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

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 3 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 3×
Anthropic Claude · recommended 3×
Google Gemini · recommended 3×
Hugging Face Transformers · recommended 2×
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?

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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>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite