🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk based on the analysis notes. While the necessity of network access should be clarified, there are no indications of malicious activities such as shell execution, obfuscation, or credential harvesting.

Low network risk due to common usage of httpx
No evidence of shell execution, obfuscation, or credential harvesting

Per-check LLM notes

Network: The use of httpx for network calls is common and not inherently suspicious; however, the absence of clear documentation on why network access is necessary warrants further investigation.
Shell: No shell execution patterns were detected, indicating a low risk of direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation for malicious purposes.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

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

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (5727 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

226 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 score 3.0

Found 2 network call pattern(s)

wargs) self._client = httpx.Client(base_url=self._base_url, timeout=self._timeout) def clo
wargs) self._client = httpx.AsyncClient(base_url=self._base_url, timeout=self._timeout) async d

✓ 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: agentref.dev>

✓ 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 agentref

Create a Python-based mini-app that functions as a simple dashboard for managing affiliate marketing campaigns using the AgentRef Affiliate API. This app will allow users to view performance metrics, manage their campaigns, and track earnings in real-time. Here are the steps and features you need to implement:

1. **Setup**: Begin by setting up a virtual environment and installing the 'agentref' package via pip.
2. **Authentication**: Implement a secure way for users to authenticate with their AgentRef credentials.
3. **Dashboard Layout**: Design a clean and user-friendly dashboard layout using a library like Streamlit or Dash.
4. **Campaign Management**: Enable users to create, edit, delete, and view details of their affiliate campaigns directly from the dashboard.
5. **Performance Metrics**: Display key performance indicators such as clicks, conversions, and earnings per campaign.
6. **Real-Time Tracking**: Integrate real-time tracking capabilities to update performance metrics dynamically.
7. **Notifications**: Set up email notifications for significant events like reaching a milestone in earnings or when a campaign ends.
8. **Analytics Reports**: Provide options to generate custom reports on campaign performance.

The 'agentref' package will be used extensively throughout the project to interact with the AgentRef API. It will handle authentication, fetching data for campaigns and performance metrics, and managing campaigns. Ensure your implementation leverages the full potential of the 'agentref' package by exploring its documentation and utilizing its advanced features.