apcore-toolkit

v0.8.0 safe
4.0
Medium Risk

Shared scanner, schema extraction, and output toolkit for apcore framework adapters

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across various categories except for metadata, which shows some concerns regarding maintainer activity and metadata quality. However, there is no concrete evidence of malicious intent or supply-chain attack.

  • Low network, shell, obfuscation, and credential risks.
  • Metadata quality and maintainer activity show some concerns.
Per-check LLM notes
  • Network: The network patterns indicate legitimate HTTP requests, which could be part of the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 25 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/aiperceivable/apcore-toolkit-python#readm
  • Detailed PyPI description (12944 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

  • 377 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 77 commits in aiperceivable/apcore-toolkit-python
  • Single author but highly active (77 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ncode("utf-8") req = urllib.request.Request( url, data=payload,
  • try: with urllib.request.urlopen(req, timeout=self.timeout) as resp:
  • e=False) with patch("urllib.request.urlopen", return_value=mock_resp): result = enha
  • ib.error with patch("urllib.request.urlopen", side_effect=urllib.error.URLError("Connection refu
  • Error.""" with patch("urllib.request.urlopen", side_effect=TimeoutError("timed out")):
  • e=False) with patch("urllib.request.urlopen", return_value=mock_resp): with pytest.r
✓ 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: gmail.com>

✓ 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 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 apcore-toolkit 
Create a fully-functional mini-application named 'SchemaExtractor' using the Python package 'apcore-toolkit'. This application should serve as a versatile tool for developers and data analysts who need to extract and manage schemas from various data sources. The primary goal of SchemaExtractor is to simplify the process of understanding and manipulating data structures across different databases and file formats.

**Application Requirements:**
1. **Data Source Support:** The application should support at least three types of data sources: MySQL, CSV files, and JSON files.
2. **Schema Extraction:** Utilize the 'apcore-toolkit' to scan each data source and extract its schema. The schema should include details such as table names, column names, data types, and relationships.
3. **Output Formats:** Provide options for exporting the extracted schema in multiple formats including JSON, YAML, and XML.
4. **User Interface:** Develop a simple command-line interface (CLI) that allows users to specify the type of data source, input location, and desired output format.
5. **Error Handling:** Implement robust error handling to manage issues such as connection failures, invalid inputs, and unsupported file formats.
6. **Documentation:** Include comprehensive documentation on how to install and use the application, as well as examples of typical usage scenarios.

**Utilizing 'apcore-toolkit':** 
- Use the 'apcore-toolkit' package to handle the scanning and schema extraction processes efficiently. Ensure that you leverage its capabilities to streamline the interaction with different data sources and formats.
- For each supported data source, demonstrate how the 'apcore-toolkit' can be configured and utilized within your application to achieve the desired functionality.
- Explore advanced features of 'apcore-toolkit', such as schema comparison and transformation, and integrate them into SchemaExtractor where applicable.

This project aims to showcase the versatility and power of 'apcore-toolkit' while providing a valuable tool for managing data schemas.

💬 Discussion Feed

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