🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all checked categories except for metadata, where the maintainer's account activity raises minor concerns. However, these concerns alone do not justify labeling the package as suspicious or malicious.

No network calls
No shell execution
No obfuscation
No credential harvesting
Single package from maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating the package does not execute external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Low (2.0/10)

○ 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 (3223 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "[email protected]" 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 almanak

Create a fully-functional mini-application using the Almanak Python package that simulates a simple decentralized finance (DeFi) trading bot. This bot will interact with a mock DeFi ecosystem to perform automated trades based on predefined rules and market conditions. Here’s a detailed plan of what your project should accomplish:

1. **Project Setup**: Begin by installing the Almanak package and setting up a virtual environment for your project.
2. **Environment Mockup**: Develop a simplified version of a DeFi market environment where assets can be bought, sold, and swapped according to certain rules. This environment should simulate real-world DeFi platforms but in a controlled, testable way.
3. **Agent Creation**: Using Almanak's agent-based modeling capabilities, create a trading bot that operates within your mocked-up DeFi environment. The bot should be able to execute trades based on specific conditions such as price movements, time intervals, or volume thresholds.
4. **State Machine Implementation**: Implement a state machine architecture for your bot to handle different states like 'Idle', 'Monitoring Market', 'Executing Trade', etc. Each state should have clear entry and exit criteria.
5. **Non-Custodial Execution**: Ensure that all trades executed by your bot are done through a non-custodial mechanism provided by Almanak. This means that the bot will use Safe smart accounts to manage funds without ever holding them directly.
6. **Testing and Validation**: Test your bot thoroughly under various market scenarios to ensure it behaves as expected. Validate its performance against benchmarks or predefined success metrics.
7. **Documentation and Reporting**: Provide comprehensive documentation detailing how each component of your bot works, along with a report summarizing its performance during testing.

Suggested Features:
- Real-time market data simulation for testing purposes.
- Adjustable parameters for trade strategies (e.g., stop-loss, take-profit).
- Logging mechanisms to track bot actions and outcomes.
- Integration with common blockchain explorers for viewing transaction details.

How Almanak is Utilized:
- Almanak's agent-based modeling allows you to define complex behaviors for your bot, making it adaptable to different market conditions.
- Its state machine framework ensures that your bot transitions smoothly between different operational modes.
- Non-custodial execution through Safe smart accounts enhances security while automating trades.