RRepoGEO

REPOGEO REPORT · LITE

kunchenguid/no-mistakes

Default branch main · commit 7b46d2bf · scanned 6/6/2026, 11:46:55 PM

GitHub: 729 stars · 37 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 kunchenguid/no-mistakes, 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
  • highreadme#1
    Reposition the README's main headings to clearly state the core function

    Why:

    CURRENT
    <h1 align="center"><code>git push no-mistakes</code></h1>
<h3 align="center">Kill all the slop. Raise clean PR.</h3>
    COPY-PASTE FIX
    <h1 align="center">no-mistakes: AI-driven Git Proxy for Automated, Clean Pull Requests</h1>
<h3 align="center">Automate code validation, fix errors, and open perfect PRs before they hit upstream.</h3>
  • mediumabout#2
    Improve the repository description for better clarity

    Why:

    CURRENT
    git push no-mistakes
    COPY-PASTE FIX
    An AI-driven git proxy that validates code, auto-fixes errors, and opens clean pull requests before pushing to your remote.

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 kunchenguid/no-mistakes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SonarQube
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SonarQube · recommended 2×
  2. Husky · recommended 1×
  3. pre-commit · recommended 1×
  4. ESLint · recommended 1×
  5. Pylint · recommended 1×
  • CATEGORY QUERY
    How to ensure code quality and prevent errors before pushing changes to a remote repository?
    you: not recommended
    AI recommended (in order):
    1. Husky
    2. pre-commit
    3. ESLint
    4. Pylint
    5. Flake8
    6. Black
    7. RuboCop
    8. Stylelint
    9. Prettier
    10. GoFmt
    11. Jest
    12. Pytest
    13. JUnit
    14. NUnit
    15. Snyk
    16. SonarQube
    17. Bandit

    AI recommended 17 alternatives but never named kunchenguid/no-mistakes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for AI-powered code validation and automatically generating clean pull requests from local changes?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. GitHub Actions
    3. DeepSource
    4. SonarQube
    5. SonarCloud
    6. Codiga
    7. CodeClimate
    8. Snyk Code
    9. GitLab Auto DevOps

    AI recommended 9 alternatives but never named kunchenguid/no-mistakes. 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 kunchenguid/no-mistakes?
    pass
    AI named kunchenguid/no-mistakes explicitly

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

  • If a team adopts kunchenguid/no-mistakes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kunchenguid/no-mistakes 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 kunchenguid/no-mistakes solve, and who is the primary audience?
    pass
    AI named kunchenguid/no-mistakes explicitly

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

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kunchenguid/no-mistakes — RepoGEO report