AI Analysis
The package exhibits low signs of obfuscation and credential harvesting risks, but the metadata analysis suggests low maintainer effort and lack of transparency, which raises suspicion.
- Low obfuscation risk
- Low credential risk
- Signs of low maintainer effort and lack of transparency
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and lack of transparency, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (4.4/10)
Test suite present — 15 test file(s) found
Test runner config found: conftest.py15 test file(s) detected (e.g. test_context_snapshots.py)
Some documentation present
Brief PyPI description (319 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
214 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 2 network call pattern(s)
ast).""" try: r = httpx.get(f"{url.rstrip('/')}{path}", timeout=3.0) return r.st) async with aiohttp.ClientSession( timeout=aiohttp.ClientTimeout(total=180)
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a personalized task management system using the 'aisolate-agent' package. This application should enable users to input daily tasks and goals, which the AI agent will then analyze to suggest the most efficient order and timing for completing these tasks based on user preferences, past performance, and current context (such as weather conditions or time of day). Here are the key features to include: 1. User Interface: Develop a simple yet intuitive UI where users can add tasks, set deadlines, and mark tasks as completed. 2. Task Prioritization: Use 'aisolate-agent' to prioritize tasks based on urgency, importance, and dependencies between tasks. 3. Time Management: Implement a feature that suggests optimal times for starting each task based on the user's productivity patterns and external factors like weather or time of day. 4. Goal Setting: Allow users to set long-term goals and have the AI agent break them down into smaller, manageable tasks and suggest a timeline for completion. 5. Learning and Adaptation: Incorporate machine learning capabilities from 'aisolate-agent' to continuously learn from user behavior and improve task suggestions over time. 6. Reporting and Analytics: Provide users with weekly reports summarizing their progress towards both short-term tasks and long-term goals, highlighting areas for improvement. 7. Integration: Consider integrating with calendar applications or other productivity tools to enhance usability and synchronization. Utilize the 'aisolate-agent' package to handle the intelligent planning and reasoning aspects of your application, ensuring that the task management system not only helps users stay organized but also optimizes their productivity through smart recommendations.
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