RRepoGEO

REPOGEO REPORT · LITE

refuel-ai/autolabel

Default branch main · commit 404dcd01 · scanned 6/22/2026, 2:57:51 PM

GitHub: 2,322 stars · 160 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 refuel-ai/autolabel, 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 README's opening statement to highlight LLM-powered data labeling

    Why:

    CURRENT
    The README currently starts with social links and quick install before explaining 'What is Autolabel'.
    COPY-PASTE FIX
    Add a concise, prominent statement at the very top of the README (e.g., right after the title/badges) like: 'Autolabel is a Python library for programmatic data labeling, cleaning, and enrichment of text datasets using Large Language Models (LLMs).'
  • mediumtopics#2
    Add specific data labeling and annotation topics

    Why:

    CURRENT
    anthropic-claude, data-science, gpt-4, huggingface-transformers, langchain, large-language-models, llm, llms, machine-learning, openai, python
    COPY-PASTE FIX
    anthropic-claude, data-science, data-labeling, data-annotation, dataset-generation, gpt-4, huggingface-transformers, langchain, large-language-models, llm, llms, machine-learning, openai, python
  • lowreadme#3
    Add a 'Why Autolabel' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why Autolabel?' or 'Autolabel vs. X' that explicitly outlines its unique focus on programmatic, LLM-driven data labeling compared to manual methods, rule-based systems, or general LLM APIs.

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 refuel-ai/autolabel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 3×
  2. Anthropic Claude · recommended 3×
  3. Google Gemini · recommended 3×
  4. Hugging Face Transformers · recommended 2×
  5. Snorkel Flow · recommended 1×
  • CATEGORY QUERY
    How can I automatically label large text datasets using large language models?
    you: not recommended
    AI recommended (in order):
    1. Snorkel Flow
    2. Argilla
    3. OpenAI API
    4. Anthropic Claude
    5. Google Gemini
    6. Prodigy
    7. OpenAI API
    8. Anthropic Claude
    9. Google Gemini
    10. OpenAI API
    11. Anthropic Claude
    12. Google Gemini
    13. Hugging Face Transformers
    14. Llama 3
    15. Mistral
    16. Falcon
    17. AWS SageMaker
    18. Google Cloud Vertex AI

    AI recommended 18 alternatives but never named refuel-ai/autolabel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries help clean and enrich text data using generative AI?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Python Library
    2. Hugging Face Transformers
    3. LangChain
    4. Haystack
    5. Spacy

    AI recommended 5 alternatives but never named refuel-ai/autolabel. 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 refuel-ai/autolabel?
    pass
    AI named refuel-ai/autolabel explicitly

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

  • If a team adopts refuel-ai/autolabel in production, what risks or prerequisites should they evaluate first?
    pass
    AI named refuel-ai/autolabel 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 refuel-ai/autolabel solve, and who is the primary audience?
    pass
    AI named refuel-ai/autolabel 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 refuel-ai/autolabel. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/refuel-ai/autolabel.svg)](https://repogeo.com/en/r/refuel-ai/autolabel)
HTML
<a href="https://repogeo.com/en/r/refuel-ai/autolabel"><img src="https://repogeo.com/badge/refuel-ai/autolabel.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

refuel-ai/autolabel — 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