RRepoGEO

REPOGEO REPORT · LITE

zenml-io/awesome-open-data-annotation

Default branch main · commit 4f58fd5e · scanned 6/8/2026, 7:57:39 AM

GitHub: 705 stars · 65 forks

AI VISIBILITY SCORE
22 /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
1 / 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 zenml-io/awesome-open-data-annotation, 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 and repository description to clarify it's a list

    Why:

    CURRENT
    # 🏷 Open Source Data Annotation & Labeling Tools
    COPY-PASTE FIX
    # 🏷 Awesome Open Source Data Annotation & Labeling Tools
  • highabout#2
    Update the repository description to clarify it's a list

    Why:

    CURRENT
    Open Source Data Annotation & Labeling Tools
    COPY-PASTE FIX
    A curated list of Awesome Open Source Data Annotation & Labeling Tools
  • mediumtopics#3
    Add 'awesome-list' and 'awesome' topics

    Why:

    CURRENT
    ai, annotation, datacentric, labelled-data, labelling, machine-learning, mlops
    COPY-PASTE FIX
    ai, annotation, datacentric, labelled-data, labelling, machine-learning, mlops, awesome-list, awesome

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 zenml-io/awesome-open-data-annotation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prodigy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Prodigy · recommended 2×
  2. heartexlabs/label-studio · recommended 1×
  3. opencv/cvat · recommended 1×
  4. doccano/doccano · recommended 1×
  5. microsoft/VoTT · recommended 1×
  • CATEGORY QUERY
    What open-source tools are available for efficiently labeling data for machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Label Studio (heartexlabs/label-studio)
    2. CVAT (opencv/cvat)
    3. Doccano (doccano/doccano)
    4. Prodigy
    5. VoTT (microsoft/VoTT)
    6. LabelImg (tzutalin/labelImg)
    7. Annotorious (annotorious/annotorious)

    AI recommended 7 alternatives but never named zenml-io/awesome-open-data-annotation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a comprehensive list of free tools to annotate various data types for ML projects.
    you: not recommended
    AI recommended (in order):
    1. LabelImg
    2. Label Studio
    3. CVAT
    4. Doccano
    5. Prodigy
    6. VoTT
    7. Audino

    AI recommended 7 alternatives but never named zenml-io/awesome-open-data-annotation. 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 zenml-io/awesome-open-data-annotation?
    pass
    AI did not name zenml-io/awesome-open-data-annotation — likely talking about a different project

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

  • If a team adopts zenml-io/awesome-open-data-annotation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zenml-io/awesome-open-data-annotation 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 zenml-io/awesome-open-data-annotation solve, and who is the primary audience?
    pass
    AI did not name zenml-io/awesome-open-data-annotation — likely talking about a different project

    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 zenml-io/awesome-open-data-annotation. 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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zenml-io/awesome-open-data-annotation — 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