🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network risk and concerns over low maintainer activity and incomplete metadata, suggesting potential risks that warrant closer scrutiny.

moderate network risk
low maintainer activity
incomplete metadata

Per-check LLM notes

Network: Network calls to external services suggest the package may be reporting usage or fetching updates, but further investigation is needed to confirm legitimacy.
Shell: No shell execution patterns detected, indicating low risk of immediate system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintainer activity and lacks essential metadata, raising concerns about its legitimacy and potential risks.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

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

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1391 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

123 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)

_iso, intent=intent) with httpx.Client(timeout=30.0) as client: r = client.post(
pi/v1/usage/current" with httpx.Client(timeout=30.0) as client: r = client.get(

✓ 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

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 agentskeptic

Create a mini-application named 'TruthVerifier' that leverages the 'agentskeptic' Python package to verify information真实性校验器微型应用程序，利用Python包'agentskeptic'来验证信息。请逐步说明该应用程序的功能、建议功能列表以及如何使用'agentskeptic'包。

### Project Overview
TruthVerifier is a command-line tool designed to help users verify the accuracy of information retrieved from CrewAI and LangGraph databases using the AgentSkeptic kernel. This tool aims to provide quick and reliable truth verification for any data query.

### Key Features
- **Query Input:** Users can input their queries via the command line interface.
- **Database Connection:** The application should establish a connection to both CrewAI and LangGraph databases.
- **Verification Process:** Utilize the AgentSkeptic kernel to process and verify the truthfulness of the queried data.
- **Output Display:** Display the verified results in a user-friendly format, indicating whether the information is true, false, or uncertain.
- **Error Handling:** Implement robust error handling to manage issues such as database connectivity failures and invalid inputs.
- **Logging Mechanism:** Log all activities including queries, verification processes, and outcomes for auditing purposes.

### Utilization of 'agentskeptic'
- **Initialization:** Start by importing the 'agentskeptic' package and initializing the necessary components required for connecting to the CrewAI and LangGraph databases.
- **Data Retrieval:** Use the package's functionalities to fetch data based on user inputs.
- **Verification Execution:** Execute the verification process using the AgentSkeptic kernel, which evaluates the retrieved data against the database truth.
- **Result Presentation:** Format and present the verification results clearly to the user, highlighting any discrepancies or uncertainties.
- **Enhanced Functionality:** Consider adding advanced features like historical data comparison, probabilistic assessments, or integration with other verification tools.

### Development Steps
1. Set up your development environment with Python and install the 'agentskeptic' package.
2. Design the command-line interface for user interaction.
3. Implement the database connection logic.
4. Integrate the verification process using 'agentskeptic'.
5. Develop the output display mechanism.
6. Incorporate error handling and logging functionalities.
7. Test the application thoroughly to ensure reliability and efficiency.
8. Document the code and provide usage instructions.

### Conclusion
By developing TruthVerifier, you will create a powerful tool for verifying information against trusted sources, enhancing transparency and reliability in data-driven decisions.