atlas-gmp-engine

v1.0.0 safe
3.0
Low Risk

Bayesian inference engine for geographic place guessing

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, or obfuscation. However, it is new and has minimal engagement, raising some concerns about the maintainer's activity level.

  • Low risk of malicious activity
  • New package with minimal engagement
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution by the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The package is new with minimal engagement, and the maintainer seems to be inactive or new.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_engine.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://guessmyplace.vercel.app/docs
  • Detailed PyPI description (14071 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 56 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 16 commits in GuessMyPlace/atlas-gmp-engine
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with atlas-gmp-engine 
Create a geographical guessing game using the 'atlas-gmp-engine' Python package. This mini-application will allow users to guess the location of a randomly selected city based on clues provided by the application. The core functionality of the app involves Bayesian inference to refine guesses based on user inputs. Here’s a detailed plan on how to develop this application:

1. **Project Setup**: Begin by setting up a new Python environment. Install the 'atlas-gmp-engine' package along with any other necessary dependencies like numpy, pandas, and matplotlib.
2. **Data Preparation**: Collect a dataset of cities around the world, including their coordinates, population, climate type, and continent. This data will serve as the basis for the Bayesian model.
3. **Model Building**: Use 'atlas-gmp-engine' to build a Bayesian model that takes into account various attributes of the cities to predict the likelihood of a correct guess. The model should be able to update its predictions based on user input.
4. **Application Development**: Develop a simple command-line interface (CLI) where users can start a game session. The game should randomly select a city from the dataset and provide initial clues about the city's attributes.
5. **User Interaction**: Allow users to make guesses about the city’s name, and after each guess, the application should provide feedback and update the Bayesian model with the new information. The feedback could include hints such as “Your guess is too far north” or “The population is larger than you guessed.”
6. **Game Logic**: Implement a scoring system where users earn points for correct guesses and lose points for incorrect ones. The game ends when the user correctly identifies the city or runs out of attempts.
7. **Visualization**: Optionally, use matplotlib to create visualizations that show the probability distribution of the guessed city over time, giving users a graphical representation of how their guesses affect the Bayesian model.
8. **Testing & Refinement**: Test the application thoroughly to ensure it works as expected and refine the model and user experience based on testing feedback.
9. **Documentation**: Write clear documentation for both end-users and developers, explaining how to play the game and how the Bayesian model works under the hood.

This project not only provides entertainment but also demonstrates the practical application of Bayesian inference in a real-world scenario.

💬 Discussion Feed

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