REPOGEO REPORT · LITE

mazzzystar/Queryable

Default branch main · commit b95a05a8 · scanned 6/26/2026, 9:48:19 AM

GitHub: 2,960 stars · 448 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)

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

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 mazzzystar/Queryable, 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 H1 to clarify the project's actual purpose.
Why:
CURRENT
```
# Queryable
```
COPY-PASTE FIX
```
# Queryable: Offline Natural Language Photo Search for iOS (CLIP/MobileCLIP)
```

mediumreadme#2

Strengthen the README's opening sentence to explicitly state its function and target platform.

Why:

CURRENT

The open-source code of Queryable, an iOS app, leverages the ~~OpenAI's CLIP~~ Apple's MobileCLIP model to conduct offline searches in the 'Photos' album.

COPY-PASTE FIX

Queryable is an open-source iOS app that enables offline natural language search for your photos using advanced AI models like Apple's MobileCLIP and OpenAI's CLIP. It allows you to find images in your 'Photos' album with descriptive text queries, ensuring complete privacy as all processing happens on-device.

lowtopics#3

Add more application-specific and on-device AI topics.

Why:

CURRENT

clip-model, ios, macos, mobile, mobile-clip, mobileclip, natural-language-image-search, openai-clip, photos, search, semantic-search, swiftui

COPY-PASTE FIX

clip-model, ios, macos, mobile, mobile-clip, mobileclip, natural-language-image-search, openai-clip, photos, search, semantic-search, swiftui, on-device-ai, coreml, photo-search-app, privacy-focused

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 mazzzystar/Queryable

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Core ML

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Core ML · recommended 2×
Vision Framework · recommended 1×
OpenAI's CLIP · recommended 1×
Google's ALIGN · recommended 1×
Create ML · recommended 1×

CATEGORY QUERY
How to implement natural language search for photos directly on an iOS device?
you: not recommended
AI recommended (in order):
1. Core ML
2. Vision Framework
3. OpenAI's CLIP
4. Google's ALIGN
5. Create ML
6. Photos Framework
7. Natural Language Framework
8. Faiss (facebookresearch/faiss)
9. Core Data
10. Realm (realm/realm-swift)
AI recommended 10 alternatives but never named mazzzystar/Queryable. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for an offline mobile solution to semantically search user photo albums using text queries.
you: not recommended
AI recommended (in order):
1. Core ML
2. TensorFlow Lite
3. CLIP
4. BLIP
5. ONNX Runtime
6. OpenCV
7. Realm
8. SQLite
AI recommended 8 alternatives but never named mazzzystar/Queryable. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 mazzzystar/Queryable?
pass
AI named mazzzystar/Queryable explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mazzzystar/Queryable in production, what risks or prerequisites should they evaluate first?
pass
AI named mazzzystar/Queryable 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 mazzzystar/Queryable solve, and who is the primary audience?
pass
AI named mazzzystar/Queryable 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 mazzzystar/Queryable. 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/mazzzystar/Queryable.svg)](https://repogeo.com/en/r/mazzzystar/Queryable)

HTML

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

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mazzzystar/Queryable — 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