AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of direct execution threats but has a higher metadata risk due to limited contributor activity and community engagement.
- Low risk in network, shell, obfuscation, and credential aspects
- High metadata risk due to minimal commits and lack of community interaction
Per-check LLM notes
- Network: No network calls detected, which is normal for a query builder package that does not require external services.
- Shell: No shell execution patterns detected, indicating no risk of executing arbitrary commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The repository's lack of community engagement and the single, minimal-commit contributor suggest potential risk.
Package Quality Overall: Low (4.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3053 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: TypedType checker (mypy / pyright / pytype) referenced in project3 type-annotated function signatures (partial)
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 2 commits in adalekin/approck-sqlalchemy-query-builderSingle 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
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 7.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksVery few commits: 2 totalSingle contributor with only 2 commit(s) — possibly throwaway account
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 approck-sqlalchemy-query-builder
Create a simple web-based inventory management system using Flask as the backend framework and the 'approck-sqlalchemy-query-builder' package to handle complex database queries. The system should allow users to add, edit, delete, and search for items in the inventory. The search functionality should support advanced filtering based on multiple criteria, such as item name, category, price range, and stock availability. Step 1: Set up the Flask application and configure the database connection using SQLAlchemy. Step 2: Define models for the inventory items, including fields like id, name, description, price, category, and stock quantity. Step 3: Implement routes for adding new items, editing existing ones, and deleting items from the inventory. Step 4: Create a route for searching items in the inventory. This route should accept a JSON object containing nested filter rules. Use the 'approck-sqlalchemy-query-builder' package to convert these rules into SQLAlchemy query objects and execute them against the database. Step 5: Add validation to the search input using Pydantic schemas provided by the 'approck-sqlalchemy-query-builder' package to ensure that only valid and safe queries are executed. Suggested Features: - Support for sorting results based on different fields. - Pagination of search results to improve performance and usability. - Integration with a frontend framework like React or Vue.js for a more user-friendly interface. - Logging of all actions performed on the inventory for auditing purposes. The 'approck-sqlalchemy-query-builder' package will be used to construct complex SQLAlchemy queries from the nested JSON filter rules provided by the user. This allows for flexible and powerful search capabilities without exposing the database to unsafe inputs.
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