🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network usage, shell execution, and obfuscation, but has a notable metadata risk due to signs of low effort and lack of transparency, which raises suspicion.

Metadata risk of 6 out of 10
Signs of low effort and potential lack of transparency

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several signs of low effort and potential lack of transparency, raising concerns about its legitimacy.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: phper.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agentgraph-core

Create a project called 'TaskMaster' which is a task management tool utilizing the 'agentgraph-core' Python package. TaskMaster should allow users to create tasks, assign them to other users, set deadlines, and track progress through various stages of completion. Additionally, it should include a feature for users to leave comments on tasks and have a built-in notification system to alert users when tasks are assigned or due soon.

Step-by-step guide:
1. Initialize the project structure and install the 'agentgraph-core' package.
2. Define the user model and task model using the agent and graph primitives provided by 'agentgraph-core'. Each task will be an agent that can transition through different states (e.g., pending, in progress, completed).
3. Implement the functionality for users to create tasks and assign them to other users. This involves setting up tools and runs within 'agentgraph-core' to handle these operations.
4. Add a deadline feature for tasks where users can set a specific date and time for task completion. Utilize the schedules primitive from 'agentgraph-core' to manage these deadlines.
5. Develop a commenting system where users can leave feedback or additional instructions on tasks. This can be achieved by leveraging the events and artifacts primitives in 'agentgraph-core' to capture and store comments as part of the task's lifecycle.
6. Integrate a notification system that alerts users via email or in-app notifications when tasks are assigned or nearing their deadlines. Use the human gates primitive from 'agentgraph-core' to trigger these notifications at appropriate times.
7. Finally, implement a dashboard view where users can see all their tasks, sorted by status and deadline. This will involve querying the knowledge ledger provided by 'agentgraph-core' to retrieve and display relevant information.

Suggested Features:
- User authentication and authorization.
- Task prioritization options.
- Integration with external calendar applications for better time management.
- Reporting functionalities to analyze task completion rates and user productivity.
- Mobile app compatibility for easy access on-the-go.