RRepoGEO

REPOGEO REPORT · LITE

mistralai/cookbook

Default branch main · commit 64f2e7a3 · scanned 5/21/2026, 5:52:59 PM

GitHub: 2,247 stars · 505 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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/cookbook, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A collection of practical code examples and runnable notebooks for building applications with Mistral AI models.
  • mediumreadme#2
    Strengthen the README's opening paragraph

    Why:

    CURRENT
    The Mistral Cookbook features examples contributed by Mistralers and our community, as well as our partners. If you have cool examples showcasing Mistral models, feel free to share them by submitting a PR to this repo.
    COPY-PASTE FIX
    The Mistral Cookbook is a comprehensive collection of practical code examples and runnable notebooks for building applications with Mistral AI models. Contributed by Mistralers, our community, and partners, it provides developers and engineers with hands-on guides and integration patterns. We welcome contributions showcasing innovative uses of Mistral models; please refer to our submission guidelines.

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/cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. langchain-ai/langchain · recommended 2×
  3. openai/openai-python · recommended 1×
  4. google/generative-ai-python · recommended 1×
  5. huggingface/diffusers · recommended 1×
  • CATEGORY QUERY
    Seeking practical code examples for building applications with advanced generative AI models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenAI API (openai/openai-python)
    3. LangChain (langchain-ai/langchain)
    4. Google Generative AI (google/generative-ai-python)
    5. diffusers (huggingface/diffusers)

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

    Show full AI answer
  • CATEGORY QUERY
    Where can I find runnable notebooks demonstrating modern language model integration patterns?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. LangChain (langchain-ai/langchain)
    3. OpenAI Cookbook (openai/openai-cookbook)
    4. Vertex AI Workbench
    5. PyTorch Lightning Bolts (Lightning-AI/lightning-bolts)
    6. Kaggle Notebooks

    AI recommended 6 alternatives but never named mistralai/cookbook. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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/cookbook?
    pass
    AI named mistralai/cookbook 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/cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mistralai/cookbook 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/cookbook solve, and who is the primary audience?
    pass
    AI named mistralai/cookbook explicitly

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

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mistralai/cookbook — 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