Agent skill

ros-integration

Deep integration with ROS/ROS2 middleware for node development, launch files, package management, and robot communication. Execute ros2 commands, create and validate packages, configure publishers/subscribers/services/actions, and debug topic connectivity.

Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/robotics-simulation/skills/ros-integration

Metadata

Additional technical details for this skill

author
babysitter-sdk
version
1.0.0
category
middleware
backlog id
SK-001

SKILL.md

ros-integration

You are ros-integration - a specialized skill for ROS/ROS2 middleware integration, providing deep capabilities for robot software development, node creation, and system configuration.

Overview

This skill enables AI-powered ROS/ROS2 development including:

  • Generating ROS/ROS2 package structures with proper CMakeLists.txt and package.xml
  • Creating launch files (Python launch for ROS2, XML for ROS1)
  • Configuring node parameters and YAML configuration files
  • Setting up publishers, subscribers, services, and actions
  • Generating message, service, and action definitions
  • Configuring QoS policies for DDS communication
  • Implementing lifecycle node management (ROS2)
  • Debugging topic/service connectivity issues
  • Configuring tf2 transform broadcasts

Prerequisites

  • ROS/ROS2 installation (Humble, Iron, or Rolling recommended for ROS2)
  • colcon build tools (ROS2) or catkin (ROS1)
  • rosdep for dependency management
  • Python 3.8+ for launch files

Capabilities

1. Package Generation

Generate complete ROS2 packages with proper structure:

bash
# Create a new ROS2 package
ros2 pkg create --build-type ament_python my_robot_pkg \
  --dependencies rclpy std_msgs sensor_msgs geometry_msgs

# For C++ packages
ros2 pkg create --build-type ament_cmake my_robot_cpp_pkg \
  --dependencies rclcpp std_msgs sensor_msgs

Package Structure (Python)

my_robot_pkg/
├── my_robot_pkg/
│   ├── __init__.py
│   ├── my_node.py
│   └── utils/
├── launch/
│   └── robot_launch.py
├── config/
│   └── params.yaml
├── resource/
│   └── my_robot_pkg
├── test/
├── package.xml
├── setup.py
└── setup.cfg

2. Launch File Creation

Generate Python launch files for ROS2:

python
from launch import LaunchDescription
from launch_ros.actions import Node
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
from ament_index_python.packages import get_package_share_directory
import os

def generate_launch_description():
    # Get package share directory
    pkg_share = get_package_share_directory('my_robot_pkg')

    # Declare launch arguments
    use_sim_time = DeclareLaunchArgument(
        'use_sim_time',
        default_value='false',
        description='Use simulation time'
    )

    # Node configuration
    robot_node = Node(
        package='my_robot_pkg',
        executable='my_node',
        name='robot_controller',
        output='screen',
        parameters=[
            os.path.join(pkg_share, 'config', 'params.yaml'),
            {'use_sim_time': LaunchConfiguration('use_sim_time')}
        ],
        remappings=[
            ('/cmd_vel', '/robot/cmd_vel'),
            ('/odom', '/robot/odom')
        ]
    )

    return LaunchDescription([
        use_sim_time,
        robot_node
    ])

3. Node Development

Create ROS2 nodes with publishers, subscribers, services, and actions:

python
import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy
from std_msgs.msg import String
from geometry_msgs.msg import Twist
from sensor_msgs.msg import LaserScan

class RobotController(Node):
    def __init__(self):
        super().__init__('robot_controller')

        # Declare parameters
        self.declare_parameter('max_speed', 1.0)
        self.declare_parameter('safety_distance', 0.5)

        # QoS profile for sensor data
        sensor_qos = QoSProfile(
            reliability=ReliabilityPolicy.BEST_EFFORT,
            history=HistoryPolicy.KEEP_LAST,
            depth=10
        )

        # Publishers
        self.cmd_vel_pub = self.create_publisher(
            Twist, '/cmd_vel', 10
        )

        # Subscribers
        self.laser_sub = self.create_subscription(
            LaserScan, '/scan', self.laser_callback, sensor_qos
        )

        # Timer for control loop
        self.timer = self.create_timer(0.1, self.control_loop)

        self.get_logger().info('Robot controller initialized')

    def laser_callback(self, msg):
        # Process laser scan data
        self.latest_scan = msg

    def control_loop(self):
        # Main control logic
        max_speed = self.get_parameter('max_speed').value
        # ... control logic ...

def main(args=None):
    rclpy.init(args=args)
    node = RobotController()
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

4. Message/Service/Action Definitions

Generate custom message definitions:

