aisp-cli

v0.0.1 suspicious
4.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package aisp-cli v0.0.1 has minimal risks in terms of network, shell execution, obfuscation, and credential handling. However, the metadata risk score is moderately high, suggesting possible issues with maintenance and transparency.

  • Metadata risk score is 6 out of 10
  • Low version number suggests early or incomplete development
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious but should be assessed based on the package's intended functionality.
  • Shell: No shell execution detected, indicating the package does not appear to execute external commands directly.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and potential lack of transparency, raising concerns about its legitimacy.

📦 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
  • 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 aisp-cli 
Create a mini-application called 'AI Whisper' using the Python package 'aisp-cli'. This application will serve as a simple yet powerful tool for processing audio files and extracting meaningful insights from them. Here’s a detailed plan on how to develop this application:

1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python 3.x installed along with pip. Install the 'aisp-cli' package using pip.

2. **Feature Overview**:
   - **Audio File Upload**: Allow users to upload audio files of various formats (MP3, WAV, etc.).
   - **Transcription Service**: Use 'aisp-cli' to transcribe the uploaded audio files into text.
   - **Sentiment Analysis**: Apply sentiment analysis on the transcribed text to determine the overall mood or emotion expressed in the audio.
   - **Keyword Extraction**: Identify and highlight key phrases or words within the transcribed text.
   - **Visualization**: Display the results of the sentiment analysis and keyword extraction in a user-friendly manner.

3. **Implementation Steps**:
   - **Step 1**: Design a simple GUI interface using a library like Tkinter or PyQt5 where users can select and upload their audio files.
   - **Step 2**: Integrate 'aisp-cli' to handle the transcription process once an audio file is uploaded. Ensure error handling for cases where transcription fails.
   - **Step 3**: After transcription, use a sentiment analysis API or library to analyze the transcribed text. Display the sentiment score and corresponding label (positive, neutral, negative).
   - **Step 4**: Implement a feature to extract keywords from the transcribed text. This could be done using a keyword extraction tool or library.
   - **Step 5**: Develop a visualization component that graphically represents the sentiment analysis result and highlights the extracted keywords.

4. **Testing**: Thoroughly test the application with various types of audio files to ensure it handles different scenarios gracefully.

5. **Deployment**: Package the application as a standalone executable or deploy it online so others can easily access and use it.

By following these steps, you'll create a versatile tool that leverages the power of 'aisp-cli' to transform raw audio data into actionable insights.

💬 Discussion Feed

Leave a comment

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