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Ironcore

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GadaaLabs
ironcore

State machines, ISRs, RTOS, hardware abstraction, and timing analysis — domain expertise for electrical and embedded systems engineers

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill nameironcore
Stars
67
Forks
10
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GadaaLabs on GitHub. Read the source before you install it.

Installation

Install the Ironcore AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/ironcore .claude/skills/ironcore
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ironcore in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ironcore on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ironcore is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Embedded Systems Patterns

Overview

IRONCOREIron is hard, precise, and unforgiving — exactly like embedded systems. When invoked: assesses hardware context (bare-metal / RTOS / HAL / ISR), loads the relevant pattern file, and enforces embedded discipline — deterministic timing, ISR safety, lock-free queues, type-safe register access.

Core principle: Embedded systems have zero margin for error — timing violations, race conditions, and memory corruption cause real-world failures. Design for determinism and verifiability.

Announce at start: "Running IRONCORE for embedded systems patterns."


Entry Point — First 5 Minutes

HARDWARE CONTEXT ASSESSMENT:

"What platform and RTOS?"
A) Bare-metal (no RTOS) — Cortex-M, AVR, PIC
B) FreeRTOS / Zephyr / ThreadX
C) Linux embedded (Yocto, Buildroot)
D) FPGA / HDL
E) Mixed (Linux + MCU co-processor)

"What is failing or being designed?"
1) State machine / control flow
2) ISR / interrupt handling
3) RTOS task design / scheduling
4) Hardware register / peripheral driver
5) Timing / real-time requirements
6) Communication protocol (SPI, I2C, UART, CAN)
7) Memory / stack corruption

Context → Section mapping:

  • Any + 1 → State Machine Design (patterns/state-machines.md)
  • Any + 2 → ISR Safety Rules (patterns/isr-safety.md)
  • B + 3 → RTOS Task Decomposition (patterns/rtos-tasks.md)
  • Any + 4 → Hardware Register Abstraction (patterns/hardware-abstraction.md)
  • Any + 5 → Deadline Analysis (patterns/rtos-tasks.md — WCRT section)
  • Any + 7 → Stack Size Estimation + run hunter

Critical first question for ALL embedded work: "Is this hard real-time (missed deadline = failure) or soft real-time (missed deadline = degraded performance)?"


State Machine Design

Load patterns: patterns/state-machines.md

Required elements:

  1. HSM structure — entry/exit actions, parent states for hierarchy
  2. Transition table — explicit (from, event, to, action) rows — no implicit transitions
  3. Guard conditions — typed guards, not raw conditionals inside handlers
  4. Tests — transition coverage, no unreachable states

Rule: All events must be handled in all states (even if the handler is explicit ignore).


ISR Safety Rules

Load patterns: patterns/isr-safety.md

Non-negotiable rules:

  1. Minimal ISR work — read hardware register, push to buffer, set flag. Nothing else.
  2. volatile for all ISR-shared variables — compiler cannot cache these
  3. Memory barriers__DMB() before setting flags, before reading data
  4. Lock-free queues for ISR→main — SPSC queue, no mutex (mutexes block ISRs)
  5. No malloc/free in ISRs — ever

RTOS Task Decomposition

Load patterns: patterns/rtos-tasks.md

Steps:

  1. Identify tasks by rate: control loop, communication, UI, logging
  2. Assign priorities by rate monotonic: shorter period = higher priority
  3. Verify schedulability: Σ(WCET/period) ≤ n(2^(1/n)−1) (for n=3: ≤ 0.78)
  4. Measure stack usage with watermark pattern, add 25% margin
  5. Choose IPC — queue for data, semaphore for events, mutex for shared resources

Priority inversion: Always use xSemaphoreCreateMutex() (has priority inheritance), never binary semaphore for resource protection.


Hardware Register Abstraction

Load patterns: patterns/hardware-abstraction.md

Required:

  1. Type-safe register structs — volatile fields, bitfield macros (REG_SET/GET/MASK)
  2. MMIO safety — NULL check, alignment check, __DMB() after writes
  3. Endianness macros — HTONS/HTONL/NTOHS/NTOHL for all network/protocol data
  4. Timing checklist — clock freq, setup/hold times, interrupt latency, watchdog timeout

Red Flags

Never:

  • Do blocking operations in ISR
  • Share ISR↔main data without volatile
  • Use malloc/free in ISRs or time-critical code
  • Ignore stack overflow potential
  • Skip deadline analysis for hard real-time tasks

Always:

  • Use memory barriers for ISR-main communication
  • Verify RMS utilization bound before deploying
  • Test state machines for unreachable states
  • Validate MMIO access bounds
  • Document timing requirements and verify them

Integration with Superpowers

SkillIntegration
forgeWrite hardware-in-loop tests first
hunterDebug timing violations, race conditions
sentinelVerify timing budgets before claiming success
chronicleStore hardware-specific patterns

Final Checklist

  • State machine has no unreachable states
  • ISR does minimal work (deferred processing used)
  • Lock-free data structures for ISR-main communication
  • Memory barriers in place
  • RMS utilization bound verified (≤ 0.78 for 3 tasks)
  • Stack sizes validated with watermark + 25% margin
  • WCRT analysis passes for all tasks
  • MMIO access validated and bounded
  • Endianness handled correctly
  • Timing requirements documented and verified

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ironcore AI skill do?

State machines, ISRs, RTOS, hardware abstraction, and timing analysis — domain expertise for electrical and embedded systems engineers

Why use Ironcore on TypingMind?

Because you install it once and use it with any model. Ironcore is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ironcore in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/ironcore. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ironcore?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ironcore?

As many as you like. As long as a model supports skills, you can use Ironcore with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ironcore AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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