aquascope

v0.5.0 suspicious
5.0
Medium Risk

Open-source water data aggregation toolkit with AI-powered research methodology recommendations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its network and shell execution capabilities, which could potentially be exploited for unauthorized actions.

  • moderate network risk due to HTTP requests
  • high shell risk from executing subprocesses
Per-check LLM notes
  • Network: The package makes HTTP requests to external URLs, which could be for legitimate purposes like API calls or updates, but requires further investigation to confirm.
  • Shell: Executing subprocesses can be risky if not properly controlled, especially when running external scripts like 'streamlit'. This might indicate the package is intended to run as a service or application, but could also be used for unauthorized actions.
  • Obfuscation: The obfuscation patterns appear to be related to the use of pandas for datetime conversion, which is common in data processing tasks and not inherently malicious.
  • Credentials: No credential harvesting patterns were detected in the provided code snippets.
  • Metadata: The author has only one package, which might indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://rekin226.github.io/aquascope/
  • Detailed PyPI description (13098 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

  • 281 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 28 commits in Rekin226/aquascope
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • }, } resp = httpx.post(chat_url, json=payload, timeout=timeout) resp.raise_for_
  • erts] try: resp = httpx.post(url, json=payload, timeout=15.0) resp.raise_for_stat
  • try: resp = httpx.get(url, params=params, timeout=60, follow_redirects=True)
  • (body) try: with smtplib.SMTP(config.smtp_host, config.smtp_port, timeout=15) as smtp:
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • data["datetime"] = __import__("pandas").to_datetime(data["datetime"]) data = data.set_i
  • data["sample_datetime"] = __import__("pandas").to_datetime(data["sample_datetime"]) data = dat
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • file__).parent / "app.py" subprocess.run( [sys.executable, "-m", "streamlit", "run", str(app_
✓ 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

Repository Rekin226/aquascope appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AquaScope Contributors" 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 aquascope 
Create a web-based mini-application called 'WaterInsight' using the Python package 'aquascope'. This application aims to provide researchers and environmental scientists with a powerful tool to aggregate water quality data from various sources, analyze it, and recommend appropriate research methodologies based on AI-driven insights. Here’s a step-by-step guide on how to develop this application:

1. **Setup**: Begin by setting up your development environment. Ensure you have Python installed along with necessary packages such as Flask for the backend and any frontend framework like React or Vue.js. Install 'aquascope' via pip.
2. **Data Aggregation Module**: Develop a module within WaterInsight that leverages 'aquascope' to collect water quality data from multiple sources including databases, APIs, and direct file uploads. This data should cover parameters like pH, dissolved oxygen, turbidity, etc., across different locations and times.
3. **Data Analysis Interface**: Using 'aquascope', create an intuitive interface where users can visualize the aggregated data through charts and graphs. Implement filters to allow users to refine their view based on specific parameters, locations, and time periods.
4. **AI-Driven Methodology Recommendations**: Integrate 'aquascope's AI capabilities to provide personalized research methodology suggestions based on the analyzed data. These recommendations could include statistical analysis methods, predictive modeling techniques, and other relevant approaches.
5. **User Authentication & Management**: Incorporate user authentication so that registered users can save their data sets, access previous analyses, and receive tailored recommendations.
6. **Documentation & Support**: Finally, ensure comprehensive documentation is available for both users and developers. Include FAQs, tutorials, and a support forum.

Suggested Features:
- Real-time data updates from connected sources
- Comparative analysis tools allowing side-by-side comparison of different datasets
- Customizable alert systems for significant changes in water quality parameters
- Export options for visualizations and analysis results

By following these steps, you'll develop a robust and user-friendly application that not only aggregates but also provides deep insights into water quality data, empowering users to make informed decisions.

💬 Discussion Feed

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