RRepoGEO

REPOGEO REPORT · LITE

sheldonresearch/ProG

Default branch main · commit cc59eb9b · scanned 5/30/2026, 7:46:43 PM

GitHub: 584 stars · 74 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 sheldonresearch/ProG, 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
    Add a 'Why ProG-V2?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why ProG-V2?' after the introduction, explicitly stating its unique value proposition for graph prompting and benchmarking compared to general GNN libraries. For example: 'While general GNN libraries like PyTorch Geometric and DGL provide foundational graph operations, ProG-V2 is purpose-built as a unified Python library and reproducible benchmark specifically for graph prompt learning. It offers a modular architecture for prompt strategies, comprehensive coverage, and standardized benchmarking utilities that are not found in general-purpose GNN frameworks.'
  • mediumabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., project documentation, official website, or the GitHub repo itself) to the 'homepage' field in the repository's About section.
  • lowreadme#3
    Enhance README with a quick start or examples section

    Why:

    COPY-PASTE FIX
    Add a 'Quick Start' or 'Getting Started' section immediately after the 'What's New' or 'Architecture' section, demonstrating a minimal working example of implementing a graph prompt learning workflow. This could include a simple code snippet for defining a prompt strategy and running a benchmark.

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 sheldonresearch/ProG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Geometric (PyG)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 2×
  2. Deep Graph Library (DGL) · recommended 2×
  3. Spektral · recommended 2×
  4. Graph Neural Network Library (GNN-Lib) · recommended 1×
  5. NetworkX · recommended 1×
  • CATEGORY QUERY
    What Python libraries are available for implementing graph prompt learning workflows?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNN-Lib)
    5. NetworkX

    AI recommended 5 alternatives but never named sheldonresearch/ProG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust framework for benchmarking different graph neural network prompting strategies.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. Deep Graph Library (DGL)
    3. Spektral
    4. Graph Neural Network Library (GNNA)
    5. GraphGym

    AI recommended 5 alternatives but never named sheldonresearch/ProG. 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 sheldonresearch/ProG?
    pass
    AI named sheldonresearch/ProG explicitly

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

  • If a team adopts sheldonresearch/ProG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sheldonresearch/ProG 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 sheldonresearch/ProG solve, and who is the primary audience?
    pass
    AI named sheldonresearch/ProG 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)
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sheldonresearch/ProG — 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