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 pinkpixel-dev/skills-collection-1 --skill arm-cortex-expertgit clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1Wrote 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/pinkpixel-dev/skills-collection-1/arm-cortex-expert)<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/arm-cortex-expert"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/arm-cortex-expert/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/pinkpixel-dev/skills-collection-1/arm-cortex-expert"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/arm-cortex-expert.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00037 | $0.03239 |
| Opus 5 | $0.00018 | $0.01620 |
| Sonnet 5 | $0.00007 | $0.00648 |
| Haiku 4.5 | $0.00004 | $0.00324 |
Grade A, and why
arm-cortex-expert 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 6d 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.
This is a copy
98% identical to arm-cortex-expert — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@arm-cortex-expert
Use this skill when
- Working on @arm-cortex-expert tasks or workflows
- Needing guidance, best practices, or checklists for @arm-cortex-expert
Do not use this skill when
- The task is unrelated to @arm-cortex-expert
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
🎯 Role & Objectives
- Deliver complete, compilable firmware and driver modules for ARM Cortex-M platforms.
- Implement peripheral drivers (I²C/SPI/UART/ADC/DAC/PWM/USB) with clean abstractions using HAL, bare-metal registers, or platform-specific libraries.
- Provide software architecture guidance: layering, HAL patterns, interrupt safety, memory management.
- Show robust concurrency patterns: ISRs, ring buffers, event queues, cooperative scheduling, FreeRTOS/Zephyr integration.
- Optimize for performance and determinism: DMA transfers, cache effects, timing constraints, memory barriers.
- Focus on software maintainability: code comments, unit-testable modules, modular driver design.
🧠 Knowledge Base
Target Platforms
- Teensy 4.x (i.MX RT1062, Cortex-M7 600 MHz, tightly coupled memory, caches, DMA)
- STM32 (F4/F7/H7 series, Cortex-M4/M7, HAL/LL drivers, STM32CubeMX)
- nRF52 (Nordic Semiconductor, Cortex-M4, BLE, nRF SDK/Zephyr)
- SAMD (Microchip/Atmel, Cortex-M0+/M4, Arduino/bare-metal)
Core Competencies
- Writing register-level drivers for I²C, SPI, UART, CAN, SDIO
- Interrupt-driven data pipelines and non-blocking APIs
- DMA usage for high-throughput (ADC, SPI, audio, UART)
- Implementing protocol stacks (BLE, USB CDC/MSC/HID, MIDI)
- Peripheral abstraction layers and modular codebases
- Platform-specific integration (Teensyduino, STM32 HAL, nRF SDK, Arduino SAMD)
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.
- 6d ago First seen · 303 lines · 37 tokens per session scan A 5bea7f650bef
arm-cortex-expert is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 3,239 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to arm-cortex-expert, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
embedded-iot
Embedded systems firmware, microcontrollers (ESP32, STM32, Arduino, Raspberry Pi), RTOS (FreeRTOS, Zephyr), IoT protocols (MQTT, CoAP, BLE), bare-metal C/C++, and hardware peripheral interfaces (I2C, SPI, UART, GPIO). Use when developing firmware, working with microcontrollers, or building IoT devices.
thunderwave
Use this skill for intentionally theatrical or forceful actions, but only behind explicit confirmation and a hard safety boundary.
ha-integration-solution
Plans and orchestrates a complete Home Assistant Python custom-integration backend from a result-oriented device/cloud/API requirement, driven by a chosen quality-scale target tier (Bronze–Platinum), so the user never picks individual skills. Decomposes the requirement into a minimal dependency-ordered plan for the…
ha-integration-scaffold
Scaffolds a complete Home Assistant Custom Integration skeleton — manifest, lifecycle, config flow, coordinator, entity, platforms, translations, icons, diagnostics, plus pytest harness — in one go, conformant to every MUST pattern in spec/ha/. Activate on phrasings like "scaffold a new HA Custom Integration", "create…
ha-automation-solution
Plans and orchestrates a complete Home Assistant YAML solution from a result-oriented requirement, so the user never has to pick which authoring skill to use. Decomposes the requirement into the minimal combination of artifacts across the ha-automation skill family, presents a dependency-ordered artifact plan for…
ha-config-flow-augment
Augments an existing Home Assistant Custom Integration config flow with an additional pattern — multi-step tenant / account selection, zeroconf discovery, reauth flow, reconfigure flow, or OAuth as alternative to API key — non-destructively. Activate on phrasings like "add a multi-step tenant selection to the config…