apc-model-parser

v0.2.2 suspicious
4.0
Medium Risk

Convert process-model definitions to and from a canonical intermediate representation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks associated with network calls, shell execution, and obfuscation, but its metadata quality is poor, and there is little activity, which raises concerns about potential supply-chain attacks.

  • Low activity and poor metadata quality indicate possible issues with the package's legitimacy.
  • No significant risks detected in terms of network calls, shell execution, or obfuscation.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

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

Some documentation present

  • Detailed PyPI description (5010 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

  • 54 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 8 commits in Advanced-Process-Control/model-parser
  • Single author with few commits — possibly a personal or throwaway project

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Advanced Process Control" 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 apc-model-parser 
Create a process modeling tool using the 'apc-model-parser' package in Python. This tool will allow users to define process models using a simple text-based syntax and convert them into a standardized format. Additionally, it should support importing existing models in the standardized format and converting them back into the text-based syntax for editing.

Step 1: Define the Text-Based Syntax
- Develop a simple, yet powerful syntax for defining process models. The syntax should allow for the definition of processes, activities, transitions between activities, and conditions for those transitions.

Step 2: Implement Parsing and Serialization
- Use the 'apc-model-parser' package to parse the user-defined models into a canonical intermediate representation (IR). Ensure that the IR supports all the elements defined in the text-based syntax.
- Also, implement functionality to serialize the IR back into the text-based syntax, allowing for easy modification and review of the models.

Step 3: Create a User Interface
- Develop a basic command-line interface (CLI) that allows users to input their process models in the text-based syntax and view the parsed output in the IR format.
- Include options for saving and loading models, as well as exporting and importing models in the standardized format.

Suggested Features:
- Support for conditional branching within processes.
- Ability to visualize the parsed IR in a graphical format.
- Integration with a version control system like Git for tracking changes in models over time.
- Exporting models into different formats such as JSON or XML for interoperability with other systems.

How to Utilize 'apc-model-parser':
- Use 'apc-model-parser' to handle the conversion between the text-based syntax and the canonical IR. This includes parsing the text input into the IR and serializing the IR back into text when needed.
- Leverage the package's capabilities to ensure that the IR accurately represents the complexities of the process models, including nested structures and conditional logic.

💬 Discussion Feed

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