alphatims

v1.1.1 suspicious
4.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of a proper author name.

  • Low risk in network, shell execution, obfuscation, and credential harvesting.
  • Elevated metadata risk due to maintainer's account status.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

○ 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" -> https://alphatims.readthedocs.io/en/latest/
  • Detailed PyPI description (33333 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in MannLabs/alphatims
  • Small but multi-author team (3–4 contributors)

🔬 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: alphapept.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository MannLabs/alphatims appears legitimate

⚠ 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 alphatims 
Develop a small application called 'TIMS Explorer' that leverages the 'alphatims' Python package to facilitate the exploration and analysis of data from Thermo Scientific™ Tandem Mass Tag (TMT) proteomics experiments. The application should allow users to load TMT experiment files, visualize the data, and perform basic statistical analyses. Here are the steps and features to consider:

1. **Setup Environment**: Ensure you have Python installed, then install the 'alphatims' package via pip.
2. **Data Loading**: Implement functionality to load TMT experiment files using 'alphatims'. Display a summary of the loaded data including sample names, protein IDs, and quantification values.
3. **Data Visualization**: Create visualizations such as heatmaps or scatter plots using libraries like Matplotlib or Seaborn to show the distribution of quantification values across different samples.
4. **Statistical Analysis**: Integrate basic statistical tools to compare quantification values between groups of samples. This could include t-tests or ANOVA tests, and display the results.
5. **User Interface**: Develop a simple GUI using Tkinter or PyQt to make the application more user-friendly. The GUI should allow users to select files, view summaries, and generate visualizations and statistics directly from the interface.
6. **Saving Results**: Allow users to save the visualizations and statistical outputs as images or text files.

The 'alphatims' package will be primarily used for loading and processing TMT experiment data. Make sure to document how each feature utilizes the package's capabilities.

💬 Discussion Feed

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