REPOGEO REPORT · LITE

grok-ai/nn-template

Default branch main · commit 8ba02bba · scanned 6/7/2026, 6:41:49 AM

GitHub: 651 stars · 66 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 grok-ai/nn-template, 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 highlight MLOps integration

Why:

CURRENT

Generic template to bootstrap your PyTorch project, read more in the documentation.

COPY-PASTE FIX

NN Template is a comprehensive PyTorch project boilerplate that integrates MLOps best practices for reproducibility, experiment tracking (W&B), data versioning (DVC), and configuration management (Hydra), enabling researchers and engineers to quickly start deep learning projects.

mediumtopics#2

Refine topics to emphasize 'template' and 'MLOps integration'

Why:

CURRENT

best-practices, best-practises, cookiecutter, deep-learning, dvc, github-actions, huggingface-datasets, hydra, mkdocs, pre-commit, project-structure, pytorch, pytorch-lightning, reproducibility, research, streamlit, template, wandb, weights-and-biases

COPY-PASTE FIX

best-practices, cookiecutter, data-versioning, deep-learning, deep-learning-template, dvc, experiment-tracking, github-actions, huggingface-datasets, hydra, mkdocs, mlops-template, pre-commit, project-structure, pytorch, pytorch-lightning, pytorch-template, reproducibility, research, streamlit, template, wandb, weights-and-biases

lowabout#3

Expand the 'About' description to detail MLOps integration

Why:

CURRENT

Generic template to bootstrap your PyTorch project.

COPY-PASTE FIX

A comprehensive PyTorch project template integrating MLOps best practices for reproducibility, experiment tracking (W&B), data versioning (DVC), and configuration management (Hydra).

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 grok-ai/nn-template

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Lightning-AI/lightning

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Lightning-AI/lightning · recommended 1×
mlflow/mlflow · recommended 1×
facebookresearch/hydra · recommended 1×
iterative/dvc · recommended 1×
wandb/wandb · recommended 1×

CATEGORY QUERY
How to quickly start a new deep learning project with PyTorch and good MLOps practices?
you: not recommended
AI recommended (in order):
1. PyTorch Lightning (Lightning-AI/lightning)
2. MLflow (mlflow/mlflow)
3. Hydra (facebookresearch/hydra)
4. DVC (iterative/dvc)
5. Weights & Biases (wandb/wandb)
6. Optuna (optuna/optuna)
7. Docker
AI recommended 7 alternatives but never named grok-ai/nn-template. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a robust project template to ensure reproducibility in deep learning research.
you: not recommended
AI recommended (in order):
1. Cookiecutter Data Science
2. PyTorch Lightning Project Template
3. MLflow Project Template
4. Deep Learning Project Template by @drivendata
5. Kedro
6. Hydra
AI recommended 6 alternatives but never named grok-ai/nn-template. 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 grok-ai/nn-template?
pass
AI named grok-ai/nn-template explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts grok-ai/nn-template in production, what risks or prerequisites should they evaluate first?
pass
AI named grok-ai/nn-template 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 grok-ai/nn-template solve, and who is the primary audience?
pass
AI named grok-ai/nn-template 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 grok-ai/nn-template. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/grok-ai/nn-template"><img src="https://repogeo.com/badge/grok-ai/nn-template.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite