percy-visual-testing
Percy visual testing platform integration for visual regression detection
Percy visual testing platform integration for visual regression detection
Behavior tree design and implementation for robot decision making
Specialized skill for robot vision including feature detection, tracking, and camera calibration
ML model optimization and deployment on robot edge devices (Jetson, embedded)
Grasp planning and execution for robotic manipulation tasks
Specialized skill for NVIDIA Isaac Sim photorealistic simulation and synthetic data generation
Robot kinematics and dynamics computation including forward/inverse kinematics and dynamics
Sampling-based and optimization-based motion planning algorithms
Expert skill for Model Predictive Control implementation and tuning
Coordination and task allocation for multi-robot systems and fleets
Specialized skill for ROS2 Nav2 navigation stack configuration and behavior trees
Deep learning based object detection and segmentation for robotics applications
Specialized skill for 3D point cloud processing and analysis using PCL and Open3D
RL training for robot control using simulation with sim-to-real transfer
RViz configuration and custom visualization for robot development and debugging
Robot safety system design and validation for industrial and collaborative robots
Expert skill for multi-sensor fusion and state estimation using Kalman filtering. Implement EKF/UKF, configure robot_localization, fuse IMU, GPS, odometry, and visual sensors for robust localization.
Expert skill for SLAM algorithm selection, configuration, and tuning. Configure visual SLAM (ORB-SLAM3, RTAB-Map), LiDAR SLAM (Cartographer, LIO-SAM), tune parameters, evaluate accuracy, and optimize for real-time performance.
Expert skill for ROS tf2 coordinate frame management and transforms
Expert skill for robot model creation and validation in URDF and SDF formats. Generate URDF files with proper link-joint hierarchy, create Xacro macros, calculate inertial properties, configure joint types, and validate models.
AI/ML model security testing and adversarial research capabilities. Generate adversarial examples, test model robustness, perform model extraction attacks, test for data poisoning, analyze model fairness, and support ART framework integration.
Comprehensive fuzzing operations with AFL++, libFuzzer, and OSS-Fuzz integration
Generate threat models using STRIDE, PASTA, or VAST methodologies
Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.