RRepoGEO

REPOGEO REPORT · LITE

joanrod/star-vector

Default branch main · commit 0e083c19 · scanned 6/28/2026, 6:13:08 AM

GitHub: 4,466 stars · 254 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
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 joanrod/star-vector, 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
    Add a concise, domain-specific opening sentence to the README

    Why:

    COPY-PASTE FIX
    StarVector is a novel foundation model that revolutionizes **SVG generation** by treating vectorization as a code generation task, processing both visual and textual inputs to produce high-quality Scalable Vector Graphics.
  • mediumtopics#2
    Add more specific topics to improve category recall

    Why:

    CURRENT
    llm, multimodal-large-language-models, svg, vlm
    COPY-PASTE FIX
    llm, multimodal-large-language-models, svg, vlm, image-to-svg, text-to-svg, vector-graphics-generation, ai-vectorization, foundation-model
  • lowcomparison#3
    Add a 'Comparison' or 'Why StarVector?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why StarVector? (or ## Comparison to Existing Tools)

StarVector differentiates itself from traditional vectorization software (e.g., Adobe Illustrator, Inkscape, Vectorizer.AI) and general image generation models (e.g., Midjourney, DALL-E 3) by focusing on **SVG code generation** from both visual and textual inputs. As a **foundation model**, it provides a programmatic approach to high-quality vector graphics, enabling developers and researchers to integrate advanced vectorization capabilities into their own applications and workflows, rather than serving as an end-user application.

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 joanrod/star-vector
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Adobe Illustrator
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Adobe Illustrator · recommended 2×
  2. Inkscape · recommended 2×
  3. Vectorizer.AI · recommended 1×
  4. Vectornator · recommended 1×
  5. Autotracer.org · recommended 1×
  • CATEGORY QUERY
    What tools can convert raster images into scalable vector graphics using AI?
    you: not recommended
    AI recommended (in order):
    1. Vectorizer.AI
    2. Adobe Illustrator
    3. Inkscape
    4. Vectornator
    5. Autotracer.org
    6. Vector Magic
    7. CorelDRAW

    AI recommended 7 alternatives but never named joanrod/star-vector. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to generate vector graphics code from natural language descriptions or visual inputs?
    you: not recommended
    AI recommended (in order):
    1. Vecteezy Convert
    2. Adobe Illustrator
    3. Inkscape
    4. Midjourney
    5. DALL-E 3
    6. Figma
    7. Canva
    8. AutoTrace

    AI recommended 8 alternatives but never named joanrod/star-vector. 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 joanrod/star-vector?
    pass
    AI named joanrod/star-vector explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts joanrod/star-vector in production, what risks or prerequisites should they evaluate first?
    pass
    AI named joanrod/star-vector 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 joanrod/star-vector solve, and who is the primary audience?
    pass
    AI named joanrod/star-vector explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite