Robotics Skills: 18 Fundamentals-First Advisors

Ask a general assistant "which IK solver should I use?" and you get a confident paragraph. It might name a library that was archived two years ago, quote a spec it half-remembers, and skip the textbook method that was the right answer for your 6-DOF arm. Robotics punishes that. The field is old enough to have deep fundamentals and fast enough that half the tooling you remember is stale.

These 18 skills fix both ends. Every answer starts from the classic method and why it works. Then it adds modern alternatives verified against live sources in the same conversation. Then it stops and lets you choose.

How each skill works

1. Frame. It pins down one decision at a time, in dependency order. Broad requests get split up and started upstream.
2. Fundamentals. It explains the classic method and why it's shaped that way, with terminology defined in plain language before the math.
3. Modern scan. It runs a live search on every invocation. Each skill ships a dated snapshot of its field with a source link on every entry, but treats it as a starting point to re-verify, never as the answer.
4. You choose. It offers 2-4 real options, always including the classic method, with one marked as recommended and the reason stated.
5. Apply, then loop. Your choice goes onto a running decision stack and the next decision comes up. If a new choice contradicts an earlier one, the skill stops and says so.

It has three modes. Guided (the default) handles one decision per turn. Fast-forward takes the recommended option and stops only for irreversible, budget, or hardware calls. Audit checks your existing setup against the sequence.

The skills

Cross-cutting:

• robotics-advisor: kinematics, dynamics, and control theory, grounded in Craig's Introduction to Robotics with page citations
• ros2-master: topics vs services vs actions, lifecycle nodes, QoS, executors, and ros2_control
• robot-perception: sensor choice, intrinsic and hand-eye calibration, 3D/6D perception, fusion, and time sync
• robot-safety: risk assessment, ISO 10218 and TS 15066, performance levels, e-stop architecture, and the certification path
• robot-sim: simulator choice, contact fidelity, domain randomization, and whether sim results predict hardware
• robot-learning: whether to learn at all, then demonstrations, policy class, evaluation, and a safety wrapper

Platforms:

• robot-arm: URDF → IK → hardware interface → MoveIt 2 → calibration → safety
• robot-hand: grippers to five-finger hands, covering grasp strategy, grip force, in-hand manipulation, and teleop retargeting
• robot-mobile: SLAM vs prebuilt maps, localization, Nav2 planners and costmaps, docking, and multi-floor
• robot-legged: quadrupeds and humanoids, covering balance criteria, gait planning, MPC vs RL, and loco-manipulation
• robot-aerial: multirotor vs VTOL, PX4 vs ArduPilot, GPS-denied flight, endurance budget, and failsafes
• robot-marine: ROV vs AUV vs USV, thruster allocation, underwater navigation, pressure, and corrosion
• robot-soft: soft and continuum robots, covering actuation, modeling limits, soft sensing, and when compliance wins

Systems and domains:

• robot-fleet: Open-RMF, traffic negotiation, shared lifts, doors, and chargers, and DDS discovery at scale
• robot-swarm: decentralized control, consensus, and when a swarm actually beats a coordinator
• robot-field: agriculture, construction, and inspection outdoors, RTK GNSS failure modes, and hardening
• robot-medical: surgical, rehab, and assistive robots, RCM mechanisms, pHRI, and the regulatory path
• robotics-radar: a maintenance sweep that re-verifies the snapshots and adds coverage for new robot types

You never need to remember which skill to call. Describe your robot, and the right skill triggers and hands off to its neighbors as the problem moves.

Why you'd trust it

• Nothing is presented unchecked. Every snapshot entry carries a live source URL, and answers pass that link through instead of asserting the fact bare.
• It catches stale-memory traps. For example, osrf/rmf_core, the Open-RMF repo most training data still cites, was archived in 2021 and development moved to the open-rmf org.
• It's measured honestly. We ran 14 blind-judged questions over 9 rounds. The latest round came out at parity (93% vs 93%): the skills cite about 20 points better and score about 25 points worse on a "fabrication" criterion that turned out to measure whether a judge without search could confirm a reference. All 29 cited paper IDs came from verified snapshots. The full write-up, including a retracted earlier headline, is in the repo's EVAL.md.

Open source (MIT): https://github.com/joey114132/claude-robotics-skills