RRepoGEO

REPOGEO REPORT · LITE

alinaqi/maggy

Default branch main · commit 710c27b5 · scanned 6/8/2026, 5:17:29 AM

GitHub: 691 stars · 57 forks

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 alinaqi/maggy, 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 the README H1 and tagline to clarify its role as an AI engineering command center

    Why:

    CURRENT
    # Claude Bootstrap + Maggy
> **Turn Claude Code into a self-reviewing, test-enforced engineering system that remembers context across sessions — then route work across 13 models from a single dashboard.**
    COPY-PASTE FIX
    # Maggy: Autonomous AI Engineering Command Center
> **Maggy transforms your AI coding assistants (Claude Code, Codex, Gemini, DeepSeek, etc.) into a self-reviewing, test-enforced engineering system. It's a self-hosted command center for multi-model orchestration, providing persistent context, quality gates, and a unified dashboard to route work across 13+ AI models.**
  • hightopics#2
    Add more specific topics to emphasize multi-model AI engineering and orchestration

    Why:

    CURRENT
    ai-coding, claude, claude-code, developer-tools, project-initialization, python, react, security, typescript
    COPY-PASTE FIX
    ai-engineering, multi-model-ai, ai-coding-assistant-management, developer-tools, claude, codex, gemini, deepseek, ai-workflow-orchestration, python, react, typescript
  • mediumreadme#3
    Add a comparison section to differentiate Maggy from enterprise AI coding tools

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Maggy vs. Enterprise AI Coding Tools' or 'Why Maggy?' after 'Who This Is For', with content like: 'Unlike SaaS platforms such as GitHub Copilot Enterprise or GitLab Duo Enterprise, Maggy provides a self-hosted, open-source AI engineering command center. It focuses on giving you full control over your AI development workflow, enabling multi-model orchestration and custom engineering best practices without vendor lock-in, rather than being a single-model assistant or a cloud-managed service.'

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 alinaqi/maggy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot Enterprise
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot Enterprise · recommended 2×
  2. SonarQube · recommended 2×
  3. GitLab Duo Enterprise · recommended 1×
  4. Azure AI Studio · recommended 1×
  5. Google Cloud Vertex AI · recommended 1×
  • CATEGORY QUERY
    What tools help manage multiple AI coding assistants and enforce engineering best practices?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. GitLab Duo Enterprise
    3. Azure AI Studio
    4. Google Cloud Vertex AI
    5. Hugging Face Hub
    6. SonarQube
    7. SonarCloud
    8. Review Board
    9. Gerrit

    AI recommended 9 alternatives but never named alinaqi/maggy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI development environment that remembers context and ensures code quality with automated reviews.
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. GitHub Advanced Security
    3. CodeQL
    4. JetBrains AI Assistant
    5. IntelliJ IDEA Ultimate
    6. PyCharm Professional
    7. SonarQube
    8. Visual Studio Code
    9. GitHub Copilot Chat
    10. SonarLint
    11. CodeGPT
    12. Google Cloud Vertex AI Workbench
    13. Code Llama
    14. CodeGemma
    15. Google Cloud Build
    16. Cloud Code
    17. AWS CodeWhisperer
    18. AWS CodeGuru
    19. CodiumAI
    20. ESLint
    21. Black
    22. Flake8
    23. Semgrep
    24. Bandit

    AI recommended 24 alternatives but never named alinaqi/maggy. 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 alinaqi/maggy?
    pass
    AI named alinaqi/maggy explicitly

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

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

    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
  • Prioritized action items8 vs 3 in Lite