RRepoGEO

REPOGEO REPORT · LITE

mazzzystar/Queryable

Default branch main · commit b95a05a8 · scanned 5/15/2026, 4:59:58 PM

GitHub: 2,940 stars · 450 forks

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's opening sentence to clarify project type

    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
    This repository contains the open-source code for Queryable, an iOS app that enables offline natural language search of your 'Photos' album using Apple's MobileCLIP model.
  • mediumabout#2
    Clarify the repository description to emphasize it's an app's source code

    Why:

    CURRENT
    Run OpenAI's CLIP and Apple's MobileCLIP model on iOS to search photos.
    COPY-PASTE FIX
    Source code for Queryable, an iOS app that runs OpenAI's CLIP and Apple's MobileCLIP model for offline natural language photo search.
  • lowtopics#3
    Add 'coreml' to the repository 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, coreml, ios, macos, mobile, mobile-clip, mobileclip, natural-language-image-search, openai-clip, photos, search, semantic-search, swiftui

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
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. Faiss · recommended 2×
  3. CLIP · recommended 1×
  4. BLIP · recommended 1×
  5. Core Data · recommended 1×
  • CATEGORY QUERY
    How can I implement natural language search for photos directly on an iOS device?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. CLIP
    3. BLIP
    4. Core Data
    5. Realm
    6. Vision Framework
    7. VNGenerateImageFeaturePrintRequest
    8. VNRecognizeTextRequest
    9. VNDetectObjectsRequest
    10. VNDetectFaceRectanglesRequest
    11. Faiss
    12. Hugging Face Transformers
    13. Swift-Transformers
    14. coremltools

    AI recommended 14 alternatives but never named mazzzystar/Queryable. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an offline solution to semantically search user photos on mobile devices.
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. TensorFlow Lite
    3. PyTorch
    4. TensorFlow
    5. Faiss
    6. ScaNN
    7. Annoy
    8. Hnswlib

    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.

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MARKDOWN (README)
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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