auto_survey

v0.2.5 safe
4.0
Medium Risk

Automated literature surveys.

🤖 AI Analysis

Final verdict: SAFE

The package auto_survey v0.2.5 has minimal risks associated with it, primarily due to its use of network calls and shell commands which, while potentially risky, appear to serve legitimate purposes related to its functionality.

  • Low obfuscation and credential risks.
  • Moderate network and shell execution risks but within expected operational parameters.
Per-check LLM notes
  • Network: The network calls seem to be fetching academic papers and PDFs, which is likely related to the package's functionality.
  • Shell: Executing shell commands like installing dependencies and checking software versions could be legitimate but might also indicate potential for misuse if not properly controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low risk, but lacks author details and classifiers indicating low effort or new maintainer.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5255 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in saattrupdan/auto-survey
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ts := 10): response = httpx.get( url="https://api.semanticscholar.org/graph/v1/p
  • ) } response = httpx.get( url=pdf_url, headers=headers, follow_redirects=True
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • pandoc_installed = ( subprocess.run( ["pandoc", "--version"], capture_output=True, t
  • syprint_installed = ( subprocess.run( ["weasyprint", "--version"], capture_output=Tru
  • pt=--quiet") try: subprocess.run(pandoc_command, input=markdown, encoding="utf-8", check=True
  • # Install newest project subprocess.run(["make", "install"]) # Add to version control subpr
  • # Add to version control subprocess.run(["git", "add", ".pre-commit-config.yaml"]) subprocess.ru
  • pre-commit-config.yaml"]) subprocess.run(["git", "add", "CHANGELOG.md"]) subprocess.run(["git", "
✓ 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 saattrupdan/auto-survey appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with auto_survey 
Create a Python-based mini-application called 'LiteratureExplorer' that leverages the 'auto_survey' package to automate the process of conducting literature surveys. This application should enable researchers to input specific keywords or topics, and it will automatically generate a comprehensive survey of relevant academic papers, articles, and books. The goal is to streamline the initial phase of research by providing a quick overview of existing work in a given field.

Step-by-Step Instructions:
1. Start by setting up the project environment. Ensure 'auto_survey' is installed using pip.
2. Design a user-friendly interface where users can enter their search terms.
3. Implement a feature within 'LiteratureExplorer' that uses 'auto_survey' to scrape and compile data from various academic databases such as Google Scholar, PubMed, and JSTOR based on the user's input.
4. Develop a summarization module that processes the collected data to highlight key findings, methodologies, and conclusions from each source.
5. Integrate a visualization component that presents the summarized data in an interactive format, allowing users to explore different aspects of the survey results.
6. Finally, implement a recommendation system that suggests potential areas for further research based on gaps identified in the surveyed literature.

Suggested Features:
- User authentication to save personal searches and access history.
- Customizable filters to refine search results (e.g., publication date range).
- Collaboration tools enabling multiple users to contribute to and share surveys.
- Export options to download the survey results in common file formats like PDF or CSV.

Utilizing 'auto_survey':
- Use 'auto_survey' to automate the collection and initial processing of literature sources. It should handle tasks such as web scraping, indexing, and basic content analysis.
- Leverage any additional functionalities provided by 'auto_survey', such as natural language processing capabilities, to enhance the summarization and recommendation features of your application.

💬 Discussion Feed

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