Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add arpitg1304/robotics-agent-skills --skill robotics-software-principlesgit clone --depth 1 https://github.com/arpitg1304/robotics-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arpitg1304/robotics-agent-skills/robotics-software-principles)<a href="https://agentmods.dev/skills/arpitg1304/robotics-agent-skills/robotics-software-principles"><img src="https://agentmods.dev/badge/skills/arpitg1304/robotics-agent-skills/robotics-software-principles/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arpitg1304/robotics-agent-skills/robotics-software-principles"><img src="https://agentmods.dev/badge/skills/arpitg1304/robotics-agent-skills/robotics-software-principles.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00140 | $0.07058 |
| Opus 5 | $0.00070 | $0.03529 |
| Sonnet 5 | $0.00028 | $0.01412 |
| Haiku 4.5 | $0.00014 | $0.00706 |
Grade A, and why
robotics-software-principles scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 897 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robotics Software Design Principles
Why Robotics Software Is Different
Robotics code operates under constraints that most software never faces:
- Physical consequences — A bug doesn't just crash a process, it crashes a robot into a wall
- Real-time deadlines — Missing a 1ms control loop deadline can cause oscillation or damage
- Sensor uncertainty — All inputs are noisy, delayed, and occasionally wrong
- Hardware diversity — Same algorithm must work on 10 different grippers from 5 vendors
- Sim-to-real gap — Code must run identically in simulation and on real hardware
- Long-running operation — Robots run for hours/days; memory leaks and drift matter
- Safety criticality — Some failures must NEVER happen, regardless of software state
These constraints demand disciplined design. Below are principles that account for them.
Principle 1: Single Responsibility — One Module, One Job
Every module (node, class, function) should have exactly ONE reason to change.
Why it matters in robotics: A perception module that also does control means a camera driver update can break your arm controller. In safety-critical systems, this coupling is unacceptable.
# ❌ BAD: God module — perception + planning + control + logging
class RobotController:
def __init__(self):
self.camera = RealSenseCamera()
self.detector = YOLODetector()
self.planner = RRTPlanner()
self.arm = UR5Driver()
self.logger = DataLogger()
def run(self):
image = self.camera.capture()
objects = self.detector.detect(image)
path = self.planner.plan(objects[0].pose)
self.arm.execute(path)
self.logger.log(image, objects, path)
# If ANY of these changes, you touch this class
# ✅ GOOD: Separated responsibilities with clear interfaces
class PerceptionModule:
"""ONLY responsibility: raw sensor data → detected objects"""
def __init__(self, camera: CameraInterface, detector: DetectorInterface):
self.camera = camera
self.detector = detector
def get_detections(self) -> List[Detection]:
image = self.camera.capture()
return self.detector.detect(image)
class PlanningModule:
"""ONLY responsibility: goal + world state → trajectory"""
def __init__(self, planner: PlannerInterface):
self.planner = planner
def plan_to(self, target: Pose, obstacles: List[Obstacle]) -> Trajectory:
return self.planner.plan(target, obstacles)
class ExecutionModule:
"""ONLY responsibility: trajectory → hardware commands"""
def __init__(self, arm: ArmInterface):
self.arm = arm
def execute(self, trajectory: Trajectory) -> ExecutionResult:
return self.arm.follow_trajectory(trajectory)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 897 lines · 140 tokens per session scan A d0a62379e726
robotics-software-principles is a skill published in the GitHub repository arpitg1304/robotics-agent-skills (355 stars, last pushed 29d ago), licensed Apache-2.0. It adds 140 tokens to every session and 7,058 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…
pylabrobot
Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.
urdf
URDF robot description authoring and validation. Use when creating, editing, inspecting, validating, or debugging .urdf files, robot links, joints, limits, inertials, visual/collision geometry, mesh references, frame conventions, or robot-description artifacts. Use the SRDF skill for MoveIt2 semantic groups and…
step-parts
Find, evaluate, and download common purchasable CAD parts from step.parts, including named off-the-shelf actuators, servos, motors, electronics boards, connectors, screws, bolts, nuts, washers, bearings, standoffs, and other catalog components. Use when Codex needs to search the hosted step.parts catalog before…
offensive-wifi
Wireless / 802.11 attack methodology for red team engagements and wireless security assessments. Covers monitor-mode setup, WPA/WPA2-PSK handshake capture and PMKID attacks, WPA3 SAE downgrade and Dragonblood, WPA-Enterprise (EAP) attacks (MSCHAPv2 cracking, EAP-TLS cert theft, evil-twin RADIUS), Karma / Known Beacons…