AI Analysis
The package shows some signs of potential misuse due to missing metadata and an unverified origin, but lacks concrete indicators of malicious intent.
- Missing author information
- Non-existent git repository
Per-check LLM notes
- Network: The use of httpx.Client suggests the package makes network calls, which could be legitimate depending on its functionality.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating a low risk of secret theft.
- Metadata: The package has a missing author and a non-existent git repository, raising suspicion but without clear evidence of malice.
Package Quality Overall: Low (4.4/10)
Test suite present — 4 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml4 test file(s) detected (e.g. conftest.py)
Some documentation present
Detailed PyPI description (7711 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
67 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Found 1 network call pattern(s)
rue, exist_ok=True) with httpx.Client(headers={"User-Agent": USER_AGENT}, timeout=30.0) as client:
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a web-based application called 'Atatürk Archive Explorer' that allows users to explore and analyze the speeches, statements, telegrams, and proclamations of Mustafa Kemal Atatürk from 1906 to 1938. This application will utilize the 'ataturk-mcp' Python package, which provides access to the corpus of Atatürk's works via a RESTful API. ### Features: 1. **Search Functionality**: Implement a search bar where users can enter keywords to find specific documents related to Atatürk's work. The application should support filtering results based on date ranges. 2. **Document Viewer**: Display the full text of selected documents with options to highlight search terms and view metadata such as title, date, and source. 3. **Timeline Visualization**: Create a timeline visualization showing the distribution of Atatürk's works over time. Users should be able to click on different points in the timeline to load corresponding documents. 4. **Sentiment Analysis**: Integrate a sentiment analysis feature to gauge the overall tone of Atatürk's works. Display the sentiment score alongside each document. 5. **Export Options**: Allow users to download the full text of any document as a PDF or plain text file. ### Steps to Build the Application: 1. **Setup Environment**: Install necessary packages including Flask for the backend, and Bootstrap for the frontend. Ensure 'ataturk-mcp' is installed and accessible through its API endpoints. 2. **API Integration**: Use 'ataturk-mcp' to fetch data about Atatürk's works. Write functions to query the API based on user input and return relevant documents. 3. **Frontend Development**: Design an intuitive interface using HTML/CSS/JavaScript. Incorporate Flask templates to render dynamic content from backend queries. 4. **Implement Search Functionality**: Develop a search function that accepts user input and returns matching documents. Enhance this with filters for narrowing down results. 5. **Build Document Viewer**: Create a section for displaying full documents with features like highlighting search terms and viewing metadata. 6. **Create Timeline Visualization**: Use JavaScript libraries like D3.js to generate an interactive timeline. Ensure clicking on timeline markers loads corresponding documents. 7. **Add Sentiment Analysis**: Utilize a Python library like TextBlob to perform sentiment analysis on each document. Display these scores in the document viewer. 8. **Enable Export Options**: Implement functionality for downloading documents as PDFs or plain text files. Use libraries like WeasyPrint for PDF generation. 9. **Testing and Deployment**: Thoroughly test all features for accuracy and usability. Deploy the application using services like Heroku or AWS. By following these steps and incorporating the specified features, you'll create a valuable tool for historians, researchers, and Atatürk enthusiasts alike.
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