RRepoGEO

REPOGEO REPORT · LITE

Acmesec/theAIMythbook

Default branch master · commit 1b670a9c · scanned 5/16/2026, 10:06:54 AM

GitHub: 1,281 stars · 129 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Acmesec/theAIMythbook, 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's opening to clarify the repo's nature as a guide/resource

    Why:

    CURRENT
    # Ai迷思录（应用与安全指南）
    COPY-PASTE FIX
    # Ai迷思录（应用与安全指南）

This repository serves as a comprehensive guide and mythbook on AI application and security, offering best practices for large language model prompt design and addressing common pitfalls.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-security, prompt-engineering, large-language-models, ai-applications, cybersecurity, ai-guide, ai-best-practices, ai-myths, ai-safety, 大模型, AI安全, 提示工程
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the Creative Commons Attribution 4.0 International (CC-BY-4.0) license, suitable for documentation and guides.

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 Acmesec/theAIMythbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OWASP/esapi-java-project
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OWASP/esapi-java-project · recommended 1×
  2. Sanitizer (Python library) · recommended 1×
  3. DOMPurify/DOMPurify · recommended 1×
  4. OpenAI Moderation API · recommended 1×
  5. Azure Content Safety · recommended 1×
  • CATEGORY QUERY
    What are best practices for securing large language model applications and avoiding common issues?
    you: not recommended
    AI recommended (in order):
    1. OWASP ESAPI (OWASP/esapi-java-project)
    2. Sanitizer (Python library)
    3. DOMPurify (DOMPurify/DOMPurify)
    4. OpenAI Moderation API
    5. Azure Content Safety
    6. Google Cloud Perspective API
    7. Auth0
    8. Okta
    9. AWS IAM
    10. Google Cloud IAM
    11. Azure AD
    12. Cloudflare
    13. AWS WAF
    14. Nginx
    15. AWS KMS
    16. Google Cloud KMS
    17. Azure Key Vault
    18. HashiCorp Vault (hashicorp/vault)
    19. Presidio (Microsoft) (microsoft/presidio)
    20. Datadog
    21. Splunk
    22. ELK Stack
    23. Burp Suite
    24. OWASP ZAP (zaproxy/zaproxy)
    25. HackerOne
    26. Bugcrowd

    AI recommended 26 alternatives but never named Acmesec/theAIMythbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I effectively design and optimize prompts for large language models to improve performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weights & Biases Prompts
    4. Humanloop
    5. OpenAI API
    6. Jinja2
    7. Python f-strings
    8. Hugging Face Evaluate library
    9. Mechanical Turk

    AI recommended 9 alternatives but never named Acmesec/theAIMythbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Acmesec/theAIMythbook?
    pass
    AI did not name Acmesec/theAIMythbook — 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 Acmesec/theAIMythbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Acmesec/theAIMythbook 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 Acmesec/theAIMythbook solve, and who is the primary audience?
    pass
    AI named Acmesec/theAIMythbook explicitly

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

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Acmesec/theAIMythbook — 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