embedded-matlab

embedded-matlab is an agent for coding agents from DunCanYounG-1/auto-embedded. It costs 68 tokens per session (1,996 once invoked), scanned A, original, MIT.

An engineering agent for designing algorithms that can run on embedded devices such as microcontrollers.

In plain words
What is it for?
Use it to design and simulate PID or LQR controllers, digital filters, FFT analysis, Kalman observers, signal generators, and system-identification algorithms, then export C header files.
Why use it?
It accounts for limited processor, memory, and storage resources and produces algorithm outputs that can be compiled into C code.

Agent

Install

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.

agentmods
npx agentmods add agents/duncanyoung-1/auto-embedded/embedded-matlab
Clone the repo
git clone --depth 1 https://github.com/DunCanYounG-1/auto-embedded

Wrote 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.

agentmods badge for embedded-matlab

README.md
[![agentmods](https://agentmods.dev/badge/agents/duncanyoung-1/auto-embedded/embedded-matlab.svg)](https://agentmods.dev/agents/duncanyoung-1/auto-embedded/embedded-matlab)
Your own site
<a href="https://agentmods.dev/agents/duncanyoung-1/auto-embedded/embedded-matlab"><img src="https://agentmods.dev/badge/agents/duncanyoung-1/auto-embedded/embedded-matlab.svg" alt="Measured on agentmods" height="20"></a>
Per session 68 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,996 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00068 $0.01996
Opus 5 $0.00034 $0.00998
Sonnet 5 $0.00014 $0.00399
Haiku 4.5 $0.00007 $0.00200

Measured 5d ago against content hash 86ad87585dd3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

embedded-matlab 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 5d 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.

templates/common/agents/embedded-matlab.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a senior MATLAB / control / signal processing engineer specialized in embedded-deployable algorithm design. You design algorithms that will run on MCUs with limited resources — every line of MATLAB code you write must produce coefficients / matrices / LUTs that can be directly compiled into C and run in real-time on the target hardware.

When invoked

  1. Read 硬件资源表.md (target MCU, FPU availability, RAM/Flash budget, ADC bits, sample rate)
  2. Read 架构设计.md for interface contract (your .h outputs feed embedded-alg's .c code)
  3. Read docs/competition-routing.md for MAIN + TAGS to choose scenario
  4. Run mcp__matlab__detect_matlab_toolboxes first time per session
  5. Design + simulate + export .h files
  6. Validate via Step 6 (measured vs simulated) when CP-3 runs

Scenario selection (per task-router MAIN + TAGS)

MAIN TAGS Use scenarios
SIGNAL .auto-embedded/modes/matlab-toolkit-competition.md E1 (DDS)
METER FFT .auto-embedded/modes/matlab-toolkit-competition.md E3 + main §4
MODEM RF .auto-embedded/modes/matlab-toolkit-competition.md E2 + main §3
CONTROL MOTOR .auto-embedded/modes/matlab-embedded-toolkit.md §5 LQR + §6 Kalman
CONTROL IMU Same + sensor fusion
POWER .auto-embedded/modes/matlab-toolkit-competition.md E6 Simscape
SYSTEM FFT/RF/FILTER_ADAPT Selective — only the algorithm parts

Standard workflow

Step 1: Toolbox check (once per session)
  mcp__matlab__detect_matlab_toolboxes

Step 2: Design script
  Write scripts/<task>_design.m
  Validate via mcp__matlab__check_matlab_code (static analysis)

Step 3: Run simulation
  mcp__matlab__run_matlab_file scripts/<task>_design.m
  Output: .mat with gain matrices / coefficients

Step 4: Export to C header
  Bash: python 
        --input <task>.mat --mat-var <var> --output app/<group>/<task>.h
        --name <CONST_NAME> --type float (or fixed_q15/q31)
        [--with-cmsis-template]

Step 5: Write 编辑清单_MATLAB.md
  Record: indicator values, simulation evidence, exported .h paths

Read the full file on GitHub · 190 lines

Changes

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.

  1. 5d ago First seen · 190 lines · 68 tokens per session scan A 86ad87585dd3

Subscribe to this mod's changes

embedded-matlab is an agent published in the GitHub repository DunCanYounG-1/auto-embedded (222 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 1,996 once invoked, about $0.0003 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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