RRepoGEO

REPOGEO REPORT · LITE

daveebbelaar/ai-cookbook

Default branch main · commit 60e29729 · scanned 5/22/2026, 5:47:56 AM

GitHub: 4,118 stars · 1,454 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 daveebbelaar/ai-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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README introduction to clarify "cookbook" nature and LLM focus

    Why:

    CURRENT
    This Cookbook contains examples and tutorials to help developers build AI systems with copy/paste code snippets that you can easily integrate into your own projects.
    COPY-PASTE FIX
    This AI Cookbook provides practical, copy-paste code examples and tutorials specifically designed to help developers build real-world AI systems, especially those leveraging large language models (LLMs) and agents. Unlike frameworks, this is a collection of ready-to-integrate recipes.
  • mediumtopics#2
    Add topics emphasizing "examples" and "tutorials"

    Why:

    CURRENT
    agents, ai, anthropic, llm, openai, python
    COPY-PASTE FIX
    agents, ai, anthropic, llm, openai, python, examples, tutorials, code-snippets, generative-ai-recipes
  • lowreadme#3
    Add a "What you'll find here" section to detail cookbook contents

    Why:

    COPY-PASTE FIX
    ## What you'll find here
This cookbook features practical examples for building AI systems, including:
- Integrating large language model (LLM) APIs (e.g., OpenAI, Anthropic)
- Developing autonomous agents with tools
- Crafting effective prompts and few-shot examples
- Deploying simple GenAI solutions
Each recipe is designed for easy copy-paste integration into your Python projects.

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 daveebbelaar/ai-cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 1×
  2. PyTorch · recommended 1×
  3. Kaggle Learn · recommended 1×
  4. scikit-learn · recommended 1×
  5. fast.ai · recommended 1×
  • CATEGORY QUERY
    Where can I find practical code examples and tutorials for building AI systems?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. Kaggle Learn
    4. scikit-learn
    5. fast.ai
    6. Hugging Face Transformers
    7. Google AI Blog

    AI recommended 7 alternatives but never named daveebbelaar/ai-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate large language model agents into my Python projects effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. CrewAI
    4. AutoGen
    5. Haystack
    6. Guidance
    7. Transformers Agents

    AI recommended 7 alternatives but never named daveebbelaar/ai-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
    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 daveebbelaar/ai-cookbook?
    pass
    AI named daveebbelaar/ai-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 daveebbelaar/ai-cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named daveebbelaar/ai-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 daveebbelaar/ai-cookbook solve, and who is the primary audience?
    pass
    AI named daveebbelaar/ai-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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daveebbelaar/ai-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