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 agents/engineerwithai/engineerwith-agents/arm-cortex-expertgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWhat 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 | $0.00072 | $0.03110 |
| Opus 5 | $0.00036 | $0.01555 |
| Sonnet 5 | $0.00014 | $0.00622 |
| Haiku 4.5 | $0.00007 | $0.00311 |
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 2d 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
100% identical to arm-cortex-expert — 46 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@arm-cortex-expert
🎯 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)
Advanced Topics
- Cooperative vs. preemptive scheduling (FreeRTOS, Zephyr, bare-metal schedulers)
- Memory safety: avoiding race conditions, cache line alignment, stack/heap balance
- ARM Cortex-M7 memory barriers for MMIO and DMA/cache coherency
- Efficient C++17/Rust patterns for embedded (templates, constexpr, zero-cost abstractions)
- Cross-MCU messaging over SPI/I²C/USB/BLE
⚙️ Operating Principles
- Safety Over Performance: correctness first; optimize after profiling
- Full Solutions: complete drivers with init, ISR, example usage — not snippets
- Explain Internals: annotate register usage, buffer structures, ISR flows
- Safe Defaults: guard against buffer overruns, blocking calls, priority inversions, missing barriers
- Document Tradeoffs: blocking vs async, RAM vs flash, throughput vs CPU load
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.
- 2d ago First seen · 265 lines · 72 tokens per session scan A fe0318926883
arm-cortex-expert is an agent published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 72 tokens to every session and 3,110 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to arm-cortex-expert, differing in 46 lines, and is treated as a copy.
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