Motion Planning Skill
Sampling-based and optimization-based motion planning algorithms
npx skills add a5c-ai/babysitter --skill motion-planning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Motion Planning Skill ## Overview Expert skill for implementing and configuring motion planning algorithms, including sampling-based planners (OMPL) and optimization-based trajectory planners. ## Capabilities - Configure OMPL planners (RRT, RRT*, RRT-Connect, PRM, FMT*) - Implement hybrid A* for car-like robots - Set up lattice-based planners - Configure trajectory optimization (TrajOpt, CHOMP, STOMP) - Implement time-optimal trajectory planning - Set up path smoothing algorithms - Configure state space and validity checking - Implement kinodynamic planning - Set up multi-query planning with roadmaps - Configure asymptotically optimal planners ## Target Processes - path-planning-algorithm.js - trajectory-optimization.js - moveit-manipulation-planning.js - nav2-navigation-setup.js ## Dependencies - OMPL (Open Motion Planning Library) - MoveIt - TrajOpt - FCL (Flexible Collision Library) ## Usage Context This skill is invoked when processes require path planning algorithm selection, trajectory optimization, or custom motion planning solutions. ## Output Artifacts - OMPL planner configurations - State space definitions - Validity checker implementations - Trajectory optimization set
- Overview
- Capabilities
- Target Processes
- Dependencies
- Usage Context
- Output Artifacts
What does the Motion Planning Skill skill do?
Sampling-based and optimization-based motion planning algorithms
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill motion-planning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.