RRepoGEO

REPOGEO REPORT · LITE

SeldonIO/alibi

Default branch master · commit 99c3421d · scanned 7/1/2026, 8:31:38 PM

GitHub: 2,634 stars · 264 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
77 /100
Needs work
Category recall
2 / 2
Avg rank #6.0 when recommended
Rule findings
2 pass · 0 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 SeldonIO/alibi, 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 Alibi's scope in the README to distinguish from alibi-detect

    Why:

    CURRENT
    Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project alibi-detect.
    COPY-PASTE FIX
    Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. **Please note that Alibi focuses exclusively on explanation methods; for outlier detection, concept drift, or adversarial instance detection, refer to our sister project, alibi-detect.**
  • mediumreadme#2
    Add an explicit license statement to the README

    Why:

    COPY-PASTE FIX
    Alibi is released under the terms specified in the [LICENSE file](https://github.com/SeldonIO/alibi/blob/master/LICENSE).
  • lowreadme#3
    Strengthen the README's introductory sentence for better positioning

    Why:

    CURRENT
    Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
    COPY-PASTE FIX
    Alibi is a comprehensive, source-available Python library providing high-quality implementations of leading machine learning model inspection and interpretation methods. It focuses on robust black-box, white-box, local, and global explanation techniques for classification and regression models.

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
2 / 2
100% of queries surface SeldonIO/alibi
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
15%
Of all named tools, what % are you?
Top rival
shap/shap
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. shap/shap · recommended 2×
  2. marcotcr/lime · recommended 2×
  3. microsoft/interpret · recommended 2×
  4. eli5/eli5 · recommended 1×
  5. tensorflow/tensorboard · recommended 1×
  • CATEGORY QUERY
    How can I interpret and inspect the decisions of my black-box machine learning models?
    you: #6
    AI recommended (in order):
    1. SHAP (shap/shap)
    2. LIME (marcotcr/lime)
    3. ELI5 (eli5/eli5)
    4. InterpretML (microsoft/interpret)
    5. What-If Tool (tensorflow/tensorboard)
    6. Alibi Explain (SeldonIO/alibi) ← you
    7. Skater (oracle/skater)
    Show full AI answer
  • CATEGORY QUERY
    What are the leading Python frameworks for eXplainable AI (XAI) model interpretability?
    you: #6
    AI recommended (in order):
    1. SHAP (shap/shap)
    2. LIME (marcotcr/lime)
    3. ELI5 (eli5-team/eli5)
    4. InterpretML (microsoft/interpret)
    5. Captum (pytorch/captum)
    6. Alibi Explain (SeldonIO/alibi) ← you
    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 SeldonIO/alibi?
    pass
    AI named SeldonIO/alibi explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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SeldonIO/alibi — RepoGEO report