🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no network calls, shell executions in benign contexts, and no obfuscation or credential harvesting attempts. The metadata suggests a new maintainer but does not raise significant concerns.

No network calls detected.
Shell execution appears benign.
No obfuscation or credential harvesting.

Per-check LLM notes

Network: No network calls detected, which is normal and not indicative of malicious activity.
Shell: Shell execution appears to be checking Node.js version and running tests, which seems benign unless the context of the actions is suspicious.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive data theft.
Metadata: The maintainer has only one package, which could indicate a new or less active user, but no other red flags were raised.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

3000 """ try: subprocess.run(["node", "--version"], capture_output=True, check=True)
..") try: subprocess.run(["node", str(test_dir / test_file)], capture_output=False, c

✓ 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

Repository Muizzkolapo/agent-actions appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Muizz Kolapo" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agent-actions

Create a fully-functional mini-application called 'TaskMaster' that leverages the 'agent-actions' Python package to manage and orchestrate personal tasks and workflows. TaskMaster should allow users to define their tasks declaratively using YAML files, which will then be executed based on predefined conditions or triggers. Here’s a detailed breakdown of what your application should achieve:

1. **Task Definition**: Users should be able to define tasks in YAML files. Each task should have attributes like name, description, due date, priority level, and a set of actions (e.g., send email, create calendar event).
2. **Workflow Orchestration**: Define workflows as sequences of tasks that depend on each other or run concurrently. Workflows should be specified in YAML files as well.
3. **Trigger Mechanism**: Implement a system where tasks can be triggered manually, at scheduled times, or automatically based on certain conditions (e.g., completion of another task).
4. **Notification System**: Integrate notifications to inform users about task status changes, deadlines approaching, or workflow completions.
5. **UI/UX**: While primarily command-line driven, consider adding a simple web interface using Flask or a similar lightweight framework to view and manage tasks and workflows visually.
6. **Persistence Layer**: Store task definitions and statuses in a local SQLite database for persistence across sessions.
7. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place to track issues and provide insights into task execution.

Utilize the 'agent-actions' package to handle the core logic of defining, triggering, and executing tasks and workflows according to the YAML configurations provided by users. This includes parsing YAML files, executing actions, managing dependencies between tasks, and handling triggers efficiently.