RRepoGEO

REPOGEO REPORT · LITE

codenoid/github-roast

Default branch main · commit d59d9653 · scanned 6/14/2026, 4:33:24 AM

GitHub: 767 stars · 43 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 codenoid/github-roast, 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 to clearly state it's an AI application

    Why:

    CURRENT
    # GitHub Roast

SvelteKit, Cloudflare Pages, Cloudflare D1, OpenAI gpt-4o-mini
    COPY-PASTE FIX
    # GitHub Roast

A fun web application that generates humorous, satirical "roasts" of GitHub profiles using AI. Built with SvelteKit, Cloudflare Pages, Cloudflare D1, and OpenAI gpt-4o-mini.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai, sveltekit, cloudflare, openai, serverless, web-application, humor, roast, github-profile
  • mediumlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., choose between MIT, Apache-2.0, or GPL-3.0) in the root of the repository, containing the full license text for the chosen license.

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 codenoid/github-roast
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 4×
  2. huggingface/transformers · recommended 3×
  3. Vercel · recommended 2×
  4. streamlit/streamlit · recommended 1×
  5. Streamlit Community Cloud · recommended 1×
  • CATEGORY QUERY
    How can I quickly build a web application that generates funny AI responses?
    you: not recommended
    AI recommended (in order):
    1. Streamlit (streamlit/streamlit)
    2. OpenAI API
    3. Hugging Face Transformers (huggingface/transformers)
    4. Streamlit Community Cloud
    5. Gradio (gradio-app/gradio)
    6. Hugging Face Transformers (huggingface/transformers)
    7. OpenAI API
    8. Hugging Face Spaces
    9. Next.js (vercel/next.js)
    10. OpenAI API
    11. Vercel AI SDK (vercel/ai)
    12. Vercel
    13. Flask (pallets/flask)
    14. OpenAI API
    15. Hugging Face Transformers (huggingface/transformers)
    16. Heroku
    17. Render
    18. Bubble
    19. OpenAI Plugin

    AI recommended 19 alternatives but never named codenoid/github-roast. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What serverless web application frameworks are good for integrating large language models?
    you: not recommended
    AI recommended (in order):
    1. Next.js
    2. Vercel
    3. AWS Amplify
    4. Netlify
    5. Nuxt.js
    6. AWS Lambda
    7. API Gateway
    8. Amazon Bedrock
    9. Serverless Framework
    10. Azure Functions
    11. Google Cloud Functions
    12. Gatsby
    13. Netlify Functions
    14. SvelteKit
    15. Cloudflare Workers

    AI recommended 15 alternatives but never named codenoid/github-roast. 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 codenoid/github-roast?
    pass
    AI named codenoid/github-roast explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of codenoid/github-roast. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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