asynctor

v0.12.9 suspicious
6.0
Medium Risk

Async functions to compare with anyio and asyncio, and toolkit to read excel with async/await.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to the high shell risk from using subprocess.run with shell=True, which can introduce significant vulnerabilities. However, the lack of obfuscation, credential risk, and the package's low activity level prevent it from being classified as outright malicious.

  • High shell risk due to usage of subprocess.run with shell=True
  • Low activity and limited author history
Per-check LLM notes
  • Network: The use of async httpx client is common for making asynchronous HTTP requests and doesn't inherently suggest malicious activity.
  • Shell: The use of subprocess.run with shell=True can be risky as it allows execution of arbitrary commands which could lead to code injection and other vulnerabilities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has low activity and an author with limited history, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3908 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 102 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in waketzheng/asynctor
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • r.app) async with httpx.AsyncClient( timeout=timeout, transport=transport, base_
  • =base_url) async with httpx.AsyncClient(timeout=timeout, **kwargs) as c: yield c class
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • lex.split(cmd) return subprocess.run(command, **kw) # nosec @classmethod def run_and_ec
  • nd, list): return subprocess.run(self._command, **kwargs) # nosec return self.run_by
  • d): kw["shell"] = shell = True command = cmd if shell else shlex.split(cmd)
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 asynctor 
Create a mini-application named 'AsyncExcelComparer' using the 'asynctor' package that allows users to asynchronously compare multiple Excel files. This application should serve as a powerful tool for data analysts and researchers who need to quickly identify differences between large datasets stored in Excel files.

The application should have the following core functionalities:
1. **User Interface**: Develop a simple yet intuitive command-line interface where users can input the paths of the Excel files they wish to compare.
2. **Asynchronous File Reading**: Utilize the 'asynctor' package's asynchronous capabilities to read multiple Excel files concurrently. Ensure that the application can handle large Excel files efficiently without blocking the main thread.
3. **Data Comparison**: Implement a feature to compare data across specified sheets and columns in the provided Excel files. Users should be able to select which sheets and columns they want to compare.
4. **Difference Reporting**: After comparing the data, generate a report detailing the differences found between the files. This report should be presented in a user-friendly format, either on the command line or saved as a new Excel file.
5. **Error Handling**: Incorporate robust error handling to manage scenarios such as file not found errors, invalid input from users, and issues related to reading Excel files.
6. **Customization Options**: Allow users to customize certain aspects of the comparison process, such as ignoring certain rows/columns during comparison or setting a threshold for identifying significant differences.

To achieve these functionalities, you will extensively use the 'asynctor' package's async/await toolkit for Excel reading and its comparison functions. Additionally, consider integrating other Python libraries like pandas for data manipulation and openpyxl for working with Excel files if necessary.

This project aims to demonstrate the power of asynchronous programming in handling I/O-bound tasks efficiently, specifically in the context of data analysis and Excel file management.

💬 Discussion Feed

Leave a comment

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