RRepoGEO

REPOGEO REPORT · LITE

Armur-Ai/Pentest-Swarm-AI

Default branch main · commit 2d17a849 · scanned 5/17/2026, 9:11:53 PM

GitHub: 1,139 stars · 246 forks

AI VISIBILITY SCORE
35 /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
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 Armur-Ai/Pentest-Swarm-AI, 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
    Reorder README introduction to lead with current pentesting capabilities

    Why:

    CURRENT
    The section "Built for the Mythos era" immediately follows the H1 and subtitle, discussing future models before current capabilities.
    COPY-PASTE FIX
    Immediately after the H1 and subtitle, add a concise sentence that clearly states its current function and AI integration. For example: "Pentest Swarm AI is an open-source, AI-driven penetration testing tool that orchestrates a swarm of agents for recon, exploitation, and reporting, compatible with current LLMs like Claude Sonnet, Opus, Llama, and OpenAI-compatible models." This sentence should precede the "Built for the Mythos era" section.
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    A valid URL for the project's homepage (e.g., `https://armur.ai/pentest-swarm-ai` or similar) should be added to the repository's 'About' section.
  • mediumreadme#3
    Emphasize current LLM compatibility and supported security tools in README

    Why:

    CURRENT
    "Wire in the model of your choice today — Claude Sonnet, Opus, Llama, anything OpenAI-compatible — and swap in Mythos the day access opens." is placed after the "Built for the Mythos era" section.
    COPY-PASTE FIX
    Create a dedicated "Key Features" or "Capabilities" section early in the README, listing current LLM compatibility and integrated security tools as distinct bullet points. For example:
    "### Key Features
    *   **AI-Powered Pentesting:** Orchestrates autonomous AI agents for recon, classification, exploitation, and reporting.
    *   **Broad LLM Compatibility:** Supports current models like Claude Sonnet, Opus, Llama, and any OpenAI-compatible LLM.
    *   **Integrated Security Tools:** Leverages 7+ native security tools including nmap, sqlmap, Burp, ZAP, and Metasploit."

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 Armur-Ai/Pentest-Swarm-AI
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4 / GPT-3.5
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 / GPT-3.5 · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. Llama 2 · recommended 1×
  5. Mixtral · recommended 1×
  • CATEGORY QUERY
    How to automate full penetration testing workflows using multiple AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Llama 2
    5. Mixtral
    6. Hugging Face Transformers (huggingface/transformers)
    7. vLLM (vllm-project/vllm)
    8. NVIDIA Triton Inference Server (triton-inference-server/server)
    9. AWS Bedrock
    10. Google Cloud Vertex AI
    11. Metasploit Framework (rapid7/metasploit-framework)
    12. Nuclei (projectdiscovery/nuclei)
    13. Nessus
    14. OpenVAS (greenbone/openvas)
    15. Burp Suite
    16. Nmap

    AI recommended 16 alternatives but never named Armur-Ai/Pentest-Swarm-AI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an AI-driven system for continuous security monitoring and bug bounty program support.
    you: not recommended
    AI recommended (in order):
    1. HackerOne Clear
    2. Snyk
    3. CrowdStrike Falcon
    4. Wiz
    5. Qualys
    6. SentinelOne Singularity Platform

    AI recommended 6 alternatives but never named Armur-Ai/Pentest-Swarm-AI. 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 Armur-Ai/Pentest-Swarm-AI?
    pass
    AI named Armur-Ai/Pentest-Swarm-AI explicitly

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

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

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

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