analp

v1.0.0 suspicious
4.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risk factors but raises suspicion due to its newness and lack of detailed maintainer information.

  • Metadata risk score of 5/10
  • Package is new with limited maintainer details
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or system compromise.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The package is new and lacks detailed maintainer information, raising some suspicion.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_imports.py)
○ 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
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in anaticulae/iamraw
  • Active community — 5 or more distinct 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: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository anaticulae/iamraw appears legitimate

⚠ Maintainer History score 6.0

3 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)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with analp 
Create a Python-based mini-application named 'AnalyzeMyText' that leverages the 'analp' package to provide users with comprehensive analysis of their input text. This application should allow users to copy-paste any piece of text into a simple GUI or command-line interface, and then receive detailed insights about the text's structure, sentiment, and key themes. Here are the core functionalities that your application should support:

1. **Text Input**: Provide a straightforward method for users to input their text, whether through a GUI text box or command-line argument.
2. **Basic Analysis**: Use 'analp' to perform basic text analysis such as word count, sentence count, and character count.
3. **Sentiment Analysis**: Utilize 'analp' to determine the overall sentiment of the text - positive, negative, or neutral.
4. **Keyword Extraction**: Identify and highlight important keywords and phrases within the text using 'analp'.
5. **Theme Detection**: Analyze the text to detect major themes or topics discussed within it.
6. **Output Presentation**: Display the results in a user-friendly format, either through the console or a simple GUI window.
7. **Error Handling**: Ensure that the application gracefully handles errors such as empty input or unsupported text formats.
8. **Customization Options**: Allow users to customize certain aspects of the analysis, like specifying language or adjusting sensitivity levels for sentiment analysis.

For each feature, describe how you would integrate 'analp' to achieve the desired functionality, focusing on code snippets where necessary.

💬 Discussion Feed

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