REPOGEO REPORT · LITE

Spirit-AI-Team/spirit-v1.5

Default branch main · commit 6d67377f · scanned 6/6/2026, 2:58:09 AM

GitHub: 590 stars · 34 forks

AI VISIBILITY SCORE

35 /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

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 Spirit-AI-Team/spirit-v1.5, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

robotics, foundation-model, vision-language-model, vla, robot-control, ai-robotics, deep-learning

highreadme#2

Strengthen the README's introductory paragraph

Why:

CURRENT

This repository contains the official implementation of the **Spirit-v1.5 VLA model**, as well as the runtime wrapper required to reproduce our results on the RoboChallenge benchmark.

COPY-PASTE FIX

This repository contains the official implementation of the **Spirit-v1.5 VLA model**, a state-of-the-art robotic foundation model designed to enable advanced robotic manipulation capabilities. It includes the runtime wrapper required to reproduce our results on the RoboChallenge benchmark, where Spirit-v1.5 currently ranks **#1**.

mediumreadme#3

Add a 'Key Features' section to highlight differentiators

Why:

COPY-PASTE FIX

## Key Features

- **State-of-the-Art VLA Model:** Spirit-v1.5 is a Vision-Language-Action (VLA) model, specifically designed for complex robotic control.
- **RoboChallenge Benchmark Leader:** Currently ranks #1 on the RoboChallenge Table30 benchmark, demonstrating superior performance in robotic manipulation.
- **Open-Source Implementation:** Provides full implementation, including inference and fine-tuning code, for research and development.

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 Spirit-AI-Team/spirit-v1.5

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI GPT-4V

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI GPT-4V · recommended 1×
Google DeepMind RT-2 · recommended 1×
Google DeepMind PaLM-E · recommended 1×
Meta AI's Segment Anything Model (SAM) · recommended 1×
Microsoft's Florence-2 · recommended 1×

CATEGORY QUERY
What are the best vision-language models for controlling robotic systems?
you: not recommended
AI recommended (in order):
1. OpenAI GPT-4V
2. Google DeepMind RT-2
3. Google DeepMind PaLM-E
4. Meta AI's Segment Anything Model (SAM)
5. Microsoft's Florence-2
6. LLaVA
7. MiniGPT-4
AI recommended 7 alternatives but never named Spirit-AI-Team/spirit-v1.5. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Need a robust foundation model to develop advanced robotic manipulation capabilities.
you: not recommended
AI recommended (in order):
1. RT-X (Robotics Transformer X)
2. OpenAI's CLIP (Contrastive Language-Image Pre-training)
3. Google's PaLM-E (Pathways Language Model Embodied)
4. Meta's DINOv2 (Self-supervised Vision Transformer)
5. Microsoft's Florence (Foundation Model for Vision and Language)
6. Google's SayCan (Say, then Can)
AI recommended 6 alternatives but never named Spirit-AI-Team/spirit-v1.5. 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 Spirit-AI-Team/spirit-v1.5?
pass
AI named Spirit-AI-Team/spirit-v1.5 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Spirit-AI-Team/spirit-v1.5 in production, what risks or prerequisites should they evaluate first?
pass
AI named Spirit-AI-Team/spirit-v1.5 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 Spirit-AI-Team/spirit-v1.5 solve, and who is the primary audience?
pass
AI named Spirit-AI-Team/spirit-v1.5 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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MARKDOWN (README)

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HTML

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Spirit-AI-Team/spirit-v1.5 — 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