Object Detection/Segmentation Skill
Deep learning based object detection and segmentation for robotics applications
Best use case
Object Detection/Segmentation Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Deep learning based object detection and segmentation for robotics applications
Teams using Object Detection/Segmentation Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/object-detection/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Object Detection/Segmentation Skill Compares
| Feature / Agent | Object Detection/Segmentation Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Deep learning based object detection and segmentation for robotics applications
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Object Detection/Segmentation Skill ## Overview Expert skill for deploying and optimizing deep learning models for object detection, instance segmentation, and 3D object detection in robotics applications. ## Capabilities - Configure YOLO (v5, v8) for real-time detection - Set up Detectron2 for instance segmentation - Implement semantic segmentation models - Configure TensorRT optimization for Jetson - Set up ONNX runtime deployment - Implement 3D object detection (PointPillars, VoxelNet) - Configure depth-based object detection - Set up ROS vision pipelines with image_pipeline - Implement object tracking (SORT, DeepSORT, ByteTrack) - Configure multi-camera detection fusion ## Target Processes - object-detection-pipeline.js - synthetic-data-pipeline.js - nn-model-optimization.js - moveit-manipulation-planning.js ## Dependencies - YOLO (Ultralytics) - Detectron2 - TensorRT - ONNX Runtime - vision_msgs ## Usage Context This skill is invoked when processes require object detection model deployment, instance segmentation, 3D detection, or multi-object tracking for robot perception. ## Output Artifacts - Detection model configurations - TensorRT optimized models - ROS detection node implementations - Tracking pipeline configurations - Multi-camera fusion setups - Inference optimization scripts
Related Skills
secret-detection-scanner
Detect secrets, credentials, and sensitive data in code and configurations. Scan git history for secrets, detect API keys, tokens, passwords, check environment files, monitor CI/CD logs for exposure, generate remediation steps, and track secret rotation status.
fallacy-detection-analysis
Identify formal and informal logical fallacies in arguments, classify fallacy types, and explain precisely why reasoning fails with reference to logical principles
learning-objectives-writing
Write measurable, SMART learning objectives using Bloom's Taxonomy cognitive levels aligned with desired outcomes and assessment strategies
bim-clash-detection
BIM clash detection skill for identifying and managing coordination conflicts between disciplines
process-builder
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
babysitter
Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)
yolo
Run Babysitter autonomously with minimal manual interruption.
user-install
Install the user-level Babysitter Codex setup.
team-install
Install the team-pinned Babysitter Codex workspace setup.
retrospect
Summarize or retrospect on a completed Babysitter run.
resume
Resume an existing Babysitter run from Codex.
project-install
Install the Babysitter Codex workspace integration into the current project.