🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no indications of malicious activities. However, it has low maintenance and poor metadata quality.

No network or shell risks detected
Low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external API interactions.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: The use of base64 decoding for PDF byte manipulation is likely legitimate, not malicious obfuscation.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The package shows signs of low maintenance and poor metadata quality, but there are no direct indicators of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_client.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3542 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

150 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)

4 ... pdf_bytes = base64.b64decode(result.pdf) ... with open('output.pdf', 'wb') as

✓ 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 short
Author "" 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 answerrocket-client

Create a data analysis dashboard using the 'answerrocket-client' Python package. This application will allow users to input their dataset and perform various analyses and visualizations without needing deep knowledge of statistical methods or programming skills. The goal is to make complex data insights accessible and easy to understand for non-technical users.

Steps:
1. Set up your development environment with Python and install the 'answerrocket-client' package.
2. Design a simple user interface where users can upload their CSV files.
3. Implement functionality within your application to use 'answerrocket-client' to analyze the uploaded datasets.
4. Display the results of the analysis in an interactive dashboard format, including charts, tables, and key metrics.
5. Add features like saving the analysis results, exporting them as PDFs or images, and sharing via email or social media.
6. Ensure the application handles errors gracefully, providing informative messages when something goes wrong.
7. Test the application thoroughly with different types of datasets to ensure it works reliably under various conditions.
8. Document your code and provide instructions on how to set up and run the application.

Suggested Features:
- Real-time data visualization updates as the user interacts with the dashboard.
- Option to choose from different pre-built templates for common types of analyses (e.g., sales trends, customer segmentation).
- Ability to drill down into specific subsets of data for more detailed analysis.
- Integration with popular cloud storage services for easy data import/export.
- User authentication and permission management to control who can access which datasets.

How 'answerrocket-client' is Utilized:
- Use the package to connect to AnswerRocket's API and send the uploaded dataset for analysis.
- Leverage the API's capabilities to automatically generate insights, such as trends, correlations, and predictive models.
- Retrieve the analysis results from the API and format them appropriately for display in your dashboard.
- Handle any errors returned by the API gracefully, providing clear feedback to the user.

By completing this project, you'll gain experience working with external APIs, building user-friendly interfaces, and turning raw data into actionable insights.