AI Analysis
Final verdict: SAFE
The package exhibits minimal risks across all categories with only slight concerns about obfuscation and metadata quality. There are no indications of malicious activities or supply-chain attacks.
- Low obfuscation risk related to logging timestamps
- No detected network, shell execution, or credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
- Obfuscation: The obfuscation pattern seems to be used for logging the timestamp of a cached Globus summary, which is likely not malicious.
- Credentials: No credential harvesting patterns detected.
- Metadata: The package shows some low-effort indicators but lacks clear red flags.
Package Quality Overall: Low (3.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (544 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
28 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 2.0
Found 1 obfuscation pattern(s)
": data, "timestamp": __import__("time").time(), } logger.info("Cached Globus summary: %s",
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: osu.edu>
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 shortAuthor "" 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 aidrin
Develop a Python-based data readiness assessment tool named 'DataPrepAssist' using the 'aidrin' package. This tool will help users understand if their datasets are suitable for AI applications by evaluating various aspects of data quality and readiness. Here’s a detailed outline of the project requirements: 1. **Project Setup**: Initialize a new Python environment and install necessary packages including 'aidrin'. Ensure your setup includes Flask for web serving and SQLAlchemy for database interactions. 2. **Data Input Module**: Design a module that allows users to upload CSV files directly through a web interface. The application should validate file types and handle common errors gracefully. 3. **Data Evaluation Engine**: Utilize the 'aidrin' package to perform comprehensive data readiness assessments. This includes checking for completeness, consistency, accuracy, and relevance. Each check should provide a score and a brief explanation of findings. 4. **Visualization Dashboard**: Create a dashboard that visualizes the assessment results. Include charts and graphs that summarize key metrics such as missing values, outlier detection, and data distribution. 5. **Recommendation System**: Based on the assessment results, generate actionable recommendations for improving data quality. These could include suggestions like imputing missing values, removing outliers, or transforming non-numeric data into numeric formats. 6. **Database Integration**: Store assessment results and recommendations in a SQLite database for future reference and comparison. Implement functionality to retrieve past assessments and compare them with the current one. 7. **User Interface Enhancements**: Improve user experience by adding features like real-time progress indicators during data evaluation and a clean, intuitive design for all interfaces. 8. **Testing & Documentation**: Write unit tests to ensure each component functions correctly and document the code thoroughly, explaining how to use the application and the significance of each feature provided by 'aidrin'. This project aims to create a versatile tool that not only evaluates data but also empowers users with insights and practical steps to enhance their datasets for AI projects.