🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple red flags including potential obfuscation techniques, network risks, and questionable metadata. While there's no concrete evidence of malicious activity, the combination of these factors raises significant suspicion.

Base64 obfuscation
Network request risks
Suspicious maintainer history
Non-existent Git repository

Per-check LLM notes

Network: The use of httpx.AsyncClient suggests network requests which could be legitimate for API calls or data fetching, but needs further investigation into the endpoints and data being transmitted.
Shell: No shell execution patterns detected.
Obfuscation: Base64 decoding is commonly used for data obfuscation but can also be legitimate for data storage and retrieval.
Credentials: No direct evidence of credential harvesting patterns detected.
Metadata: The package has suspicious maintainer history and a non-existent Git repository, raising concerns about its legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

self._http_client = httpx.AsyncClient(timeout=self._config.timeout) return self._http_clie
# 旧: 毎リクエストで async with httpx.AsyncClient(...) → handshake 重複 # 新: instance に 1 つ持って execute_q
self._http_client = httpx.AsyncClient(timeout=timeout) return self._http_client a
self._http_client = httpx.AsyncClient(timeout=self._azure_config.timeout) return self._htt
self._http_client = httpx.AsyncClient(timeout=config.timeout) self._enabled = config.enab
conds) async with aiohttp.ClientSession(timeout=timeout) as session: async with sess

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

credentials_json = base64.b64decode(config.credentials_base64).decode('utf-8') if c

✓ 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: softbank.co.jp>

✓ 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 agenticstar-platform

Create a fully-functional mini-application named 'AI Task Manager' using the 'agenticstar-platform' Python package. This application will serve as an enterprise tool to manage and automate various tasks using AI agents. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Start by setting up your Python environment and installing the 'agenticstar-platform' package. Ensure you have the necessary dependencies installed.

2. **Application Architecture**: Design the architecture of your application. It should include modules for task creation, agent assignment, task monitoring, and result reporting.

3. **Task Creation Module**: Implement a feature where users can create tasks. Each task should have details such as task description, priority level, deadline, and required skills.

4. **Agent Assignment Module**: Use the 'agenticstar-platform' package to assign appropriate AI agents to tasks based on their capabilities and current workload. Ensure the system can handle multiple agents and dynamically adjust assignments.

5. **Task Monitoring Module**: Develop a real-time dashboard that shows the status of all assigned tasks. Include features like progress tracking, estimated completion time, and any issues encountered by the agents.

6. **Result Reporting Module**: After task completion, generate detailed reports summarizing the outcomes, including any challenges faced, solutions implemented, and performance metrics of the AI agents.

7. **User Interface**: Create a user-friendly interface for interacting with the application. Consider both command-line and graphical interfaces depending on the target audience.

8. **Testing and Validation**: Thoroughly test the application to ensure all components work seamlessly together. Validate the AI agent assignments and task execution against expected outcomes.

9. **Documentation**: Provide comprehensive documentation for the application, including setup instructions, API usage, and troubleshooting guides.

10. **Deployment**: Prepare the application for deployment in an enterprise environment. Consider security measures, scalability options, and maintenance protocols.

Suggested Features:
- Integration with existing enterprise systems for seamless data flow.
- Advanced analytics for optimizing AI agent performance.
- Customizable alerts and notifications for critical events.
- User authentication and role-based access control.

Throughout the development process, leverage the 'agenticstar-platform' package to streamline the interaction between the application and AI agents, ensuring efficient task management and automation.