RRepoGEO

REPOGEO REPORT · LITE

honestsoul/generative_ai_project

Default branch main · commit 3710d294 · scanned 6/18/2026, 11:03:31 AM

GitHub: 1,035 stars · 458 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
23 /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
2 / 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 honestsoul/generative_ai_project, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    generative-ai, llm, project-template, application-development, python, prompt-engineering, ai-boilerplate, scalable-ai
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root containing the text of the MIT License.
  • mediumabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    A structured template for building robust generative AI applications
    COPY-PASTE FIX
    A structured, ready-to-use template for quickly building robust and scalable generative AI applications with best practices.

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 honestsoul/generative_ai_project
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Lightning-AI/lightning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Lightning-AI/lightning · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. huggingface/accelerate · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. keras-team/keras · recommended 1×
  • CATEGORY QUERY
    How to start a new generative AI project with a robust, scalable structure?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (Lightning-AI/lightning)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. Ray (ray-project/ray)
    7. MLflow (mlflow/mlflow)

    AI recommended 7 alternatives but never named honestsoul/generative_ai_project. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to manage LLM interactions, prompt templates, and caching for AI applications.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. OpenAI Python Library (openai/openai-python)
    5. Jinja2 (pallets/jinja)
    6. diskcache (grantjenks/python-diskcache)
    7. Redis (redis/redis)
    8. LiteLLM (BerriAI/litellm)
    9. PromptLayer (MagnivOrg/promptlayer)

    AI recommended 9 alternatives but never named honestsoul/generative_ai_project. 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 honestsoul/generative_ai_project?
    pass
    AI named honestsoul/generative_ai_project explicitly

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

  • If a team adopts honestsoul/generative_ai_project in production, what risks or prerequisites should they evaluate first?
    pass
    AI named honestsoul/generative_ai_project 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 honestsoul/generative_ai_project solve, and who is the primary audience?
    pass
    AI did not name honestsoul/generative_ai_project — 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?

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  • Brand-free category queries5 vs 2 in Lite
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