REPOGEO REPORT · LITE

takahirom/arbigent

Default branch main · commit 4a2dd5bf · scanned 6/3/2026, 3:17:09 PM

GitHub: 605 stars · 60 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 takahirom/arbigent, 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

highabout#1

Reposition the 'about' description for clarity

Why:

CURRENT

AI Agent for testing Android, iOS, and Web apps. Get Started in 5 Minutes. Arbigent's intuitive UI and powerful code interface make it accessible to everyone, while its scenario breakdown feature ensures scalability for even the most complex tasks.

COPY-PASTE FIX

Arbigent is an AI Agent testing framework for Android, iOS, and Web apps. It automates UI testing with an intuitive interface and scenario breakdown for complex tasks.

highreadme#2

Clarify the README's opening statement

Why:

CURRENT

**Zero to AI agent testing in minutes. Arbigent's intuitive UI and powerful code interface make it accessible to everyone, while its scenario breakdown feature ensures scalability for even the most complex tasks.**

COPY-PASTE FIX

**Arbigent is an AI agent testing framework specifically designed for automating UI tests across Android, iOS, and Web applications. It enables zero-to-AI agent testing in minutes with an intuitive UI and powerful code interface, ensuring scalability for even the most complex tasks through scenario breakdown.**

mediumtopics#3

Add more specific UI/E2E testing topics

Why:

CURRENT

agentic, ai, aiagent, android, gemini, ios, openai-api, testing

COPY-PASTE FIX

agentic, ai, aiagent, android, gemini, ios, openai-api, testing, ui-testing, e2e-testing, cross-platform, mobile-testing, web-testing

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 takahirom/arbigent

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Testim.io

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Testim.io · recommended 2×
Sauce Labs · recommended 2×
percy.io · recommended 2×
Applitools Ultrafast Test Cloud with Applitools Eyes · recommended 1×
Mabl · recommended 1×

CATEGORY QUERY
How can I automate cross-platform UI testing for mobile and web apps using AI agents?
you: not recommended
AI recommended (in order):
1. Applitools Ultrafast Test Cloud with Applitools Eyes
2. Testim.io
3. Mabl
4. Sauce Labs
5. Cypress (cypress-io/cypress)
6. cypress-image-snapshot (jaredpalmer/cypress-image-snapshot)
7. percy.io
8. Playwright (microsoft/playwright)
9. playwright-visual-regression (argos-ci/playwright-visual-regression)
10. percy.io
11. Leapwork
AI recommended 11 alternatives but never named takahirom/arbigent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a scalable AI-driven solution for robust UI testing of dynamic Android, iOS, and web applications.
you: not recommended
AI recommended (in order):
1. Applitools Eyes
2. Testim.io
3. Sauce Labs
4. Cypress
5. cypress-plugin-snapshots (jaredpalmer/cypress-plugin-snapshots)
6. Kobiton
7. Tricentis Tosca
AI recommended 7 alternatives but never named takahirom/arbigent. 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 takahirom/arbigent?
pass
AI named takahirom/arbigent explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts takahirom/arbigent in production, what risks or prerequisites should they evaluate first?
pass
AI named takahirom/arbigent 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 takahirom/arbigent solve, and who is the primary audience?
pass
AI named takahirom/arbigent 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 takahirom/arbigent. 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/takahirom/arbigent.svg)](https://repogeo.com/en/r/takahirom/arbigent)

HTML

<a href="https://repogeo.com/en/r/takahirom/arbigent"><img src="https://repogeo.com/badge/takahirom/arbigent.svg" alt="RepoGEO" /></a>

Pro

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takahirom/arbigent — 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