RRepoGEO

REPOGEO REPORT · LITE

RUC-NLPIR/Arbor

Default branch main · commit 1d106a13 · scanned 6/21/2026, 11:41:45 AM

GitHub: 578 stars · 78 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 RUC-NLPIR/Arbor, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify "autonomous research agent" and counter miscategorization

    Why:

    CURRENT
    Arbor is an autonomous research agent that turns a long-horizon objective into a cumulative search.
    COPY-PASTE FIX
    Arbor is an autonomous research agent that turns a long-horizon objective into a cumulative search. Unlike traditional NLP tools or dependency parsers, Arbor focuses on automating scientific experimentation, hypothesis generation, and iterative code refinement.
  • mediumreadme#2
    Add a "What problem does Arbor solve?" section to README

    Why:

    COPY-PASTE FIX
    ## What problem does Arbor solve?
Arbor addresses the challenge of automating complex scientific research and development workflows. It helps researchers and engineers by autonomously generating hypotheses, designing and running experiments, iteratively refining code, and learning from outcomes to accelerate discovery and optimization.

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 RUC-NLPIR/Arbor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 · recommended 1×
  2. GPT-3.5 Turbo · recommended 1×
  3. Anthropic Claude · recommended 1×
  4. Google Gemini · recommended 1×
  5. ChemOS · recommended 1×
  • CATEGORY QUERY
    What tools can automate scientific experimentation and hypothesis generation for research tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. GPT-3.5 Turbo
    3. Anthropic Claude
    4. Google Gemini
    5. ChemOS
    6. Antha
    7. Labguru
    8. Benchling
    9. DataRobot
    10. H2O.ai
    11. AlphaFold (deepmind/alphafold)
    12. Jupyter Notebooks (jupyter/notebook)
    13. NumPy (numpy/numpy)
    14. SciPy (scipy/scipy)
    15. Pandas (pandas-dev/pandas)
    16. Scikit-learn (scikit-learn/scikit-learn)
    17. TensorFlow (tensorflow/tensorflow)
    18. PyTorch (pytorch/pytorch)

    AI recommended 18 alternatives but never named RUC-NLPIR/Arbor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build an AI agent that iteratively refines code and learns from experimental outcomes?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. LlamaIndex
    4. Docker
    5. Kubernetes
    6. Jupyter Notebooks
    7. VS Code
    8. MLflow
    9. Weights & Biases
    10. PyTorch
    11. TensorFlow
    12. GitHub
    13. GitLab

    AI recommended 13 alternatives but never named RUC-NLPIR/Arbor. 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 RUC-NLPIR/Arbor?
    pass
    AI named RUC-NLPIR/Arbor explicitly

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

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

Subscribe to Pro for deep diagnoses

RUC-NLPIR/Arbor — 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