🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risk factors such as no network calls, shell executions, obfuscations, or credential harvesting. However, the metadata risk score is elevated due to the package being new and lacking additional context, which raises some suspicion.

Metadata risk score is 5/10
No description provided

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution patterns detected, indicating the package does not execute system commands, reducing the risk of malicious activities.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with low engagement and no additional context provided, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (1.2/10)

○ 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

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 2 day(s) ago
Author "Ahmet" 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 ahmet-msa-221201033

Create a bioinformatics tool using Python that helps researchers analyze multiple sequence alignments (MSA) of protein sequences. This tool will leverage the 'ahmet-msa-221201033' package, which implements the MUSCLE algorithm optimized for educational purposes. Your task is to develop a command-line interface (CLI) application that allows users to input a set of protein sequences and receive an aligned output along with various statistical analyses.

Steps to Build the Application:
1. Install the 'ahmet-msa-221201033' package and any necessary dependencies.
2. Design the CLI interface that accepts user inputs, such as file paths containing sequences or direct sequence inputs.
3. Implement functionality to process the input sequences through the MUSCLE algorithm provided by the package.
4. Develop features to display the aligned sequences in a readable format.
5. Add statistical analysis capabilities, such as calculating sequence identity percentages between pairs of sequences.
6. Ensure the application outputs the results to both console and optionally to a file.
7. Include error handling for invalid inputs and exceptions raised by the 'ahmet-msa-221201033' package.
8. Write unit tests to validate the correctness of your implementation.

Suggested Features:
- Ability to handle FASTA formatted files.
- Option to visualize the alignment graphically if possible.
- Provide an option to save the alignment result in different formats like CSV or HTML.
- Implement logging to track the application's execution.
- Allow users to specify parameters for the MUSCLE algorithm for customization.

How to Utilize 'ahmet-msa-221201033':
- Import the necessary functions from the package to perform the multiple sequence alignment.
- Use the package's capabilities to align sequences and retrieve the alignment results.
- Integrate the package's functionalities into your application's workflow to provide the desired outputs and analyses.