RRepoGEO

REPOGEO REPORT · LITE

mistralai/mistral-common

Default branch main · commit 6d1fad68 · scanned 6/4/2026, 10:33:06 PM

GitHub: 900 stars · 147 forks

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 mistralai/mistral-common, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, mistral-ai, tokenization, pre-processing, nlp, python-library, inference
  • highreadme#2
    Add a concise tagline under the main README title

    Why:

    CURRENT
    # Mistral-common
    COPY-PASTE FIX
    # Mistral-common
The official inference library for pre-processing, tokenization, and validation of inputs and outputs for Mistral AI models.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://mistralai.github.io/mistral-common/

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 mistralai/mistral-common
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NLTK
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NLTK · recommended 2×
  2. spaCy · recommended 2×
  3. Hugging Face Tokenizers Library · recommended 1×
  4. SentencePiece · recommended 1×
  5. tiktoken · recommended 1×
  • CATEGORY QUERY
    How to tokenize text and tool calls for large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Tokenizers Library
    2. SentencePiece
    3. tiktoken
    4. NLTK
    5. spaCy

    AI recommended 5 alternatives but never named mistralai/mistral-common. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best Python libraries for pre-processing and validating LLM inputs?
    you: not recommended
    AI recommended (in order):
    1. Pydantic
    2. Pandas
    3. spaCy
    4. NLTK
    5. re module
    6. Cerberus

    AI recommended 6 alternatives but never named mistralai/mistral-common. 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 mistralai/mistral-common?
    pass
    AI named mistralai/mistral-common explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite