AI Analysis
Final verdict: SUSPICIOUS
The package has minor risks associated with network usage and metadata, but lacks critical metadata like author details and a GitHub repository. This raises suspicion without clear evidence of malicious intent.
- Missing author details
- Lack of a GitHub repository
Per-check LLM notes
- Network: The use of PUT requests to an S3 URL is likely for legitimate data storage purposes, but should be verified against known good behavior.
- Shell: No shell execution patterns detected, which is normal and expected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
- Metadata: The package shows some red flags such as missing author details and a lack of a GitHub repository, but there's no clear evidence of malicious intent.
Package Quality Overall: Medium (5.2/10)
✦ High
Test Suite
9.0
Test suite present — 29 test file(s) found
Test runner config found: conftest.py29 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://workers.arkindex.orgBrief PyPI description (535 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
84 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
score 1.5
Found 1 network call pattern(s)
s content: resp = requests.put( s3_put_url, data=content,
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: teklia.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
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 arkindex-base-worker
Create a mini-application that leverages the 'arkindex-base-worker' Python package to streamline the creation of machine learning workflows for text analysis. This application will serve as a prototype for more complex ML projects and should include the following functionalities: 1. **Data Ingestion**: The app should allow users to upload text files (e.g., .txt, .csv). It should support batch processing of multiple files at once. 2. **Preprocessing**: Implement basic text preprocessing steps such as tokenization, removing stop words, and lemmatization using NLP techniques. Use the 'arkindex-base-worker' package to define these preprocessing steps as workers in your workflow. 3. **Model Training**: Integrate a simple machine learning model (such as a Naive Bayes classifier or a Logistic Regression model) trained on the preprocessed text data. This model will classify the text into predefined categories (e.g., positive/negative sentiment). 4. **Evaluation & Visualization**: After training, evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score. Visualize these metrics using a bar chart or pie chart to provide a clear overview of the model's effectiveness. 5. **Deployment**: Utilize the 'arkindex-base-worker' package to create a deployment pipeline that allows the trained model to be easily updated and retrained as new data becomes available. The application should be designed with a user-friendly interface, allowing users to interactively select their data sources, view preprocessing steps, monitor training progress, and see the evaluation results. Additionally, document each step of your development process, explaining how you utilized the 'arkindex-base-worker' package to achieve your goals.
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