Best use case
sensor-fusion is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multi-sensor fusion algorithms for perception in autonomous driving
Teams using sensor-fusion 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/sensor-fusion/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How sensor-fusion Compares
| Feature / Agent | sensor-fusion | 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?
Multi-sensor fusion algorithms for perception in autonomous driving
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
# Sensor Fusion Skill ## Purpose Enable multi-sensor fusion algorithm development for autonomous driving perception including object detection, tracking, and environmental modeling. ## Capabilities - Camera, radar, lidar data preprocessing - Object detection fusion algorithms - Tracking filter implementation (Kalman, EKF, UKF) - Association algorithms (Hungarian, GNN, JPDA) - Occupancy grid fusion - Confidence estimation and sensor weighting - Time synchronization handling - Ground truth comparison and metrics ## Usage Guidelines - Preprocess sensor data for consistent coordinate frames - Select appropriate tracking filters based on object dynamics - Implement robust association for multi-target scenarios - Fuse sensor confidence for reliable perception - Handle time delays and synchronization issues - Validate fusion against ground truth data ## Dependencies - ROS/ROS2 - TensorFlow - PyTorch - NVIDIA DriveWorks ## Process Integration - ADA-001: Perception System Development - ADA-002: Path Planning and Motion Control - ADA-003: ADAS Feature Development - ADA-004: Simulation and Virtual Validation
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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.
plan
Plan a Babysitter workflow without executing the run.
observe
Observe, inspect, or monitor a Babysitter run.