🤖 AI Analysis

Final verdict: SAFE

The package shows some signs of potential network misuse but lacks evidence of malicious intent across other critical areas such as shell execution, obfuscation, and credential harvesting. The metadata suggests it's from a potentially new or less active developer.

moderate network risk due to custom requests session
low activity or new account indicated by metadata

Per-check LLM notes

Network: The use of a requests session with custom headers might be legitimate, but could also indicate attempts at network-based attacks or data exfiltration.
Shell: No shell execution patterns were detected, indicating low risk of direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository is not found and the maintainer has only one package, which may indicate low activity or a new account.

📦 Package Quality Overall: Low (3.2/10)

○ 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 (1825 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

10 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)

losed self._session = requests.Session() self._session.headers.update({ "Conten

✓ 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: antarraksha.ai

✓ 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 2.0

1 maintainer concern(s) found

Author "Akash Kumar Dey" 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 antarraksha-crewai

Create a space mission management tool using the 'antarraksha-crewai' package, which provides an AI agent enforcement SDK specifically designed for Crewai. This tool will help manage various aspects of a space mission, including crew scheduling, resource allocation, and mission critical decision support. Here’s a detailed plan on how to proceed:

1. **Setup**: Begin by installing the 'antarraksha-crewai' package and setting up your development environment. Ensure you have Python installed and create a virtual environment for your project.
2. **Mission Planning Module**: Develop a module that allows users to input details about their space mission such as start date, duration, objectives, and crew members. Use the 'antarraksha-crewai' package to generate optimized schedules for crew activities based on mission requirements and crew capabilities.
3. **Resource Allocation Module**: Create another module that handles the allocation of resources like fuel, food, and equipment. This module should use the AI capabilities provided by 'antarraksha-crewai' to predict potential shortages and suggest adjustments to ensure mission success.
4. **Decision Support System**: Implement a decision support system that can provide real-time advice during the mission. This could include recommendations on when to perform certain tasks, how to handle unexpected situations, and optimal use of available resources. Leverage the AI agents from 'antarraksha-crewai' to analyze data streams and make informed decisions.
5. **User Interface**: Design a user-friendly interface where mission planners can interact with the system. This could be a web-based application or a desktop app depending on your preference. Ensure that the UI integrates seamlessly with the backend functionalities developed earlier.
6. **Testing & Validation**: Rigorously test each module to ensure it works as expected. Validate the effectiveness of the AI-driven suggestions against known scenarios or simulations.
7. **Deployment**: Once everything is tested and validated, deploy your application. Consider hosting it on a cloud service if it’s a web app or distributing it via app stores if it’s a mobile/desktop application.

Suggested Features:
- Real-time updates on mission status
- Automated alerts for critical events
- Historical data analysis for future missions
- Integration with external APIs for additional data sources
- Customizable settings for different types of missions

Utilize the 'antarraksha-crewai' package throughout the project to harness its AI capabilities for optimizing mission planning and execution.