REPOGEO REPORT · LITE

wang-rui/phishguard-scaffold

Default branch main · commit ab34017d · scanned 5/20/2026, 9:02:49 AM

GitHub: 1,006 stars · 169 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

30 /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

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 wang-rui/phishguard-scaffold, 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

phishing-detection, social-media-security, llm, llama, machine-learning, cybersecurity, research-framework, graph-neural-networks, adversarial-robustness

highlicense#2

Add a LICENSE file and state it in the README

Why:

COPY-PASTE FIX

Add a `LICENSE` file (e.g., `LICENSE.md` with the MIT License text) to the repository root. Then, add a line to your README, for example: `This project is released under the MIT License.`

mediumreadme#3

Clarify README's opening to emphasize 'open-source research framework'

Why:

CURRENT

**A unified framework for phishing detection and propagation control on social media, powered by LLaMA and advanced graph-based intervention strategies.**

COPY-PASTE FIX

**PhishGuard is an open-source research framework for joint semantic detection and propagation control of phishing attacks on social media, powered by LLaMA and advanced graph-based intervention strategies.**

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 wang-rui/phishguard-scaffold

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Proofpoint Targeted Attack Protection (TAP)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Proofpoint Targeted Attack Protection (TAP) · recommended 1×
CSC Digital Brand Services · recommended 1×
MarkMonitor · recommended 1×
ZeroFOX AI-Powered Protection · recommended 1×
PhishLabs Digital Risk Protection · recommended 1×

CATEGORY QUERY
What are robust frameworks for detecting and controlling phishing attacks on social media platforms?
you: not recommended
AI recommended (in order):
1. Proofpoint Targeted Attack Protection (TAP)
2. CSC Digital Brand Services
3. MarkMonitor
4. ZeroFOX AI-Powered Protection
5. PhishLabs Digital Risk Protection
6. Twitter Safety
7. Facebook Security
8. Maltego
9. Shodan
10. KnowBe4
11. Cofense
AI recommended 11 alternatives but never named wang-rui/phishguard-scaffold. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to leverage advanced AI models for semantic analysis in social media security applications?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Google Cloud Natural Language API
3. OpenAI API
4. spaCy
5. Amazon Comprehend
6. Flair
7. Microsoft Azure Cognitive Services for Language
AI recommended 7 alternatives but never named wang-rui/phishguard-scaffold. 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 wang-rui/phishguard-scaffold?
pass
AI named wang-rui/phishguard-scaffold explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts wang-rui/phishguard-scaffold in production, what risks or prerequisites should they evaluate first?
pass
AI named wang-rui/phishguard-scaffold 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 wang-rui/phishguard-scaffold solve, and who is the primary audience?
pass
AI named wang-rui/phishguard-scaffold 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 wang-rui/phishguard-scaffold. 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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HTML

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Pro

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wang-rui/phishguard-scaffold — 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