RRepoGEO

REPOGEO REPORT · LITE

Vyntral/god-eye

Default branch main · commit 14ab952c · scanned 6/28/2026, 4:26:42 PM

GitHub: 510 stars · 69 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 Vyntral/god-eye, 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 H3 to emphasize core function

    Why:

    CURRENT
    <h3 align="center">AI-powered attack-surface discovery & offensive security<br>in a single Go binary. Terminal-only. Zero cloud.</h3>
    COPY-PASTE FIX
    <h3 align="center">AI-powered subdomain enumeration & attack-surface discovery<br>with local LLM analysis via Ollama. Terminal-only. Zero cloud.</h3>
  • mediumtopics#2
    Add specific AI technology topics

    Why:

    CURRENT
    ai-security, bug-bounty, cve, cybersecurity, ethical-hacking, golang, hacking, infosec, osint, pentesting, recon, reconnaissance, security, subdomain-enumeration, vulnerability-scanner
    COPY-PASTE FIX
    ai-security, bug-bounty, cve, cybersecurity, ethical-hacking, golang, hacking, infosec, local-llm, ollama, osint, pentesting, recon, reconnaissance, security, subdomain-enumeration, vulnerability-scanner
  • lowlicense#3
    Clarify existing license in README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., "## License\nThis project is licensed under the terms found in the [LICENSE](LICENSE) file."

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 Vyntral/god-eye
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. scikit-learn/scikit-learn · recommended 1×
  4. nmap/nmap · recommended 1×
  5. robertdavidgraham/masscan · recommended 1×
  • CATEGORY QUERY
    Need a cybersecurity reconnaissance tool with AI analysis that runs entirely offline and private.
    you: not recommended
    AI recommended (in order):
    1. scikit-learn (scikit-learn/scikit-learn)
    2. TensorFlow Lite (tensorflow/tensorflow)
    3. Nmap (nmap/nmap)
    4. Masscan (robertdavidgraham/masscan)
    5. Nikto (sullo/nikto)
    6. OpenVAS
    7. TensorFlow (tensorflow/tensorflow)
    8. PyTorch Mobile (pytorch/pytorch)
    9. PyTorch (pytorch/pytorch)
    10. Kali Linux
    11. Maltego
    12. theHarvester (laramies/theHarvester)
    13. Recon-ng (lanmaster53/recon-ng)
    14. Metasploit Framework (rapid7/metasploit-framework)
    15. pandas (pandas-dev/pandas)
    16. Shodan

    AI recommended 16 alternatives but never named Vyntral/god-eye. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Go-based tool for attack surface discovery and offensive security without cloud API costs.
    you: not recommended
    AI recommended (in order):
    1. Naabu (ProjectDiscovery/Naabu)
    2. Subfinder (ProjectDiscovery/Subfinder)
    3. httpx (ProjectDiscovery/httpx)
    4. Nuclei (ProjectDiscovery/nuclei)
    5. GoBuster (OJ/gobuster)
    6. Amass (OWASP/Amass)

    AI recommended 6 alternatives but never named Vyntral/god-eye. 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 Vyntral/god-eye?
    pass
    AI named Vyntral/god-eye explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite