🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell execution activities, though there is no direct evidence of malicious intent. The lack of clear authorship and low maintenance efforts raise concerns about potential supply-chain risks.

moderate network risk
high shell execution risk
low maintenance effort

Per-check LLM notes

Network: Network calls to external URLs are observed, which could be legitimate for fetching resources or updates, but should be scrutinized for unexpected destinations.
Shell: Execution of shell commands and an external tool ('agata') raises concern as it may indicate the package is performing actions on the system beyond its intended purpose.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintenance effort and lacks a clear author identity, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

{url}...") response = requests.get(url, timeout=30) response.raise_for_status()
r API...") response = requests.get(OPENROUTER_MODELS_URL, headers=headers, timeout=30)
_URL}...") response = requests.get(ZEN_DOCS_URL, timeout=30) response.raise_for_status(
) try: response = requests.get(models_url, timeout=5) if response.status_code == 20
try: response = requests.get(url, timeout=5) response.raise_for_status()
t(timeout) response = requests.post( LLAMACPP_API_URL, json=payload,

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

try: result = subprocess.run( cmd, capture_output=True,
are try: result = subprocess.run( ['agata', 'compare', '-f', output, '-e', yaml_f

✓ 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 agentgog

Your task is to develop a simple yet powerful personal task management application using the Python package 'agentgog'. This application, named 'TaskMaster', will allow users to manage their daily tasks efficiently. It will support features such as adding new tasks, marking tasks as completed, setting reminders, and categorizing tasks into different lists like 'Work', 'Personal', etc. Additionally, it will provide a feature to generate weekly reports summarizing completed and pending tasks.

Here's a step-by-step guide on how to build 'TaskMaster':
1. **Setup Project**: Initialize a new Python project and install the 'agentgog' package. Use 'agentgog' to handle the backend logic of task management.
2. **Design User Interface**: Design a clean and user-friendly interface where users can interact with TaskMaster. This could be a console-based interface or a simple web UI depending on your preference.
3. **Add Task Functionality**: Implement functionality to add new tasks to various categories. Each task should have details like title, due date, priority level, and category.
4. **Mark Tasks as Completed**: Allow users to mark tasks as completed directly from the interface. Ensure that once marked, these tasks are no longer shown in the active tasks list.
5. **Set Reminders**: Integrate reminder functionality so that users receive notifications before the deadline of their tasks. Utilize 'agentgog' to manage these reminders effectively.
6. **Categorization and Sorting**: Enable users to sort and filter tasks based on categories and deadlines. This will help in managing tasks more efficiently.
7. **Weekly Reports**: Develop a feature that generates a summary report at the end of each week. This report should highlight tasks completed during the week and any pending tasks.
8. **Testing and Debugging**: Thoroughly test the application to ensure all functionalities work as expected. Pay special attention to edge cases and error handling.
9. **Documentation and Deployment**: Document the application's setup process and usage instructions. Consider deploying the application on a platform like Heroku or AWS for easy access.

In this project, the 'agentgog' package will serve as the backbone for managing tasks, reminders, and generating reports. Make sure to leverage its capabilities to enhance the application's performance and reliability.