AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity or obfuscation. It does not engage in network calls, shell executions, or credential harvesting, which are typical indicators of malicious behavior.
- No network calls detected
- No shell execution detected
- No credential harvesting detected
- No obfuscation detected
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (10439 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:11434/v1
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 aifastdb
Create a real-time sentiment analysis dashboard using the Python package 'aifastdb'. This mini-application will allow users to input text snippets and receive immediate sentiment scores indicating whether the text is positive, negative, or neutral. Additionally, it will store historical sentiment data efficiently and enable querying of past sentiments based on various criteria such as date range, user ID, or sentiment score. Key Features: 1. User Interface: Develop a simple web interface where users can enter text snippets and see sentiment scores instantly. 2. Sentiment Analysis: Integrate an external API or model (e.g., TextBlob or VADER from NLTK) for sentiment scoring. 3. Data Storage & Retrieval: Use 'aifastdb' to store each text snippet along with its sentiment score and timestamp. Ensure the database can handle high-frequency writes and reads efficiently. 4. Historical Data Querying: Implement functionality to query stored sentiment data based on different parameters like date range, user ID, and sentiment score. 5. Visualization: Display recent sentiment trends over time using graphs or charts within the dashboard. Steps to Build the Application: 1. Set up your development environment with necessary Python packages including 'aifastdb', any chosen sentiment analysis library, and Flask for the web framework. 2. Initialize 'aifastdb' and create tables to store text snippets, their sentiment scores, and timestamps. 3. Write a function that takes user input, performs sentiment analysis, and stores the result in 'aifastdb'. 4. Design a Flask route to accept POST requests containing text snippets, call the sentiment analysis function, and return the sentiment score back to the client. 5. Create another set of routes to handle GET requests for retrieving historical sentiment data based on specified filters. 6. Use a frontend framework (such as React or Vue.js) to build the user interface, which includes input fields for text snippets and display areas for sentiment scores and historical data visualizations. 7. Test the application thoroughly to ensure all components work together seamlessly, paying special attention to performance when dealing with large volumes of data. 8. Deploy the application to a cloud service provider such as AWS or Heroku so it can be accessed publicly.
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