REPOGEO REPORT · LITE

arthurhenrique/cookiecutter-fastapi

Default branch main · commit 6418a161 · scanned 6/1/2026, 4:51:52 PM

GitHub: 709 stars · 76 forks

AI VISIBILITY SCORE

22 /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

1 / 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 arthurhenrique/cookiecutter-fastapi, 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 README H1 and opening paragraph to highlight unique features

Why:

CURRENT

# cookiecutter-fastapi

In order to create a template to FastAPI projects. :rocket:

COPY-PASTE FIX

# cookiecutter-fastapi: Production-Ready FastAPI Template with ML, CI/CD, and Modern Python Tooling

Quickly scaffold new FastAPI projects with this opinionated Cookiecutter template, pre-configured for Machine Learning applications, `uv` for blazing-fast dependency management, GitHub Actions for robust CI/CD, and Pytest for comprehensive testing.

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/arthurhenrique/cookiecutter-fastapi/
```

mediumreadme#3

Add a 'Features' or 'What's Included' section to the README

Why:

COPY-PASTE FIX

## Features

This template provides a robust foundation for your FastAPI projects, including:

*   **Machine Learning Integration:** Ready for ML models, data pipelines, and MLOps practices.
*   **Modern Python Tooling:** Utilizes `uv` for ultra-fast dependency management and `black` for code formatting.
*   **Robust CI/CD:** Pre-configured GitHub Actions for automated testing and deployment.
*   **Comprehensive Testing:** Pytest setup for unit and integration tests.
*   **Docker Support:** Containerization ready for easy deployment.
*   **Opinionated Structure:** Clean architecture for scalable and maintainable applications.

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 arthurhenrique/cookiecutter-fastapi

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

FastAPI-MLExample

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

FastAPI-MLExample · recommended 1×
Cookiecutter Data Science · recommended 1×
FastAPI Project Generator · recommended 1×
MLOps-Basics · recommended 1×
Custom Cookiecutter Template · recommended 1×

CATEGORY QUERY
How to quickly scaffold a new Python FastAPI project with machine learning integrations?
you: not recommended
AI recommended (in order):
1. FastAPI-MLExample
2. Cookiecutter Data Science
3. FastAPI Project Generator
4. MLOps-Basics
5. Custom Cookiecutter Template
AI recommended 5 alternatives but never named arthurhenrique/cookiecutter-fastapi. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a project template to jumpstart FastAPI development with CI/CD and modern dependency management.
you: not recommended
AI recommended (in order):
1. fastapi-template (s-czerwinski/fastapi-template)
2. fastapi-boilerplate (tiangolo/fastapi-boilerplate)
3. fastapi-cookiecutter (pydanny/fastapi-cookiecutter)
4. fastapi-realworld-example-app (uvicorn/fastapi-realworld-example-app)
5. fastapi-mvc (fastapi-mvc/fastapi-mvc)
AI recommended 5 alternatives but never named arthurhenrique/cookiecutter-fastapi. 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 arthurhenrique/cookiecutter-fastapi?
pass
AI did not name arthurhenrique/cookiecutter-fastapi — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts arthurhenrique/cookiecutter-fastapi in production, what risks or prerequisites should they evaluate first?
pass
AI named arthurhenrique/cookiecutter-fastapi 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 arthurhenrique/cookiecutter-fastapi solve, and who is the primary audience?
pass
AI did not name arthurhenrique/cookiecutter-fastapi — likely talking about a different project
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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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite