RRepoGEO

REPOGEO REPORT · LITE

microsoft/CyberBattleSim

Default branch main · commit 854d6966 · scanned 5/18/2026, 8:22:18 AM

GitHub: 1,770 stars · 282 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)

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

AI VISIBILITY SCORE
54 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
Rule findings
1 pass · 1 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 microsoft/CyberBattleSim, 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 opening to emphasize RL for cybersecurity agents

    Why:

    CURRENT
    CyberBattleSim is an experimentation research platform to investigate the interaction of automated agents operating in a simulated abstract enterprise network environment. The simulation provides a high-level abstraction of computer networks and cyber security concepts. Its Python-based Open AI Gym interface allows for the training of automated agents using reinforcement learning algorithms.
    COPY-PASTE FIX
    CyberBattleSim is a **reinforcement learning platform** for cybersecurity research, enabling the training and evaluation of **AI agents** in a simulated abstract network environment. It provides a Python-based OpenAI Gym interface to investigate the interaction of automated attack and defense strategies.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    reinforcement-learning, cybersecurity, network-simulation, ai-agents, openai-gym, security-research, adversarial-ai
  • mediumhomepage#3
    Add a homepage URL to the About section

    Why:

    COPY-PASTE FIX
    https://www.microsoft.com/en-us/research/project/cyberbattlesim/

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
1 / 2
50% of queries surface microsoft/CyberBattleSim
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
NS-3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NS-3 · recommended 2×
  2. GNS3 · recommended 1×
  3. EVE-NG · recommended 1×
  4. Scapy · recommended 1×
  5. Nmap · recommended 1×
  • CATEGORY QUERY
    How can I simulate cyber attacks and defenses for training AI agents in a network environment?
    you: #6
    AI recommended (in order):
    1. GNS3
    2. EVE-NG
    3. Scapy
    4. Nmap
    5. Metasploit
    6. CyberBattleSim ← you
    7. OpenAI Gym
    8. NS-3
    9. Mininet
    10. Palo Alto Networks Cyber Range
    11. FortiGate Cyber Range
    Show full AI answer
  • CATEGORY QUERY
    Looking for a reinforcement learning platform to test network security strategies with simulated agents.
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. RLlib (ray-project/ray)
    3. OpenAI Baselines (openai/baselines)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. NS-3
    6. Mininet (mininet/mininet)

    AI recommended 6 alternatives but never named microsoft/CyberBattleSim. 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 microsoft/CyberBattleSim?
    pass
    AI named microsoft/CyberBattleSim explicitly

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

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

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

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MARKDOWN (README)
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microsoft/CyberBattleSim — 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