SynopticPy

v2026.6.0 suspicious
4.0
Medium Risk

Retrieve mesonet weather data as Polars DataFrames from Synoptic's Weather API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to incomplete author information and potential network activity that could be exploited. However, there is no direct evidence of malicious behavior.

  • Incomplete author information
  • Potential network activity
Per-check LLM notes
  • Network: The network call patterns suggest the package is making HTTP GET requests to an endpoint and possibly fetching summary data, which could be legitimate depending on its intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The author's information is incomplete and the maintainer seems new or inactive, which raises some concerns but not enough to conclusively determine malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ow() self.response = requests.get(self.endpoint, params=params) self.url = self.respon
  • elf.token) response = requests.get(URL, params).json() response = response["SUMMARY"]["
✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository blaylockbk/SynopticPy appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 SynopticPy 
Create a weather monitoring mini-app using the Python package 'SynopticPy'. This app will allow users to retrieve and analyze real-time weather data from multiple mesonet stations across a specified region. Here are the steps and features you should include in your project:

1. **Project Setup**: Start by setting up a virtual environment and installing necessary packages including 'SynopticPy' and 'Pandas'.
2. **Data Retrieval**: Use 'SynopticPy' to fetch weather data such as temperature, humidity, wind speed, and precipitation from selected mesonet stations.
3. **Data Analysis**: Implement basic data analysis functions like calculating average temperatures over a given period, identifying stations with the highest humidity levels, and visualizing trends over time.
4. **User Interface**: Develop a simple command-line interface where users can input their desired location and date range to get tailored weather reports.
5. **Output Visualization**: Utilize libraries like Matplotlib or Seaborn to create graphs and charts displaying key weather metrics over time.
6. **Error Handling**: Ensure robust error handling to manage issues like invalid station IDs or missing data.
7. **Documentation**: Write comprehensive documentation detailing how to use the app, including setup instructions, usage examples, and explanation of each feature.

By following these guidelines, your project will not only showcase the capabilities of 'SynopticPy' but also provide a practical tool for anyone interested in monitoring regional weather conditions.