🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators across all checks, with only minor concerns about metadata and repository engagement.

Low network and shell risk
No obfuscation or credential harvesting patterns detected
Repository has low engagement and lacks author details

Per-check LLM notes

Network: No network calls detected, which is normal for a tool focused on audio processing and specification handling.
Shell: No shell execution patterns detected, consistent with an application designed for audio processing tasks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets and credentials.
Metadata: The repository's low engagement and the missing author details raise some concerns, but there are no clear signs of malicious intent.

🔬 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

Email domain looks legitimate: certusinnovations.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://semver.org/

⚠ 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 AudioMlSpecTools

Create a music genre classification application using Python and the 'AudioMlSpecTools' package. This application will allow users to upload an audio file, and it will classify the genre of the music based on its audio features. Here are the steps and features to implement:

1. **User Interface**: Develop a simple web interface where users can upload their audio files.
2. **Audio Feature Extraction**: Utilize 'AudioMlSpecTools' to extract relevant features from the uploaded audio file such as Mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features.
3. **Model Training**: Pre-train a machine learning model (such as Random Forest or SVM) using a dataset like GTZAN, which contains different genres of music. Use 'AudioMlSpecTools' to preprocess the training dataset by extracting similar features.
4. **Classification**: Once the user uploads an audio file, preprocess it using the same feature extraction techniques as the training data. Then, use the pre-trained model to predict the genre of the music.
5. **Results Display**: Display the predicted genre along with confidence scores for each genre.
6. **Optional Features**:
- Provide a gallery of popular songs categorized by genre for users to test the application.
- Allow users to save their classifications for future reference.
- Implement real-time genre detection for streaming audio inputs.

The 'AudioMlSpecTools' package simplifies the process of extracting these audio features, making it easier to focus on the machine learning aspect of the project. Ensure your code is well-documented and includes comments explaining how each part of the process works.