amads

v1.2.0 suspicious
6.0
Medium Risk

A toolkit for symbolic music analysis

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and unsafe shell execution, raising concerns about its legitimacy and safety.

  • Unusual import patterns indicating possible obfuscation
  • Shell execution without proper validation
Per-check LLM notes
  • Network: The network call is used to download files which could be legitimate if the source is trusted.
  • Shell: Shell execution without proper validation or sanitization poses a high risk for potential exploitation or unintended behavior.
  • Obfuscation: The code shows unusual import patterns which may indicate obfuscation to hide the actual dependencies or functionality.
  • Credentials: No clear evidence of credential harvesting is present, but further review of how imported functions are used would be advisable.
  • Metadata: The maintainer has only one package, suggesting a new or less active account which could be suspicious.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation website" -> https://music-computing.github.io/amads/
  • Detailed PyPI description (3427 chars)
○ 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

  • 247 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in music-computing/amads
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ) as tmp_file: urllib.request.urlretrieve(filename, tmp_file.name) filename =
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • tall """ try: __import__(package_name) except ImportError: raise ImportError(
  • rred_reader] module = __import__(module_name, fromlist=[func_name]) return getattr(module, func_name), preferred_reader
  • rred_writer] module = __import__(module_name, fromlist=[func_name]) # note that the type signature of func_name has an
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • if musescore_exe: subprocess.Popen([musescore_exe, xml_path]) return if sy
  • ]: result = subprocess.run( ["open", "-a", app_name, xml_path],
  • return subprocess.Popen(["open", xml_path]) return raise Runtim
  • form == "darwin": subprocess.Popen(["open", html_path]) elif sys.platform == "win32":
  • th) else: subprocess.Popen(["xdg-open", html_path]) import warnings from math import i
  • ), str(ly_path)] result = subprocess.run(command, capture_output=True, text=True) if result.retur
✓ 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

Repository music-computing/amads appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Roger Dannenberg, Mark Gotham" 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 amads 
Create a Python-based mini-application called 'MelodicExplorer' that leverages the 'amads' package to perform advanced symbolic music analysis. This application will serve as a tool for musicians and composers to explore and understand the structure of their musical pieces. Here’s a step-by-step guide on what the application should do and how it should utilize the 'amads' package:

1. **User Interface**: Design a simple yet intuitive command-line interface where users can input a MIDI file or a MusicXML file.
2. **File Parsing**: Use the 'amads' package to parse the input file into a format suitable for analysis. This involves reading the musical score and converting it into a structured representation.
3. **Music Analysis**:
   - **Chord Progression Identification**: Analyze the chord progressions within the piece using the 'amads' toolkit's chord recognition capabilities.
   - **Melody Extraction**: Identify the melody line(s) from the input score.
   - **Harmonic Analysis**: Perform harmonic analysis to understand the harmonic structure of the piece.
4. **Visualization**: Implement basic visualization tools to display the results of the analysis. For example, create graphs showing the progression of chords over time and visual representations of the melody lines.
5. **Output**: Provide the user with a detailed report of the analysis, including the identified chords, melody, and harmonic structure. Additionally, allow the option to export these analyses into a readable format such as a PDF or a CSV file.
6. **Enhancements**:
   - **User-Friendly Interface**: Consider developing a graphical user interface (GUI) using libraries like Tkinter or PyQt to make the application more accessible.
   - **Integration with Other Tools**: Allow integration with other music analysis software or platforms.
7. **Utilization of 'amads' Package**: Throughout the development process, focus on utilizing the 'amads' package's functions for parsing, analyzing, and manipulating musical scores symbolically. Ensure that the application demonstrates the full potential of the 'amads' toolkit by incorporating its most advanced features for symbolic music analysis.

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