RRepoGEO

REPOGEO REPORT · LITE

Acmesec/theAIMythbook

Default branch master · commit 1b670a9c · scanned 6/27/2026, 6:16:58 AM

GitHub: 1,303 stars · 131 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-security, large-language-models, llm-security, prompt-engineering, ai-guidelines, cybersecurity, ai-applications, knowledge-base
  • highlicense#2
    Add a standard open-source license file

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file containing the text of the `MIT License`.
  • mediumreadme#3
    Add a concise English summary to the README

    Why:

    CURRENT
    The README starts with `# Ai迷思录（应用与安全指南）`.
    COPY-PASTE FIX
    Add the following line at the very top of the README: `This repository serves as a comprehensive knowledge base and guide on AI application and security, debunking common myths and providing practical insights for large language 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 Acmesec/theAIMythbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AWS S3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS S3 · recommended 1×
  2. Azure Blob Storage · recommended 1×
  3. Google Cloud Storage · recommended 1×
  4. HashiCorp Vault · recommended 1×
  5. Databricks Unity Catalog · recommended 1×
  • CATEGORY QUERY
    What are essential security guidelines for developing and deploying large AI models?
    you: not recommended
    AI recommended (in order):
    1. AWS S3
    2. Azure Blob Storage
    3. Google Cloud Storage
    4. HashiCorp Vault
    5. Databricks Unity Catalog
    6. MLflow Model Registry (mlflow/mlflow)
    7. Git
    8. GitHub
    9. GitLab
    10. Bitbucket
    11. Docker Content Trust
    12. Notary (theupdateframework/notary)
    13. TUF (The Update Framework) (theupdateframework/tuf)
    14. OpenVEX
    15. NVIDIA Triton Inference Server (triton-inference-server/server)
    16. Kubernetes (kubernetes/kubernetes)
    17. Istio (istio/istio)
    18. Linkerd (linkerd/linkerd2)
    19. Cloudflare Workers AI
    20. AWS SageMaker Endpoints
    21. Guardrails AI (guardrails-ai/guardrails)
    22. Snyk
    23. Dependabot
    24. Trivy (aquasecurity/trivy)
    25. Prometheus (prometheus/prometheus)
    26. Grafana (grafana/grafana)
    27. ELK Stack
    28. Splunk
    29. ART - Adversarial Robustness Toolbox by IBM (Trusted-AI/adversarial-robustness-toolbox)
    30. CleverHans (cleverhans-lab/cleverhans)
    31. Wiz
    32. Orca Security
    33. AWS Security Hub
    34. Azure Security Center
    35. Google Cloud Security Command Center
    36. SHAP (shap/shap)
    37. LIME (marcotcr/lime)
    38. Google Cloud Explainable AI
    39. IBM AI Fairness 360 (Trusted-AI/AIF360)
    40. Google's What-If Tool (PAIR-code/what-if-tool)
    41. Google's Differential Privacy library (google/differential-privacy)
    42. Opacus by Meta (pytorch/opacus)
    43. Microsoft SEAL (microsoft/SEAL)
    44. TenSEAL (OpenMined/TenSEAL)
    45. Apache Atlas (apache/atlas)
    46. Collibra

    AI recommended 46 alternatives but never named Acmesec/theAIMythbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to design and optimize prompts for large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Playground / API
    2. LangChain
    3. Guidance
    4. PromptPerfect
    5. Weights & Biases (W&B Prompts)
    6. Humanloop
    7. DSPy

    AI recommended 7 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 named Acmesec/theAIMythbook explicitly

    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 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?

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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