AI Analysis
Final verdict: SUSPICIOUS
The package exhibits a moderate risk level due to potential code obfuscation and a new or inactive maintainer's PyPI account. These factors suggest a need for further investigation before considering it safe.
- Potential code obfuscation
- Maintainer has a new or inactive PyPI account
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating the package does not execute system commands.
- Obfuscation: The observed pattern suggests potential obfuscation but could also be a normal use of encoding for data handling.
- Credentials: No clear signs of credential harvesting detected.
- Metadata: The maintainer has a new or inactive PyPI account with only one package, which could be a minor red flag.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 10 test file(s) found
10 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (5260 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
59 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)
t_str or "" decoded = base64.b64decode(ssh_command_output).decode("utf-8") logging.info(
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 2.0
1 maintainer concern(s) found
Author "Allen Institute for Neural Dynamics" 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 aind-airflow-jobs
Your task is to develop a mini-application that automates data processing workflows using the 'aind-airflow-jobs' Python package. This package offers a suite of global classes specifically designed for integrating with the AIND Airflow service, which streamlines the creation and management of complex data pipelines. Your application will serve as a proof-of-concept for leveraging these tools to enhance productivity and efficiency in data-driven projects. **Project Scope:** - **Workflow Automation:** Create a simple data processing pipeline that includes tasks such as data ingestion from a CSV file, preprocessing steps like cleaning and normalization, and finally, storing the processed data into a database. - **Scheduling Tasks:** Use the capabilities of 'aind-airflow-jobs' to schedule these tasks at regular intervals (e.g., daily). - **Monitoring and Logging:** Implement basic monitoring and logging mechanisms to track the status of each task and any errors encountered during execution. **Features to Include:** 1. **Data Ingestion:** Design a function that reads data from a CSV file located in an S3 bucket. Utilize 'aind-airflow-jobs' to manage this process efficiently. 2. **Data Preprocessing:** Develop a set of preprocessing functions that clean and normalize the ingested data. These could include handling missing values, removing duplicates, and converting data types. 3. **Database Storage:** After preprocessing, write the cleaned data into a PostgreSQL database. Ensure that your solution handles large datasets efficiently. 4. **Task Scheduling:** Schedule the entire workflow to run automatically every day at midnight. Use 'aind-airflow-jobs' to configure and manage these schedules. 5. **Error Handling and Logging:** Integrate error handling to capture and log any issues that occur during the execution of the workflow. Logs should be stored in a centralized location for easy access and review. **How to Utilize 'aind-airflow-jobs':** - Import necessary classes and modules from 'aind-airflow-jobs' to define and manage your data processing tasks. - Leverage its built-in functionalities for scheduling and monitoring tasks to ensure seamless integration with your workflow. - Customize configurations within 'aind-airflow-jobs' to fit specific requirements of your data processing pipeline. Your goal is to create a robust, scalable, and maintainable application that demonstrates the power of 'aind-airflow-jobs' in simplifying complex data processing tasks.
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