🤖 AI Analysis

Final verdict: SAFE

The package has low risks in terms of network, shell, and obfuscation activities. However, the metadata suggests potential inactivity or newness of the maintainer, which could be a concern.

Low risk in network, shell, and obfuscation activities
Potential inactivity or newness of the maintainer

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of potentially being maintained by an inactive or new author with little community engagement.

🔬 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: ai-manifests.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 adp-agent-anchor

Create a blockchain-based calibration snapshot utility using the 'adp-agent-anchor' Python package. This utility will serve as a bridge between your local calibration data and the Neo3 blockchain, allowing you to securely and transparently commit snapshots of your calibration process to the blockchain. Here’s a step-by-step guide on how to develop this utility:

1. **Setup Environment**: Start by setting up your Python environment. Install the necessary packages including 'adp-agent-anchor'. Make sure you have access to a Neo3-compatible blockchain node.
2. **Define Calibration Data Model**: Define a model or structure for the calibration data that will be committed to the blockchain. This could include parameters like timestamp, device ID, calibration values, etc.
3. **Generate Snapshots**: Implement functionality within your utility to generate snapshots of calibration data at regular intervals or upon specific triggers.
4. **Sign Snapshots**: Use 'adp-agent-anchor' to sign these snapshots before committing them to the blockchain. Ensure that the signing process adheres to the security standards required by the Neo3 network.
5. **Commit Snapshots to Blockchain**: Utilize 'adp-agent-anchor' to commit the signed snapshots to the Neo3 blockchain. Ensure that each transaction is properly recorded and can be queried later if needed.
6. **Query and Retrieve Snapshots**: Build functionality to query the blockchain for previously committed snapshots based on filters such as date range, device ID, etc. Allow users to retrieve and review past calibration activities.
7. **User Interface**: Develop a simple user interface (CLI or web-based) that allows users to interact with the utility easily. Users should be able to initiate calibration processes, view committed snapshots, and manage their blockchain interactions through this interface.
8. **Security Enhancements**: Consider implementing additional security measures such as multi-signature transactions for critical operations, or encryption for sensitive data stored locally before being committed to the blockchain.
9. **Testing and Documentation**: Thoroughly test your utility under various scenarios to ensure reliability and security. Document the setup process, usage instructions, and any troubleshooting tips for end-users.

Suggested Features:
- Automatic generation and commitment of snapshots at regular intervals.
- Manual triggering of snapshot generation and commitment via the user interface.
- Comprehensive logging and reporting of all blockchain transactions.
- Support for multiple devices or users with distinct identities and permissions.
- Integration with existing calibration systems or devices to streamline the data collection process.