atmospore

v0.1.0 suspicious
4.0
Medium Risk

Async Python client for the Atmospore pollen forecast API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious intent but has some concerning factors such as low repository activity and a single contributor, which raises suspicion.

  • Low repository activity and single contributor
  • Newly released version
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession suggests the package is making network requests, which could be for legitimate purposes like API calls or fetching data.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's low activity and single contributor suggest potential risk, especially as it's a newly released package.

📦 Package Quality Overall: Medium (5.0/10)

✦ 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_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://atmospore.com/api-docs
  • Detailed PyPI description (2765 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 2 commits in atmospore/atmospore-python
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • : self._session = aiohttp.ClientSession( timeout=aiohttp.ClientTimeout(total=self.ti
✓ 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 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Atmospore" 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 atmospore 
Create a pollen forecast mini-app using the 'atmospore' Python package. Your app should allow users to input their location (city name or zip code) and receive a detailed pollen forecast for the next few days. Here’s how your application should work:

1. **User Interface**: Develop a simple command-line interface (CLI) where users can enter their location.
2. **Location Input**: Accept user input for location details. This could be either a city name or a zip/postal code.
3. **API Call**: Use the 'atmospore' package to make an asynchronous call to the Atmospore API with the provided location information.
4. **Data Retrieval**: Retrieve the pollen forecast data from the API response. Ensure you handle any errors gracefully, such as invalid locations or API failures.
5. **Forecast Display**: Present the pollen forecast data in an easily readable format. Include details like date, type of pollen, and severity level (low, moderate, high).
6. **Additional Features**:
   - Allow users to specify a date range for the forecast.
   - Provide options to filter pollen types (e.g., tree pollen, grass pollen, weed pollen).
   - Offer an option to save the forecast to a file for later reference.
7. **Testing**: Write unit tests to ensure the functionality of your CLI tool, especially around error handling and data parsing.
8. **Documentation**: Prepare a README.md file explaining how to install and use the tool, including examples.

Utilize the 'atmospore' package effectively by exploring its async capabilities to ensure fast and efficient data retrieval from the Atmospore API. Make sure to include proper dependency management and follow best practices for Python development.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!