🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual characteristics such as a newly created repository with limited maintainer history and no associated git repository, suggesting potential risks. However, there are no immediate signs of malicious activities like shell execution or credential harvesting.

Metadata risk due to new package creation with limited maintainer history and no git repository.
No direct evidence of malicious intent or actions.

Per-check LLM notes

Network: The observed network call patterns suggest the package is using HTTP headers to authenticate API calls, which is common for legitimate packages that interact with external services.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being newly created with limited maintainer history and no git repository, raising suspicion.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (3372 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

56 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

er_id self._session = requests.Session() # ── Internal helpers ───────────────────────────────
k_url self._session = requests.Session() self._session.headers.update({ "Author
nt base URL session = requests.Session() session.headers.update({"Authorization": f"Bearer
ile URL.""" session = requests.Session() session.headers.update({"Authorization": f"Bearer
ile URL.""" session = requests.Session() session.headers.update({ "Authorizatio
model self._session = requests.Session() self._session.headers.update({ "Author

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 ai-influencer

Create a social media marketing tool called 'InfluencerGenie' using the Python package 'ai-influencer'. This tool will allow users to create, manage, and interact with AI-powered Instagram influencers programmatically. Here’s a step-by-step guide on how to build this tool:

1. **Project Setup**: Initialize your project with the necessary dependencies including 'ai-influencer', requests, and any other required libraries.
2. **User Interface**: Design a simple command-line interface (CLI) for interacting with 'InfluencerGenie'. Users should be able to perform actions such as creating new influencers, updating their profiles, scheduling posts, and analyzing performance metrics.
3. **Creating Influencers**: Implement functionality to generate new AI influencers through the 'ai-influencer' package. Users should be able to specify basic details like name, profile picture, and bio.
4. **Managing Influencers**: Allow users to update influencer details, including changing profile pictures, bios, and posting schedules. Use 'ai-influencer' to manage these updates efficiently.
5. **Scheduling Posts**: Enable users to schedule posts for their influencers. These posts should include text, images, and hashtags. Utilize 'ai-influencer' to handle the posting process.
6. **Analyzing Performance**: Integrate analytics to track the performance of the influencers. Metrics could include engagement rates, follower growth, and post reach. Display these metrics clearly in the CLI.
7. **Advanced Features**: Consider adding advanced features such as automated hashtag generation, sentiment analysis on comments, and A/B testing for different types of content.
8. **Documentation and Testing**: Ensure your project includes comprehensive documentation and thorough testing to validate its functionality.

By following these steps, you'll create a powerful yet user-friendly tool for managing AI-driven Instagram influencers.