RRepoGEO

REPOGEO REPORT · LITE

valeman/awesome-conformal-prediction

Default branch main · commit 64acf474 · scanned 5/10/2026, 4:47:19 PM

GitHub: 1,219 stars · 112 forks

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 valeman/awesome-conformal-prediction, 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
  • highhomepage#1
    Add the repository URL as the project homepage

    Why:

    COPY-PASTE FIX
    https://github.com/valeman/awesome-conformal-prediction
  • mediumreadme#2
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, e.g., "This project is licensed under [License Name 1] and [License Name 2]. See the LICENSE file for details."
  • lowreadme#3
    Emphasize 'awesome list' nature in the README's opening sentence

    Why:

    CURRENT
    ⭐ **The definitive resource for conformal prediction: methods, libraries, tutorials, benchmarks, and production guides.**
    COPY-PASTE FIX
    ⭐ **A professionally curated, definitive awesome list of resources for conformal prediction: methods, libraries, tutorials, benchmarks, and production guides.**

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 valeman/awesome-conformal-prediction
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 4×
  2. TensorFlow · recommended 4×
  3. Pyro · recommended 2×
  4. Scikit-learn · recommended 2×
  5. Keras · recommended 2×
  • CATEGORY QUERY
    How to quantify uncertainty in machine learning model predictions reliably and rigorously?
    you: not recommended
    AI recommended (in order):
    1. Pyro
    2. TensorFlow Probability (TFP)
    3. Edward2
    4. Scikit-learn
    5. PyTorch
    6. TensorFlow
    7. Keras
    8. TensorFlow
    9. PyTorch
    10. MAPIE (Model Agnostic Prediction Interval Estimator)
    11. Conformal-Pytorch
    12. Scikit-learn
    13. Statsmodels
    14. Keras
    15. TensorFlow
    16. PyTorch
    17. Uncertainty-Toolbox

    AI recommended 17 alternatives but never named valeman/awesome-conformal-prediction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources and libraries for building robust prediction intervals?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. quantregforest
    3. statsmodels
    4. LightGBM
    5. XGBoost
    6. PyTorch
    7. TensorFlow
    8. Pyro
    9. TensorFlow Probability
    10. nonconformist
    11. mapie

    AI recommended 11 alternatives but never named valeman/awesome-conformal-prediction. 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 valeman/awesome-conformal-prediction?
    pass
    AI did not name valeman/awesome-conformal-prediction — 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 valeman/awesome-conformal-prediction in production, what risks or prerequisites should they evaluate first?
    pass
    AI named valeman/awesome-conformal-prediction 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 valeman/awesome-conformal-prediction solve, and who is the primary audience?
    pass
    AI did not name valeman/awesome-conformal-prediction — 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 valeman/awesome-conformal-prediction. 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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valeman/awesome-conformal-prediction — 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