RRepoGEO

REPOGEO REPORT · LITE

openai/supervised-reptile

Default branch master · commit 8f2b71c6 · scanned 6/24/2026, 8:13:13 PM

GitHub: 1,039 stars · 209 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)

3 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 openai/supervised-reptile, 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 comprehensive topics to improve categorization

    Why:

    CURRENT
    ["paper"]
    COPY-PASTE FIX
    ["meta-learning", "few-shot-learning", "reptile-algorithm", "deep-learning", "machine-learning", "python", "omniglot", "mini-imagenet", "research-code", "paper"]
  • highreadme#2
    Reposition the README's opening sentence to explicitly state purpose

    Why:

    CURRENT
    Reptile training code for Omniglot and Mini-ImageNet.
    COPY-PASTE FIX
    This repository contains the official implementation of the Supervised Reptile meta-learning algorithm, designed for efficient model initialization and few-shot learning on datasets like Omniglot and Mini-ImageNet.
  • mediumabout#3
    Expand the repository description with key terms

    Why:

    CURRENT
    Code for the paper "On First-Order Meta-Learning Algorithms"
    COPY-PASTE FIX
    Official implementation of the Supervised Reptile meta-learning algorithm for efficient model initialization and few-shot learning, as described in the paper "On First-Order Meta-Learning Algorithms".

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 openai/supervised-reptile
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
learn2learn
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. learn2learn · recommended 1×
  2. Meta-Learning Framework (MLF) · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. JAX · recommended 1×
  • CATEGORY QUERY
    How to implement meta-learning algorithms for efficient model initialization?
    you: not recommended
    AI recommended (in order):
    1. learn2learn
    2. Meta-Learning Framework (MLF)
    3. PyTorch
    4. TensorFlow
    5. JAX
    6. PyTorch Lightning
    7. Keras

    AI recommended 7 alternatives but never named openai/supervised-reptile. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for few-shot learning on image classification tasks.
    you: not recommended
    AI recommended (in order):
    1. Meta-Dataset
    2. Learn2Learn (L2L)
    3. Mammoth (Meta-learning with Multiple Optimization Techniques)
    4. PyTorch-MetaL
    5. TensorFlow Meta-Learning (TF-MetaL)
    6. Open-MMLab's MMFewShot

    AI recommended 6 alternatives but never named openai/supervised-reptile. 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 openai/supervised-reptile?
    pass
    AI named openai/supervised-reptile explicitly

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

  • If a team adopts openai/supervised-reptile in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/supervised-reptile 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 openai/supervised-reptile solve, and who is the primary audience?
    pass
    AI named openai/supervised-reptile explicitly

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

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openai/supervised-reptile — 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