# msg/RobotStatus.msg
std_msgs/Header header
string robot_name
float64 battery_level
bool is_moving
geometry_msgs/Pose current_pose
float64[] joint_positions

Service definition:

# srv/SetMode.srv
string mode
---
bool success
string message

Action definition:

# action/Navigate.action
# Goal
geometry_msgs/PoseStamped target_pose
float64 timeout
---
# Result
bool success
string message
float64 elapsed_time
---
# Feedback
float64 distance_remaining
float64 estimated_time_remaining

5. QoS Configuration

Configure Quality of Service policies for different use cases:

python
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy, QoSDurabilityPolicy

# Sensor data (high frequency, lossy)
sensor_qos = QoSProfile(
    reliability=QoSReliabilityPolicy.BEST_EFFORT,
    history=QoSHistoryPolicy.KEEP_LAST,
    depth=5
)

# Control commands (reliable)
control_qos = QoSProfile(
    reliability=QoSReliabilityPolicy.RELIABLE,
    history=QoSHistoryPolicy.KEEP_LAST,
    depth=10
)

# Parameters and configuration (transient local)
config_qos = QoSProfile(
    reliability=QoSReliabilityPolicy.RELIABLE,
    durability=QoSDurabilityPolicy.TRANSIENT_LOCAL,
    history=QoSHistoryPolicy.KEEP_LAST,
    depth=1
)

6. Debugging Commands

Debug ROS2 systems:

bash
# List all nodes
ros2 node list

# Get node info
ros2 node info /robot_controller

# List topics
ros2 topic list -t

# Echo topic
ros2 topic echo /cmd_vel

# Topic bandwidth/frequency
ros2 topic hz /scan
ros2 topic bw /camera/image_raw

# Service list and call
ros2 service list
ros2 service call /set_mode my_robot_pkg/srv/SetMode "{mode: 'autonomous'}"

# Parameter operations
ros2 param list /robot_controller
ros2 param get /robot_controller max_speed
ros2 param set /robot_controller max_speed 2.0

# TF2 debugging
ros2 run tf2_tools view_frames
ros2 run tf2_ros tf2_echo base_link odom

MCP Server Integration

This skill can leverage the following MCP servers for enhanced capabilities:

Server Description Installation
ros-mcp-server (robotmcp) ROS/ROS2 bridge via MCP GitHub
ros2-mcp-server (kakimochi) Python-based ROS2 MCP integration Glama
roba-labs-mcp ROS documentation and learning resources Glama

Best Practices

  1. Use namespaces - Organize nodes with proper namespaces for multi-robot systems
  2. Parameter files - Keep configuration in YAML files, not hardcoded
  3. QoS matching - Ensure publishers and subscribers use compatible QoS
  4. Lifecycle nodes - Use managed lifecycle for clean startup/shutdown
  5. Composition - Use component containers for efficient node composition
  6. Testing - Include launch_testing for integration tests

Process Integration

This skill integrates with the following processes:

  • robot-system-design.js - System architecture with ROS nodes
  • robot-calibration.js - Calibration node development
  • gazebo-simulation-setup.js - ROS-Gazebo integration
  • nav2-navigation-setup.js - Navigation stack configuration
  • multi-robot-coordination.js - Multi-robot ROS communication

Output Format

When executing operations, provide structured output:

json
{
  "operation": "create-package",
  "packageName": "my_robot_pkg",
  "buildType": "ament_python",
  "status": "success",
  "artifacts": [
    "my_robot_pkg/package.xml",
    "my_robot_pkg/setup.py",
    "my_robot_pkg/my_robot_pkg/__init__.py"
  ],
  "nextSteps": [
    "Add node implementation",
    "Create launch file",
    "Build with colcon build"
  ]
}

Error Handling

  • Capture full error output from ros2 commands
  • Check for missing dependencies with rosdep
  • Verify DDS configuration for communication issues
  • Check QoS compatibility for topic connectivity
  • Validate package.xml and CMakeLists.txt syntax

Constraints

  • Verify ROS2 distribution before operations
  • Use sourced workspace for all commands
  • Respect package naming conventions (snake_case)
  • Follow ROS2 design patterns and best practices

Expand your agent's capabilities with these related and highly-rated skills.

a5c-ai/babysitter

gsd-tools

Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).

514 31
Explore
a5c-ai/babysitter

model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

514 31
Explore
a5c-ai/babysitter

verification-suite

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

514 31
Explore
a5c-ai/babysitter

state-management

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.

514 31
Explore
a5c-ai/babysitter

git-integration

Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.

514 31
Explore
a5c-ai/babysitter

frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

514 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results