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REPOGEO REPORT · LITE

SkalskiP/awesome-chatgpt-code-interpreter-experiments

Default branch master · commit 84b9adef · scanned 6/27/2026, 6:08:24 PM

GitHub: 1,015 stars · 58 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
22 /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
1 / 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 SkalskiP/awesome-chatgpt-code-interpreter-experiments, 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
    Clarify the README's opening sentence to state it's an 'awesome list' of experiments

    Why:

    CURRENT
    We aim to push ChatGPT + Code Interpreter to its limits, show you what's possible and unlock your creativity! Well, and have a lot of fun doing it! 🔥
    COPY-PASTE FIX
    This is an awesome collection of experiments and resources designed to push ChatGPT + Code Interpreter to its limits, show you what's possible, and unlock your creativity! Well, and have a lot of fun doing it! 🔥
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and add its text to this file.
  • mediumtopics#3
    Add more specific topics to reinforce the 'awesome list' and 'experiments' nature

    Why:

    CURRENT
    agent, chatbot, code-interpreter, computer-vision, jailbreak, language
    COPY-PASTE FIX
    agent, awesome-list, chatbot, code-interpreter, computer-vision, experiments, generative-ai, jailbreak, language, prompts

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 SkalskiP/awesome-chatgpt-code-interpreter-experiments
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
jupyter/notebook
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. jupyter/notebook · recommended 1×
  2. Google Colab · recommended 1×
  3. microsoft/vscode · recommended 1×
  4. openai/gym · recommended 1×
  5. DLR-RM/stable-baselines3 · recommended 1×
  • CATEGORY QUERY
    What advanced experiments can I perform with an AI assistant and a code execution environment?
    you: not recommended
    AI recommended (in order):
    1. Jupyter Notebook/Lab (jupyter/notebook)
    2. Google Colab
    3. VS Code (microsoft/vscode)
    4. OpenAI Gym (openai/gym)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)
    8. Python
    9. C++
    10. Numba (numba/numba)
    11. Cython (cython/cython)
    12. requests (psf/requests)
    13. BeautifulSoup (waylan/beautifulsoup4)
    14. Scrapy (scrapy/scrapy)
    15. pandas (pandas-dev/pandas)
    16. Hugging Face Transformers (huggingface/transformers)
    17. Kaggle Notebooks
    18. pytest (pytest-dev/pytest)
    19. unittest
    20. Black (psf/black)
    21. Pylint (pylint-dev/pylint)
    22. Flake8 (PyCQA/flake8)
    23. Plotly (plotly/plotly.py)
    24. Altair (altair-viz/altair)
    25. Matplotlib (matplotlib/matplotlib)
    26. Seaborn (mwaskom/seaborn)

    AI recommended 26 alternatives but never named SkalskiP/awesome-chatgpt-code-interpreter-experiments. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to leverage a conversational AI with a sandboxed Python interpreter for data tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API (GPT-4 Code Interpreter/Advanced Data Analysis)
    2. Piston API
    3. Anthropic Claude (Opus/Sonnet)
    4. Google Cloud Sandbox API
    5. Mistral AI (Mistral Large/Mixtral)
    6. Jupyter Kernel Gateway
    7. Google Gemini (Advanced)
    8. Replit API
    9. Llama 3
    10. Ollama
    11. vLLM
    12. Docker
    13. Hugging Face Inference API
    14. CodeSandbox API

    AI recommended 14 alternatives but never named SkalskiP/awesome-chatgpt-code-interpreter-experiments. 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 SkalskiP/awesome-chatgpt-code-interpreter-experiments?
    pass
    AI did not name SkalskiP/awesome-chatgpt-code-interpreter-experiments — likely talking about a different project

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

  • If a team adopts SkalskiP/awesome-chatgpt-code-interpreter-experiments in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkalskiP/awesome-chatgpt-code-interpreter-experiments 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 SkalskiP/awesome-chatgpt-code-interpreter-experiments solve, and who is the primary audience?
    pass
    AI did not name SkalskiP/awesome-chatgpt-code-interpreter-experiments — likely talking about a different project

    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 SkalskiP/awesome-chatgpt-code-interpreter-experiments. 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite