🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of network interaction or shell execution, aligning with typical non-networked utility libraries. The low risk score indicates minimal threat.

No network calls detected
No shell execution detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
Shell: No shell execution detected, indicating no immediate risk of command injection or similar vulnerabilities.

🔬 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 score 5.0

Git history flags: Repository created very recently: 2 day(s) ago (2026-06-03T16:06:47Z)

Repository created very recently: 2 day(s) ago (2026-06-03T16:06:47Z)
Repository has zero stars and zero forks

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Package uploaded less than 24 hours ago (2026-06-05T09:33:33.000Z)
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)

💡 AI App Starter Prompt

Use this prompt to build a project with halo-format

Create a Python-based mini-application called 'DataNav' which leverages the 'halo-format' package to manage and navigate through a complex dataset efficiently and securely. Your goal is to develop a tool that allows users to encode their data into a navigable tree structure, where each piece of data is uniquely identified and stored in a way that supports easy retrieval and verification.

Step 1: Initialize the Project
- Set up a new Python virtual environment and install the necessary packages including 'halo-format'.
- Create a main script and a few helper functions to handle user input and output.

Step 2: Data Encoding
- Implement a feature that takes user input (a dictionary or list representing the dataset) and encodes it into a navigable tree using 'halo-format'. This process should ensure that each node in the tree is uniquely identifiable and secure.
- Provide options for users to specify encryption settings or other configurations during the encoding process.

Step 3: Data Navigation
- Develop functionality that allows users to traverse the encoded tree structure. Users should be able to navigate through the tree to find specific pieces of data based on their unique identifiers.
- Include features that allow for filtering nodes based on certain criteria or attributes.

Step 4: Data Verification
- Ensure that the application includes mechanisms to verify the integrity of the data as it is retrieved from the tree. This could involve checking hashes or signatures associated with each node.
- Allow users to validate the entire tree or individual nodes against a provided key or signature.

Suggested Features:
- A command-line interface for easy interaction with the application.
- Support for importing/exporting datasets in various formats such as JSON or CSV.
- Integration with a simple web interface for visual navigation of the tree structure.
- Advanced search capabilities allowing users to query the dataset using complex criteria.

Remember to document your code thoroughly and include examples of how to use 'halo-format' effectively within your application. Your final deliverables should include a fully functional application, comprehensive documentation, and sample datasets to demonstrate the application's capabilities.