REPOGEO REPORT · LITE

Yeti-791/Tsec-Hackathon

Default branch main · commit 15a95bce · scanned 5/29/2026, 12:23:19 PM

GitHub: 527 stars · 63 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 Yeti-791/Tsec-Hackathon, 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 its role as a navigation hub

Why:

CURRENT

This repository is the official event resource warehouse for Tencent Cloud Intelligent Penetration Hackathon...

COPY-PASTE FIX

This repository serves as the official one-stop navigation hub for top open-source LLM-based autonomous penetration agents, curated from the Tencent Cloud Intelligent Penetration Hackathon. It provides a central resource for leading projects, technical materials, and insights into AI-driven offensive security.

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the repository root, specifying the applicable open-source license (e.g., MIT, Apache-2.0, or a custom license if applicable) to clarify usage rights for the repository's content.

mediumabout#3

Refine the 'About' description to emphasize its curated hub role

Why:

CURRENT

腾讯云智能渗透黑客松 Official repository of Tencent Cloud Intelligent Penetration Hackathon. Showcasing top open-source projects of LLM-based autonomous penetration agents, including multi-agent collaboration, automated penetration, AI-driven offensive security, and intelligent attack-defense solutions.

COPY-PASTE FIX

Official curated hub for the Tencent Cloud Intelligent Penetration Hackathon, providing a one-stop navigation page for top open-source LLM-based autonomous penetration agents. Explore leading projects, technical resources, and insights into multi-agent collaboration, automated penetration, and AI-driven offensive security solutions.

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 Yeti-791/Tsec-Hackathon

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Metasploit Framework

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Metasploit Framework · recommended 2×
OpenVAS / Greenbone Vulnerability Management (GVM) · recommended 1×
Nmap (Network Mapper) · recommended 1×
OWASP ZAP (Zed Attack Proxy) · recommended 1×
Nuclei · recommended 1×

CATEGORY QUERY
What are the best open-source projects for AI-driven automated penetration testing?
you: not recommended
AI recommended (in order):
1. OpenVAS / Greenbone Vulnerability Management (GVM)
2. Metasploit Framework
3. Nmap (Network Mapper)
4. OWASP ZAP (Zed Attack Proxy)
5. Nuclei
6. Prowler
AI recommended 6 alternatives but never named Yeti-791/Tsec-Hackathon. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can large language models be used to automate offensive security tasks?
you: not recommended
AI recommended (in order):
1. ChatGPT / GPT-4
2. Google Bard / Gemini
3. Perplexity AI
4. GitHub Copilot
5. CodeQL
6. Burp Suite
7. Metasploit Framework
8. Pwnagotchi
9. Empire
10. Covenant
AI recommended 10 alternatives but never named Yeti-791/Tsec-Hackathon. 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 Yeti-791/Tsec-Hackathon?
pass
AI named Yeti-791/Tsec-Hackathon explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Yeti-791/Tsec-Hackathon in production, what risks or prerequisites should they evaluate first?
pass
AI named Yeti-791/Tsec-Hackathon 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 Yeti-791/Tsec-Hackathon solve, and who is the primary audience?
pass
AI did not name Yeti-791/Tsec-Hackathon — 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?

Embed your GEO score

Drop this badge into the README of Yeti-791/Tsec-Hackathon. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/Yeti-791/Tsec-Hackathon.svg)](https://repogeo.com/en/r/Yeti-791/Tsec-Hackathon)

HTML

<a href="https://repogeo.com/en/r/Yeti-791/Tsec-Hackathon"><img src="https://repogeo.com/badge/Yeti-791/Tsec-Hackathon.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

Yeti-791/Tsec-Hackathon — 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