autoform

v0.3.0 suspicious
6.0
Medium Risk

Composable function transformations for text-space programs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation and low maintainer activity, raising concerns about its true purpose and potential risks.

  • High obfuscation risk (7/10) suggests attempts to obscure functionality.
  • Low maintainer activity and missing metadata suggest potential poor quality or malicious intent.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: The code appears to be obfuscated with unusual patterns that may hinder readability and could indicate an attempt to hide functionality.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The package shows low maintainer activity and lacks important metadata, indicating potential low quality or malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • Test runner config found: pyproject.toml
  • 23 test file(s) detected (e.g. test_analysis.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7467 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

  • 353 type-annotated function signatures detected in source
○ 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 score 4.0

Found 2 obfuscation pattern(s)

  • et(): return self.eval(prim, in_tree, **params) return self.stage(prim, in_
  • , in_tree, **params) def eval(self, prim: Prim, in_tree: Tree, /, **params) -> Tree:
✓ 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 6.0

3 maintainer concern(s) found

  • 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 autoform 
Create a Python-based text transformation utility called 'TextMorpher' using the 'autoform' package. This utility will allow users to apply various transformations to input text, such as changing case, reversing strings, adding prefixes/suffixes, and more. The app should have a user-friendly command-line interface where users can select from a menu of available transformations or chain multiple transformations together in a single operation. Here’s how you can structure your project:

1. **Setup**: Install necessary packages including 'autoform'. Ensure your environment supports Python 3.8 or higher.
2. **Core Functionality**: Define functions for each type of transformation using 'autoform', ensuring they are composable and can be easily chained together. For example, create functions for uppercase conversion, lowercase conversion, string reversal, and prefix/suffix addition.
3. **User Interface**: Develop a CLI that lists all available transformations and allows users to choose which ones to apply. The CLI should also support chaining transformations by allowing users to specify the order of operations.
4. **Input/Output Handling**: Implement robust input/output handling to manage user inputs and display transformed outputs clearly. Include error handling for invalid inputs or commands.
5. **Testing**: Write unit tests for each transformation function to ensure they work as expected under different scenarios. Use 'autoform'’s capabilities to test the chaining of transformations.
6. **Documentation**: Provide clear documentation on how to use 'TextMorpher', including examples of common transformation chains. Document how 'autoform' is used within the application to transform text.
7. **Enhancements**: Consider optional enhancements like saving transformation settings to a file, loading previously saved settings, or integrating with other text processing libraries.

Your goal is to showcase the power and flexibility of 'autoform' while building a practical, useful tool for anyone who needs to manipulate text efficiently.

💬 Discussion Feed

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