🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some unusual behaviors that require closer scrutiny, particularly concerning its use of subprocess.run and low repository activity.

shell risk due to use of subprocess.run
low metadata activity suggesting potential unreliability

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: The use of subprocess.run suggests shell execution capability, which could be legitimate but may also indicate potential for executing arbitrary commands, warranting further investigation.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
Metadata: Low activity indicators suggest potential unreliability, but no clear malicious signs.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

166 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 64 commits in thebesteric/agile-py
Single author but highly active (64 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

command) result = subprocess.run(args, capture_output=True, text=True, timeout=timeout)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hotmail.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 2.0

1 maintainer concern(s) found

Author "Eric" 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 agile

Create a fully functional mini-application that leverages the 'agile' Python package to streamline common data processing tasks. This application, named 'DataMender', will serve as a versatile tool for cleaning and transforming datasets. The goal is to provide users with a simple interface to upload CSV files, perform basic data cleaning operations, and export cleaned data back into CSV format.

Step 1: Define the core functionalities of 'DataMender'. These include:
- Uploading a CSV file via a user-friendly interface
- Cleaning data by handling missing values, removing duplicates, and correcting inconsistent formats
- Performing basic transformations such as sorting, filtering, and aggregating data
- Exporting the cleaned dataset back into a CSV file

Step 2: Utilize the 'agile' package to handle these tasks efficiently. For example, use 'agile' to simplify file handling, automate repetitive data cleaning steps, and provide robust error checking during data processing.

Step 3: Implement an intuitive command-line interface (CLI) for users to interact with 'DataMender'. Users should be able to specify actions like uploading a file, choosing cleaning options, and exporting the results through CLI commands.

Suggested Features:
- A progress bar to show the status of data processing
- Support for multiple input/output formats (not just CSV)
- Option to save settings and configurations for future use
- Error logs for troubleshooting issues encountered during data processing

How 'agile' Package is Utilized:
- Use 'agile' for efficient file operations and data manipulation functions
- Leverage 'agile' utilities to enhance performance and reliability of the data cleaning and transformation processes
- Employ 'agile' to implement error handling and logging mechanisms

Your task is to design and implement this application using Python, ensuring that it is modular, maintainable, and scalable. Document your code thoroughly and provide clear instructions on how to run and use 'DataMender'.