🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential malicious intent due to obfuscated code and a suspicious commit history, though there is no direct evidence of harmful activity.

High obfuscation risk due to use of eval and obfuscated code.
Rapid commit history with low engagement in the repository.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute external commands.
Obfuscation: The use of eval and obfuscated code suggests potential for executing arbitrary code, indicating high risk.
Credentials: No clear patterns of credential harvesting detected.
Metadata: The repository's rapid commit history and low engagement suggest potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

+ 2 * 3').""" return str(eval(expression)) # üretimde tehlikeli — sadece demo! # DEMO:

✓ 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 score 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
All 15 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Hakan Sabunis" 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 agentlite-py

Create a personalized task management assistant using the 'agentlite-py' package. This assistant will help users manage their daily tasks by providing reminders, categorizing tasks based on urgency and importance, and offering suggestions for optimizing productivity. The application should have the following features:

1. **Task Creation**: Users can add new tasks with descriptions, deadlines, and priority levels.
2. **Task Categorization**: Automatically classify tasks into categories such as 'High Priority', 'Medium Priority', and 'Low Priority'.
3. **Reminders**: Set up recurring or one-time reminders for each task via email or SMS notifications.
4. **Productivity Tips**: Based on the type and number of tasks, provide personalized productivity tips.
5. **Analytics**: Offer weekly/monthly reports summarizing completed tasks, time spent, and productivity trends.

To implement these features, you'll need to integrate 'agentlite-py' for handling interactions with Claude's AI capabilities. Specifically, use Claude's insights to enhance task categorization and generate relevant productivity tips. Additionally, ensure the application has a user-friendly interface for adding tasks and viewing analytics.