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Resolve Design Errors

OrganizationPopular
matlab
resolve-design-errors

Use when asked to run Design Error Detection (quick defect scan), find design errors in a Simulink model, perform root cause analysis on DED findings, fix division-by-zero, overflow, dead logic or out-of-bounds defects detected by SLDV, or diagnose why missing coverage cannot be achieved (dead logic blocking coverage objectives). Do NOT use for requirement verification, test generation, Inf/NaN detection, active logic analysis, or coverage measurement.

Overview

Publishermatlab
Repositorysimulink-agentic-toolkit
Skill nameresolve-design-errors
Stars
1.2K
Forks
112
Bundled files
27
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.

  • 27 bundled files

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

  • Open source

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

Installation

Install the Resolve Design Errors 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.
    https://github.com/matlab/simulink-agentic-toolkit/tree/main/skills-catalog/verification-validation-and-test/resolve-design-errors
  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/matlab/simulink-agentic-toolkit.git /tmp/simulink-agentic-toolkit
mkdir -p .claude/skills
cp -r /tmp/simulink-agentic-toolkit/skills-catalog/verification-validation-and-test/resolve-design-errors .claude/skills/resolve-design-errors
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Resolve Design Errors 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 Resolve Design Errors 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 Resolve Design Errors 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.

Detecting Design Errors (SLDV DED + Root Cause Analysis)

When to Use

  • User asks to check a Simulink model for design errors (division-by-zero, overflow, dead logic, out-of-bounds)
  • User asks to find root causes of DED findings
  • User asks to fix or understand an analysis finding
  • User asks why there is missing coverage due to dead logic (dead logic gates prevent coverage objectives from being satisfied)
  • User has SLDV artifacts (.mat file) and wants analysis without re-running DED

When NOT to Use

  • Requirement verification — checking if a model satisfies a requirement
  • Test generation or test authoring — creating test cases from requirements or for coverage
  • Comprehensive verification — this skill is a quick defect scan, not an exhaustive proof; a clean result does not guarantee the model is free of all errors
  • Coverage measurement — this skill does not measure or report model coverage; use Simulink Coverage tools

Safety Rules

  1. Never patch the original model directly. Always clone first before applying any fix.
  2. NEVER apply fixes without explicit user approval. After root cause analysis, present findings and suggest a fix strategy — then STOP and wait for the user to say "yes, apply it" or "go ahead." Even if the user's prompt says "suggest a fix" or "fix it," you must present the plan first and wait for confirmation. Do NOT create clone models, set parameters, or run verification until the user explicitly approves.
  3. SLDV results are always sound. Never assume SLDV returns false positives. If SLDV reports a defect, it is a real defect — treat every finding as a true positive and investigate accordingly.

Requires: MATLAB R2023b+, Simulink Design Verifier, Simulink Check / Model Slicer.

Prerequisites

All script functions live in the skill's scripts/ directory. Use evaluate_matlab_code with project_path set to that folder so MATLAB can find them.


Workflow

This skill provides two functions that automate SLDV-driven analysis the agent otherwise gets wrong when hand-rolling it, then hands you the facts to classify and fix findings. The workflow is four steps:

1. Detect errors      → sldv_run_defect_checker   (use the function; don't hand-roll DED)
2. Root cause errors  → sldv_find_de_root_cause    (use the function; don't hand-roll slicing)
3. Classify errors    → agent step (dead logic: intentional vs. design_error)
4. Fix errors         → agent step (propose, get approval, clone-fix-verify)

Steps 1–2 are the two functions. Steps 3–4 are agent judgment based on their output. Full API detail (return fields, options, caching, sub-functions) is in references/api-reference.md — load it when you need exact fields or options.

