🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks as it does not engage in any network calls, shell executions, or obfuscation techniques. The primary concern is the low maintainer activity and metadata quality, which slightly elevates the risk score.

Low maintainer activity
Metadata quality concerns

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
Metadata: Low risk but shows signs of low maintainer activity and metadata quality.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
5 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://gh.seria.moe/ambr
Detailed PyPI description (1488 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

169 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

4 unique contributor(s) across 100 commits in seriaati/ambr
Small but multi-author team (3–4 contributors)

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository seriaati/ambr appears legitimate

⚠ 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 ambr-py

Create a fully-functional mini-application that allows users to explore character data from Genshin Impact using the 'ambr-py' Python package. This application will serve as both a user-friendly interface and a powerful tool for fans of the game to gather detailed information about characters.

Step-by-Step Instructions:
1. Setup: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment for this project. Install the 'ambr-py' package using pip.
2. Design: Sketch out the basic design of your application. Decide on the main functionalities you want to include and how the user will interact with them. Consider including options for searching by character name, filtering by weapon type, and viewing detailed stats.
3. Implementation: Start coding your application. Use the 'ambr-py' package to fetch character data from the Project Amber API. Implement search functionality allowing users to find characters by their names. Also, implement filters so users can narrow down their searches based on various attributes like weapon type, element, or rarity.
4. Enhancements: Add additional features to enrich the user experience. For example, include images of the characters, display animations or videos if available, and allow users to save their favorite characters to a personal list.
5. Testing: Thoroughly test your application to ensure it works as expected. Check for any errors or bugs and fix them accordingly.
6. Deployment: Once your application is fully functional, consider deploying it online so others can use it too. You could host it on a platform like Heroku or deploy it as a web app using Flask or Django.

Features to Include:
- Search by Character Name: Users should be able to enter a character's name and receive detailed information about that character.
- Filter by Weapon Type/Element/Rarity: Provide options for users to filter characters based on these attributes.
- Display Character Images: Show images of each character next to their information.
- Save Favorite Characters: Allow users to mark characters as favorites and view them later.
- View Detailed Stats: Display detailed statistics about each character, such as their base attack, HP, energy recharge, etc.

How 'ambr-py' is Utilized:
- Fetching Data: Use 'ambr-py' to asynchronously fetch data from the Project Amber API. This includes fetching character details, weapons, and other related information.
- Searching and Filtering: Leverage the package's capabilities to efficiently search and filter through large datasets of character information.
- Error Handling: Implement robust error handling to manage potential issues when interacting with the API, ensuring a smooth user experience.