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 agentmods add skills/d-robotics/moss/rpi-knowledgenpx skills add D-Robotics/moss --skill rpi-knowledgegit clone --depth 1 https://github.com/D-Robotics/mossWrote 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/d-robotics/moss/rpi-knowledge)<a href="https://agentmods.dev/skills/d-robotics/moss/rpi-knowledge"><img src="https://agentmods.dev/badge/skills/d-robotics/moss/rpi-knowledge.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00296 | $0.03077 |
| Opus 5 | $0.00148 | $0.01538 |
| Sonnet 5 | $0.00059 | $0.00615 |
| Haiku 4.5 | $0.00030 | $0.00308 |
Grade A, and why
rpi-knowledge 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 4d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Raspberry Pi Knowledge
This skill is about the Raspberry Pi platform itself: board specs, the RP1 I/O chip, GPIO library choice, the camera stack, and where AI inference actually runs. The single fact that trips up the most people: on a Raspberry Pi 5 the GPIO pins live on the RP1 chip, not the main SoC — so any library that pokes /dev/mem registers directly (classic RPi.GPIO, wiringPi) silently fails or errors. Catch that first whenever GPIO "doesn't work on Pi 5."
Sources: official raspberrypi.com documentation and product pages (Pi 5 / Pi 4B / CM4 spec pages, the camera-software docs, the GPIO best-practices white paper), verified against the source for every spec line. Nothing invented.
The two facts that matter most
- Pi 5 GPIO moved off-SoC into RP1. On Pi 4B and earlier the GPIO controller is inside the BCM SoC and old code memory-maps its registers. On Pi 5 the 40-pin header is driven by the RP1 southbridge over PCIe, so those register mappings no longer exist. Classic
RPi.GPIO(andwiringPi) do not work on Pi 5. Use the character-device path:gpiozero(official recommendation,lgpiobackend) →lgpio/libgpiodfor lower level →rpi-lgpioas a drop-in shim for legacyRPi.GPIOcode. - No Raspberry Pi has an on-board NPU. Pi 5/4B/CM4 run AI on the CPU (ONNX Runtime / TFLite / PyTorch aarch64). For real acceleration you add a Hailo AI HAT+ (PCIe, Pi 5 only) and run HEF models. Don't promise NPU performance the board doesn't have.
Board decision cheat-sheet
Confirm the board first (cat /proc/device-tree/model), then everything follows. All three current boards are 0 TOPS (no NPU) and run AI on CPU unless a HAT accelerator is added.
| Board | SoC | CPU | RAM variants | RP1? | GPIO lib | AI path |
|---|---|---|---|---|---|---|
| Raspberry Pi 5 | BCM2712 | Cortex-A76 @2.4GHz | 1/2/4/8/16GB LPDDR4X | Yes | gpiozero / lgpio (NOT RPi.GPIO) | CPU; or Hailo AI HAT+ (HEF) |
| Raspberry Pi 4 Model B | BCM2711 | Cortex-A72 @1.8GHz | 1/2/4/8GB LPDDR4 | No | gpiozero / lgpio / RPi.GPIO still works | CPU / ONNX / TFLite |
| Compute Module 4 | BCM2711 | Cortex-A72 @1.5GHz | 1/2/4/8GB LPDDR4-3200 | No | gpiozero / lgpio / RPi.GPIO still works | CPU / ONNX / TFLite |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 104 lines · 296 tokens per session scan A b28bfde763bb
rpi-knowledge is a skill published in the GitHub repository D-Robotics/moss (142 stars, last pushed 8d ago), licensed MIT. It adds 296 tokens to every session and 3,077 once invoked, about $0.0015 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.
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