Does this skill help with coordinate conversion for navigation?

Yes, it provides specific implementation logic for converting Limelight's botpose (FTC coordinates) into Pedro Pathing coordinates, including inch-per-meter scaling.

How do I ensure the Limelight is actually capturing data?

You must call the .start() method after initializing the hardware map and selecting your pipeline. This skill includes validation checks to ensure results are valid before your code acts on them.

What happens if I forget to stop the Limelight after an OpMode?

Leaving the camera running wastes battery and can cause processing overhead. This skill provides 'Anti-Patterns' to remind you to include .stop() in your OpMode's stop method.

Can I use this for custom neural network detection?

Yes, the skill supports pipeline switching (0-9), allowing you to move between standard AprilTag detection and custom neural network or color tracking pipelines via code.

Limelight 3A Vision for FTC

Name: Limelight 3A Vision for FTC
Author: ncssm-robotics

byncssm-robotics

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Mobile Development

Simplifies Limelight 3A vision system integration for FTC robots, enabling advanced AprilTag detection, localization, and automated aiming.

This skill provides comprehensive guidance and implementation patterns for the Limelight 3A vision system within the FIRST Tech Challenge (FTC) ecosystem. It streamlines hardware configuration, pipeline management, and complex vision tasks such as MegaTag2 localization and color tracking. By offering pre-built logic for coordinate conversion—specifically targeting Pedro Pathing—and enforcing best practices for resource management (like start/stop cycles), it helps teams build more reliable autonomous routines and precision tele-op enhancements while avoiding common pitfalls like stale data or battery drain.

Key Features

01Streamlined hardware configuration and lifecycle management for Limelight 3A.

022 GitHub stars

03Automated utility scripts for turret aiming, distance calculation, and pipeline switching.

04Safety-first anti-patterns to prevent invalid data usage and hardware resource leaks.

05Seamless coordinate conversion between Limelight vision and Pedro Pathing systems.

06Advanced AprilTag detection and MegaTag2 localization implementation patterns.

Use Cases

01Developing automated turret aiming systems using horizontal (tx) and vertical (ty) offsets.

02Implementing high-precision field localization using AprilTags and MegaTag2.

03Creating vision-based autonomous paths by converting botpose data to Pedro Pathing coordinates.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ncssm-robotics/ftc-claude limelight

For use in Claude.ai and ChatGPT

Download Skill