🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across network, shell, obfuscation, and credential aspects, indicating a low likelihood of malicious behavior.

Low network risk due to expected HTTP calls
No signs of shell execution, obfuscation, or credential mishandling

Per-check LLM notes

Network: The network call pattern indicates the package is likely making HTTP requests to an API, which is common for packages that provide AI or e-commerce functionalities.
Shell: No shell execution patterns were detected, suggesting there is no immediate risk of command execution from this package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/your-org/ai-ecom-skills#readme
Detailed PyPI description (2185 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

90 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 1.5

Found 1 network call pattern(s)

) self._client = httpx.AsyncClient( base_url=cfg.base_url, timeout=time

✓ 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: example.com>

✓ 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 4.0

2 maintainer concern(s) found

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-ecom-skills

Create a mini-application called 'Qianchuan Ad Optimizer' using the Python package 'ai-ecom-skills'. This tool will help advertisers optimize their campaigns on the Qianchuan platform by providing insights and suggestions based on historical performance data. The application should have the following features:

1. **Campaign Analysis**: Users can input their campaign ID(s) to get detailed analysis of past ad performance including metrics like click-through rate (CTR), conversion rate, and cost per acquisition (CPA).
2. **Audience Segmentation**: Automatically segment audiences based on behavior patterns identified through machine learning algorithms provided by 'ai-ecom-skills'.
3. **Budget Allocation Tool**: Suggest optimal budget allocations across different audience segments and times of day to maximize ROI.
4. **Ad Creative Recommendations**: Provide recommendations on which types of ad creatives (e.g., video, image) perform best for different audience segments.
5. **Real-Time Alerts**: Set up real-time alerts for significant changes in key performance indicators (KPIs) so users can react quickly.

The application should use the 'ai-ecom-skills' package to leverage its capabilities in analyzing e-commerce data and generating actionable insights for Qianchuan advertising operations. Additionally, ensure the app is user-friendly and provides visualizations for easier understanding of complex data.