🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no detected network calls, shell executions, obfuscation, or credential risks. However, low maintainer activity and poor metadata quality suggest potential maintenance issues.

No network calls detected
Low maintainer activity

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell executions detected, indicating no immediate risk of command injection or execution.
Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
Credentials: No credential harvesting patterns detected, indicating no risk of secret theft.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

🔬 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: chanzuckerberg.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with acquire-zarr

Develop a mini-application that allows users to efficiently store and manage large datasets in Zarr format using the 'acquire-zarr' package. This application should enable users to stream their data into Zarr files stored either locally or in a cloud-based storage system. Here are the steps and features your application should include:

1. **Data Streaming Interface**: Create a user-friendly interface where users can select a dataset file (CSV, Excel, etc.) and specify whether they want to save it locally or in cloud storage.
2. **Configuration Settings**: Allow users to configure compression settings and chunk sizes for optimal performance when saving large datasets.
3. **Progress Tracking**: Implement a progress bar or status indicator to show users how much of their data has been processed and saved.
4. **Error Handling**: Ensure the application can handle errors gracefully, such as file read/write errors or network issues during cloud storage operations.
5. **Cloud Integration**: Support at least one popular cloud storage service (e.g., AWS S3, Google Cloud Storage) for remote storage options.
6. **Metadata Management**: Enable users to add metadata to their datasets, which will be stored alongside the data in the Zarr file.
7. **Security Features**: If storing data in the cloud, provide options for securing data with encryption or access controls.

Utilize the 'acquire-zarr' package to handle the actual streaming of data into Zarr files, leveraging its performant capabilities for both local and cloud storage scenarios. Your goal is to create a versatile tool that simplifies the process of working with large datasets in a scalable and efficient manner.