RRepoGEO

REPOGEO REPORT · LITE

rodrigopivi/Chatito

Default branch master · commit 8ad5d983 · scanned 6/7/2026, 4:56:41 AM

GitHub: 888 stars · 149 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 rodrigopivi/Chatito, 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 paragraph to emphasize NLU dataset generation for conversational AI

    Why:

    CURRENT
    Chatito helps you generate datasets for training and validating chatbot models using a simple DSL.
    COPY-PASTE FIX
    Chatito is a powerful tool for **programmatically generating diverse, high-quality NLU training datasets** for conversational AI models, chatbots, and NLP tasks like named entity recognition or text classification. It uses a simple Domain-Specific Language (DSL) to create structured, synthetic text data, offering a precise alternative to manual annotation or generic data augmentation.
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why Chatito? (Comparison to Alternatives)' or similar, explicitly explaining its unique position and how it differs from LLMs (like GPT-3/4), generic data generators (like Faker), and data augmentation tools (like NLPAug) for NLU dataset creation.
  • lowabout#3
    Refine the 'About' description to reinforce NLU and conversational AI keywords

    Why:

    CURRENT
    🎯🗯 Dataset generation for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!
    COPY-PASTE FIX
    🎯🗯 Generate diverse **NLU training datasets** for **conversational AI models**, chatbots, NLP tasks (named entity recognition, text classification) using a simple DSL!

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 rodrigopivi/Chatito
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Snorkel
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Snorkel · recommended 2×
  2. GPT-3 / GPT-4 · recommended 2×
  3. Faker · recommended 2×
  4. DataSynthesizer · recommended 2×
  5. Rasa NLU · recommended 1×
  • CATEGORY QUERY
    How to efficiently generate diverse training datasets for conversational AI models?
    you: not recommended
    AI recommended (in order):
    1. Rasa NLU
    2. Snorkel
    3. GPT-3 / GPT-4
    4. Claude
    5. Llama 2
    6. Scale AI
    7. Appen
    8. DataLoop
    9. Prodigy
    10. Faker
    11. DataSynthesizer
    12. TextCortex
    13. QuillBot API

    AI recommended 13 alternatives but never named rodrigopivi/Chatito. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for creating synthetic text data for NLP tasks like entity recognition?
    you: not recommended
    AI recommended (in order):
    1. Faker
    2. NLPAug
    3. DataSynthesizer
    4. GPT-3 / GPT-4
    5. TextAttack
    6. Jinja2
    7. Snorkel

    AI recommended 7 alternatives but never named rodrigopivi/Chatito. 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 rodrigopivi/Chatito?
    pass
    AI named rodrigopivi/Chatito explicitly

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

  • If a team adopts rodrigopivi/Chatito in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rodrigopivi/Chatito 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 rodrigopivi/Chatito solve, and who is the primary audience?
    pass
    AI named rodrigopivi/Chatito 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 rodrigopivi/Chatito. 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/rodrigopivi/Chatito.svg)](https://repogeo.com/en/r/rodrigopivi/Chatito)
HTML
<a href="https://repogeo.com/en/r/rodrigopivi/Chatito"><img src="https://repogeo.com/badge/rodrigopivi/Chatito.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

rodrigopivi/Chatito — 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