🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, obfuscation, and credential handling. However, the metadata risk score is relatively high due to low maintainer activity and poor metadata quality.

Metadata risk score of 6 out of 10
No description or documentation provided

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: Shell execution patterns are observed but seem to be related to authentication status, login status, and version checking, which could be part of legitimate functionality.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

: completed = subprocess.run( command, input=stdi
e") try: result = subprocess.run( [binary, "auth", "status", "--json"],
x") try: result = subprocess.run( [binary, "login", "status"], captur
ol) try: result = subprocess.run( [binary, "--version"], capture_outp
e b"" try: proc = subprocess.run( args, input=stdin_bytes,

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agent-cli-dispatcher

Create a command-line utility called 'TaskMaster' using the Python package 'agent-cli-dispatcher'. TaskMaster will serve as a structured task runner for various user-defined tasks, utilizing LLMs for task execution and file-based signaling for task completion and error handling. Here’s a step-by-step guide on what your application should achieve:

1. **User Task Definition**: Users should be able to define their tasks through a simple YAML configuration file. Each task should have a unique identifier, a description, and a set of parameters required for execution.
2. **Task Execution**: Use 'agent-cli-dispatcher' to dispatch these tasks to an LLM. The dispatcher should handle the communication between the CLI and the LLM, ensuring that tasks are executed in a structured manner.
3. **Outcome Routing**: Implement a system where the outcomes of these tasks (success or failure) are routed back to the user via file signals. For instance, upon successful completion, a 'SUCCESS_<task_id>.txt' file should be created, containing the output of the task. If a task fails, an 'ERROR_<task_id>.txt' file should be generated, detailing the error encountered.
4. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms. Log all interactions with the LLM, including inputs and outputs, to a log file for troubleshooting purposes.
5. **CLI Interface**: Develop a user-friendly CLI interface allowing users to add new tasks, view the status of ongoing tasks, and manage task history.
6. **Security Measures**: Implement basic security measures to ensure that sensitive information (if any) passed through the CLI or stored in files is handled securely.

Suggested Features:
- Support for multiple LLM providers, allowing users to choose which provider they want to use for task execution.
- A feature to schedule tasks at specific times or intervals.
- An option to monitor task progress in real-time via the CLI.
- Integration with cloud storage services for storing task results securely.

How 'agent-cli-dispatcher' is Utilized:
- The dispatcher acts as the central component for managing task execution. It reads the task definitions from the YAML file, communicates with the chosen LLM, and manages the flow of data based on the task's outcome.
- Utilize the dispatcher’s capabilities for handling complex task flows, such as chaining tasks together or executing parallel tasks.
- Leverage the dispatcher’s built-in support for file signals to implement your outcome routing mechanism effectively.

Your goal is to create a versatile, user-friendly, and efficient tool that showcases the power and flexibility of 'agent-cli-dispatcher' in managing complex tasks via LLMs.