI's GPT-4 Code Interpreter
3. Google's Gemini Advanced
4. GitHub Copilot
5. Tabnine
6. Google Colaboratory
7. Llama 3
8. Code Llama
9. ollama (ollama/ollama)
10. LM Studio
11. Hugging Face Transformers library (huggingface/transformers)
AI recommended 11 alternatives but never named deepklarity/jupyter-text2code. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Are there any Jupyter extensions to assist with data analysis by converting text to code?
you: not recommended
AI recommended (in order):
1. Jupyter AI
2. nbdev
3. Jupyternaut
4. OpenAI API
5. Google Gemini API
6. Hugging Face Transformers
7. Tabnine
8. GitHub Copilot
9. DataPrep.EDA
AI recommended 9 alternatives but never named deepklarity/jupyter-text2code. 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 deepklarity/jupyter-text2code?
pass
AI named deepklarity/jupyter-text2code explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts deepklarity/jupyter-text2code in production, what risks or prerequisites should they evaluate first?
pass
AI named deepklarity/jupyter-text2code 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 deepklarity/jupyter-text2code solve, and who is the primary audience?
pass
AI named deepklarity/jupyter-text2code 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 deepklarity/jupyter-text2code. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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<a href="https://repogeo.com/en/r/deepklarity/jupyter-text2code"><img src="https://repogeo.com/badge/deepklarity/jupyter-text2code.svg" alt="RepoGEO" /></a>

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deepklarity/jupyter-text2code — 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