🤖 AI Analysis

Final verdict: SAFE

The package is likely safe for use given its low risk scores across all categories except for obfuscation and metadata quality, which raise minor concerns.

moderate obfuscation risk due to base64 decoding
low maintenance and poor metadata quality

Per-check LLM notes

Network: No network calls suggest normal behavior for a client library unless it's designed to be purely local.
Shell: No shell executions indicate the package does not attempt to run external commands without user interaction.
Obfuscation: The presence of base64 decoding suggests potential obfuscation but could also be legitimate use for data processing.
Credentials: No clear signs of credential harvesting detected.
Metadata: The package shows low maintenance and metadata quality, raising some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

Test runner config found: pyproject.toml
16 test file(s) detected (e.g. test_common.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (826 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

361 type-annotated function signatures detected in source

○ 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 score 6.0

Found 3 obfuscation pattern(s)

return None return base64.b64decode(self.audio) class ImageDetail(StrEnum): auto = 'auto'
es to bytes""" return {k: base64.b64decode(v) for k, v in self.images.items() if v} class LayoutParsi
es to bytes""" return {k: base64.b64decode(v) for k, v in self.output_images.items() if v} class Page

✓ 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: gmail.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 aiyou-client

Create a Python-based task dispatcher utility called 'TaskMaster' using the 'aiyou-client' package. This utility will serve as a central hub for managing tasks across multiple workers, ensuring efficient distribution and tracking of tasks. The application should include the following key functionalities:

1. **Task Registration**: Users should be able to register new tasks with various attributes such as priority level, estimated time to complete, and specific instructions.
2. **Worker Assignment**: Tasks should be automatically assigned to available workers based on their current load and the priority of the task.
3. **Progress Tracking**: The utility must allow users to track the progress of each task, including start time, completion status, and any comments or updates from the worker.
4. **Task Prioritization**: Implement a feature that allows for dynamic re-prioritization of tasks based on changes in urgency or availability of resources.
5. **Worker Status Monitoring**: Provide real-time monitoring of worker statuses, indicating whether they are idle, busy, or offline.
6. **Report Generation**: Enable the generation of comprehensive reports summarizing task completion rates, average task duration, and worker performance metrics.

To achieve these functionalities, utilize the 'aiyou-client' package to interact with its dispatcher business and proxy APIs. For instance, use the dispatcher API to register and manage tasks, and the proxy API to communicate with workers efficiently. Ensure that the application is user-friendly, with a clear command-line interface for task management and reporting.