AI Analysis
Final verdict: SAFE
The package has minimal risks with no network calls, shell executions, obfuscations, or credential harvesting attempts. However, low maintainer activity and lack of detailed author information suggest caution.
- Low maintainer activity
- Lack of detailed author information
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell executions detected, indicating the package does not perform system command operations.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low maintainer activity and lacks detailed author information, indicating potential low effort or new/inactive account.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 3 test file(s) found
Test runner config found: pyproject.toml3 test file(s) detected (e.g. test_expand.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1605 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
16 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
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
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 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 astra-augment
Create a conversational AI training tool named 'ConvoBoost' using the Python package 'astra-augment'. This tool aims to enhance the quality and quantity of training data for conversational AI models through conversation truncation techniques. Here’s a detailed plan on how to build ConvoBoost: 1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'astra-augment', 'pandas', and 'numpy'. Ensure you have access to a dataset of conversations, ideally in CSV format. 2. **Data Loading**: Develop a function to load your conversation dataset into a pandas DataFrame. This dataset should contain columns for 'conversation_id', 'message_id', and 'text'. 3. **Conversation Truncation**: Implement functionality that leverages 'astra-augment' to truncate conversations. The goal is to create shorter versions of conversations that still retain meaningful context. Experiment with different truncation lengths and strategies. 4. **Augmentation Strategy**: Design an augmentation strategy where each original conversation generates multiple truncated versions. For example, if the original conversation has 5 messages, generate versions with 1 message, 2 messages, and so forth. 5. **Data Storage**: Create a feature to store these augmented conversations back into a new DataFrame or directly into a new CSV file. Include metadata such as the original conversation ID and the truncation length applied. 6. **Evaluation Module**: Add an evaluation module to assess the quality of the augmented conversations. Use metrics like BLEU score or human evaluation to measure how well the truncated conversations maintain the context of the original. 7. **User Interface**: Optionally, develop a simple command-line interface (CLI) or a web-based UI using Flask/Django to allow users to upload their own datasets and view the augmented results. 8. **Documentation & Testing**: Write comprehensive documentation explaining how to use ConvoBoost, including setup instructions, usage examples, and expected outputs. Conduct thorough testing to ensure reliability and accuracy of the data augmentation process. By following these steps, you will create a powerful tool for enhancing conversational AI training datasets, making it easier to improve model performance through better data preparation.
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