RRepoGEO

REPOGEO REPORT · LITE

ElectricAlexis/NotaGen

Default branch main · commit 4553cd74 · scanned 5/25/2026, 5:28:39 AM

GitHub: 1,198 stars · 128 forks

AI VISIBILITY SCORE
28 /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
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 ElectricAlexis/NotaGen, 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
    ['music-generation', 'symbolic-music', 'large-language-models', 'llm', 'deep-learning', 'pytorch', 'classical-music', 'ai-music']
  • highreadme#2
    Add a concise mission statement immediately after the title

    Why:

    CURRENT
    The current README has badges and links immediately after the H1.
    COPY-PASTE FIX
    Add this sentence right after the H1 and before any badges/links: 'This repository presents NotaGen, a cutting-edge research project focused on generating high-quality classical sheet music using advanced Large Language Model training paradigms.'
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison to Alternatives' in the README, briefly explaining how NotaGen's three-stage training paradigm (pre-training, fine-tuning, CLaMP-DPO reinforcement learning) differentiates it from other symbolic music generation models.

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 ElectricAlexis/NotaGen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MuseNet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MuseNet · recommended 2×
  2. OpenAI Jukebox · recommended 1×
  3. Magenta Studio · recommended 1×
  4. Amper Music · recommended 1×
  5. AIVA (Artificial Intelligence Virtual Artist) · recommended 1×
  • CATEGORY QUERY
    How can I use AI to generate high-quality classical music scores automatically?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Jukebox
    2. Magenta Studio
    3. Amper Music
    4. AIVA (Artificial Intelligence Virtual Artist)
    5. MuseNet
    6. Flow Machines
    7. Antescofo

    AI recommended 7 alternatives but never named ElectricAlexis/NotaGen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best large language model approaches for symbolic music generation?
    you: not recommended
    AI recommended (in order):
    1. Music Transformer
    2. MuseNet
    3. Jukebox
    4. MusicLM
    5. Pop2Piano
    6. GPT-2/3
    7. MusicVAE
    8. DiffWave
    9. AudioGen

    AI recommended 9 alternatives but never named ElectricAlexis/NotaGen. 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 ElectricAlexis/NotaGen?
    pass
    AI did not name ElectricAlexis/NotaGen — 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?

  • If a team adopts ElectricAlexis/NotaGen in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ElectricAlexis/NotaGen 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 ElectricAlexis/NotaGen solve, and who is the primary audience?
    pass
    AI named ElectricAlexis/NotaGen 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 ElectricAlexis/NotaGen. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/ElectricAlexis/NotaGen.svg)](https://repogeo.com/en/r/ElectricAlexis/NotaGen)
HTML
<a href="https://repogeo.com/en/r/ElectricAlexis/NotaGen"><img src="https://repogeo.com/badge/ElectricAlexis/NotaGen.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ElectricAlexis/NotaGen — 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