🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk and lacks clear maintenance history, raising concerns about its true intentions.

Unusual obfuscation techniques
Lack of maintainer history

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: The code shows signs of unusual obfuscation techniques which may indicate an attempt to hide functionality or malicious intent.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The package shows some red flags such as lack of maintainer history and git repository, but no concrete evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

owledgeGraph: path = Path(__import__("os").getenv("MEMORY_GRAPH_PATH", "memory_graph.json")) retur
ScoreStore() path = Path(__import__("os").getenv("MEMORY_SCORES_PATH", "memory_scores.json")) if
ore) -> None: path = Path(__import__("os").getenv("MEMORY_SCORES_PATH", "memory_scores.json")) pat

✓ 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: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 agentic-memory-mcp

Create a personalized task management assistant named 'TaskMaster' using the Python package 'agentic-memory-mcp'. TaskMaster will leverage the knowledge graph capabilities of agentic-memory-mcp to intelligently manage user tasks and provide insights based on past behavior. Here’s a detailed breakdown of the application's functionalities:

1. **User Task Input**: Users can input their daily tasks through a simple command-line interface or a basic web interface. These tasks can include details like due dates, priorities, and tags.
2. **Knowledge Graph Integration**: Utilize agentic-memory-mcp to store and manage these tasks within a knowledge graph. This allows for complex queries and relationships between tasks (e.g., dependencies, recurring tasks).
3. **Intelligent Scheduling**: Based on historical data stored in the knowledge graph, TaskMaster should suggest optimal times to complete tasks, considering factors such as user productivity patterns and task complexity.
4. **Progress Tracking & Insights**: Provide users with real-time progress updates and generate weekly/monthly reports highlighting productivity trends, common delays, and suggestions for improvement.
5. **Integration with Calendar Services**: Optionally, integrate TaskMaster with calendar services like Google Calendar to automatically schedule tasks and send reminders.
6. **Voice Command Support**: Implement voice command functionality to allow users to add, modify, or delete tasks hands-free.
7. **Customization Options**: Allow users to customize their experience by setting up personal preferences such as preferred notification times, task categorizations, etc.

For each feature, explain how agentic-memory-mcp plays a crucial role in enabling intelligent task management and enhancing user experience. For instance, discuss how the package helps in building a robust knowledge graph, performing complex queries, and integrating various data sources to provide personalized recommendations.