RRepoGEO

REPOGEO REPORT · LITE

cnitlrt/AutoTeam

Default branch dev · commit 91eb6ad8 · scanned 5/27/2026, 11:16:54 AM

GitHub: 1,096 stars · 250 forks

AI VISIBILITY SCORE
28 /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
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 cnitlrt/AutoTeam, 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
    Explicitly clarify the target platform in the README's opening subtitle

    Why:

    CURRENT
    **面向 ChatGPT Team 的账号轮转与认证同步工具**
    COPY-PASTE FIX
    **面向 OpenAI ChatGPT Team 的账号轮转与认证同步工具，非 Microsoft Teams 或 GitHub Classroom**
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/cnitlrt/AutoTeam

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 cnitlrt/AutoTeam
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LiteLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LiteLLM · recommended 2×
  2. AWS Boto3 · recommended 1×
  3. Google Cloud Client Libraries · recommended 1×
  4. Azure SDK for Python · recommended 1×
  5. google.cloud.aiplatform · recommended 1×
  • CATEGORY QUERY
    How to automate switching between multiple AI service accounts based on usage limits?
    you: not recommended
    AI recommended (in order):
    1. AWS Boto3
    2. Google Cloud Client Libraries
    3. Azure SDK for Python
    4. google.cloud.aiplatform
    5. azure-ai-textanalytics
    6. Amazon CloudWatch
    7. Google Cloud Monitoring
    8. Azure Monitor
    9. AWS Secrets Manager
    10. Google Secret Manager
    11. Azure Key Vault
    12. AWS API Gateway
    13. AWS Lambda
    14. Google Cloud Endpoints
    15. Google Cloud Functions
    16. Azure API Management
    17. Azure Functions
    18. NGINX
    19. HAProxy
    20. Envoy Proxy
    21. Cortex
    22. BentoML
    23. LiteLLM
    24. OpenAI-Proxy
    25. Redis
    26. PagerDuty
    27. Slack

    AI recommended 27 alternatives but never named cnitlrt/AutoTeam. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for managing multiple AI model access credentials and automatically handling usage quotas?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. LangChain
    3. LiteLLM
    4. Nginx
    5. Caddy
    6. Valyr

    AI recommended 6 alternatives but never named cnitlrt/AutoTeam. 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 cnitlrt/AutoTeam?
    pass
    AI did not name cnitlrt/AutoTeam — 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 cnitlrt/AutoTeam in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cnitlrt/AutoTeam 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 cnitlrt/AutoTeam solve, and who is the primary audience?
    pass
    AI named cnitlrt/AutoTeam explicitly

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

Embed your GEO score

Drop this badge into the README of cnitlrt/AutoTeam. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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cnitlrt/AutoTeam — 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