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CLI Usage Guide

This document provides comprehensive information about using the command-line interface for task management in aibtcdev-backend.

Table of Contents

Overview

The run_task.py CLI script provides a command-line interface to interact with the job management system, allowing you to list available tasks, run specific tasks, and monitor their execution.

Prerequisites

Make sure you have the environment set up and dependencies installed:

# Install dependencies
pip install -r requirements.txt

# Or if using uv (recommended)
uv sync

Basic Usage

The CLI script supports several commands:

1. List Available Tasks

# List all tasks in table format
python run_task.py list

# List tasks in JSON format
python run_task.py list --format json

# List only enabled tasks
python run_task.py list --enabled-only

# Filter by priority level
python run_task.py list --priority high

2. Run a Specific Task

# Run a task with default settings
python run_task.py run agent_account_deployer

# Run with custom timeout
python run_task.py run chain_state_monitor --timeout 600

# Run with verbose output
python run_task.py run dao_deployment --verbose

# Run with parameters (JSON format)
python run_task.py run dao_proposal_vote --parameters '{"proposal_id": "123", "vote": "yes"}'

3. Show Task Information

# Show detailed information about a specific task
python run_task.py info agent_account_deployer
python run_task.py info dao_token_holders_monitor

4. Show System Status

# Show system status in table format
python run_task.py status

# Show system status in JSON format
python run_task.py status --format json

5. Show Task Metrics

# Show metrics for all tasks
python run_task.py metrics

# Show metrics for a specific task
python run_task.py metrics agent_account_deployer

# Show metrics in JSON format
python run_task.py metrics --format json

Available Tasks

Here are the currently available tasks in the system:

  • agent_account_deployer: Deploys agent account contracts
  • chain_state_monitor: Monitors blockchain state for synchronization
  • dao_deployment: Processes DAO deployment requests
  • dao_deployment_tweet: Generates congratulatory tweets for deployed DAOs
  • dao_proposal_conclude: Processes and concludes DAO proposals
  • dao_proposal_embedder: Generates embeddings for DAO proposals
  • dao_proposal_evaluation: Evaluates DAO proposals using AI
  • dao_proposal_vote: Processes and votes on DAO proposals
  • dao_token_holders_monitor: Monitors and syncs DAO token holders
  • discord: Sends Discord messages from queue
  • tweet: Processes and sends tweets for DAOs

Examples

Example 1: Check what tasks are available

python run_task.py list --enabled-only

Example 2: Run the chain state monitor

python run_task.py run chain_state_monitor --verbose

Example 3: Check detailed info about a task

python run_task.py info dao_deployment

Example 4: Monitor system health

python run_task.py status

Example 5: Check task performance metrics

python run_task.py metrics agent_account_deployer

Task Parameters

Some tasks may accept parameters. Use the --parameters flag with JSON format:

# Example with parameters
python run_task.py run custom_task --parameters '{"param1": "value1", "param2": 123}'

Output Formats

Most commands support both table and JSON output formats:

  • Table format: Human-readable, formatted output (default)
  • JSON format: Machine-readable output for scripting

Use --format json to get JSON output for any command that supports it.

Error Handling

The CLI tool includes comprehensive error handling:

  • Timeout errors: Tasks that run too long will be terminated
  • Parameter errors: Invalid JSON parameters will be caught and reported
  • Task not found: Clear error message when trying to run non-existent tasks
  • Validation errors: Tasks that fail validation will report the specific issue

Tips

  1. Always check task info first: Use info command to understand what a task does before running it
  2. Use verbose mode for debugging: Add --verbose flag to see detailed execution information
  3. Monitor system status: Use status command to check overall system health
  4. Check metrics regularly: Use metrics command to monitor task performance
  5. Filter tasks by priority: Use --priority flag to focus on specific task types

Development Usage

For development and testing:

# Run a task with extended timeout for debugging
python run_task.py run dao_deployment --timeout 900 --verbose

# Check system status after running tasks
python run_task.py status --format json | jq '.health_status'

# Monitor specific task metrics
python run_task.py metrics chain_state_monitor --format json

This CLI tool integrates with the existing job management system and provides a convenient way to manually trigger tasks, monitor their execution, and debug issues during development.

Troubleshooting

  • Task Not Found: Ensure the task name matches exactly (case-sensitive). Use list to check available tasks.
  • JSON Parse Errors: Verify parameters are valid JSON. Escape quotes if needed.
  • Timeout Issues: Increase --timeout for long-running tasks.
  • Permission Errors: Run with appropriate privileges if accessing restricted resources.
  • Dependency Issues: Check logs for missing dependencies or configurations.

If issues persist, check server logs or enable verbose mode with --verbose.