Step 1 — Detect errors

Call sldv_run_defect_checker(model) instead of writing your own SLDV DED invocation or parsing objectives by hand — it configures the analysis, runs DED, and auto-loads cached *_sldvdata.mat results when available.

matlab
result = sldv_run_defect_checker("my_model", OutputDir="artifacts/ded")

If result.Status == "pass": STOP. Report "No design errors of the checked types were detected" and end the workflow. Do NOT call sldv_find_de_root_cause or investigate further. DED is a quick scan, not an exhaustive proof — tell the user no defects of the checked types were found, not that the model is error-free.

Proceed to Step 2 only when result.Status == "fail".

Step 2 — Root cause errors

Call sldv_find_de_root_cause(model, DedResult=result.DedResult) instead of hand-building slices — it returns backward slices, counterexamples, locality (blast-radius) measures, and shared-root cascade annotations for every finding.

matlab
rca = sldv_find_de_root_cause("my_model", DedResult=result.DedResult, OutputDir="artifacts/ded")

Then trace each finding to its root cause:

  1. Follow the counterexample through rca.SliceBlocks to find which block produces the defect-triggering value
  2. Use model_overview / model_read to understand each block's role
  3. Prefer high-Locality blocks (narrow blast radius, safer) and high-FindingCount blocks (fix resolves more defects); check rca.Cascades — a shared-root fix resolves multiple findings at once

When the slice is shallow (< 3 blocks) or stops at a Stateflow / MATLAB Function block: the Model Slicer cannot trace through those constructs. Do NOT stop and report only what the tool returned — fall back to model_read / model_overview to interpret the finding: read the defect block and its upstream connections, read the Stateflow chart or MATLAB Function logic the slice stopped at, and cross-reference counterexample values to see which branch is active.


Step 3 — Classify errors (dead logic)

Classification happens after root cause analysis — you need to see the root cause (which block, what value) before deciding whether dead logic is intentional.

Findings arrive pre-enriched. For every dead logic finding, sldv_find_de_root_cause attaches on the finding struct:

  • finding.ModelContext — a model_read dump of the block's surrounding scope. You do not need to call model_read again for this. (If empty — model_read was unavailable — fall back to model_overview / model_read yourself for that block.)
  • finding.PatternCatalog — the entire dead-logic pattern library (catalog index + every pattern), concatenated. You do not need to open the YAML files yourself.
  • finding.Classification — "pending", awaiting your decision.

How to classify. For each dead logic finding, using ModelContext and PatternCatalog:

  1. Look at the root cause block — what value does it produce that makes the branch dead?
  2. Compare the model context against the patterns in PatternCatalog.
  3. Decide: is the block INTENTIONALLY producing this value (safety guard, disabled feature, complementary Stateflow guards) or is it a BUG (wrong parameter, cascading error)?

Set the classification:

  • "intentional" — defensive logic, enable-as-input, negation guard pairs → no fix needed; report to the user as expected behavior
  • "design_error" — wrong parameter, cascading dead logic, short-circuit → fix the root cause
  • "unclassified" — unclear → ask the user

Only apply fixes for "design_error" findings.


Step 4 — Fix errors

The functions provide facts; you identify root causes and decide fixes — there is no hardcoded defect-to-fix mapping.

When you are ready to propose or apply a fix, load and follow references/fix-strategy.md. It covers root-cause identification, the clone-fix-verify procedure, fix principles, and the blast-radius discussion required for every proposal.

Two rules that always apply (see Safety Rules): present findings and wait for explicit approval before applying anything, and propose a fix for every design_error finding — 2–3 ranked options each when possible — rather than stopping after only some.


Common Mistakes

MistakeFix
Continuing after Status == "pass"STOP. Zero falsified objectives = no defects. Do not call sldv_find_de_root_cause or investigate further. Report "no defects found" and end.
Calling sldv_find_de_root_cause without DedResult or DataFilePass one of the two — check result.DedResult from the checker
Running on an unsaved modelSave first; DED needs a file on disk
Applying fixes without presenting findings to userAlways show root cause analysis results first, get approval before fixing
Stopping at a shallow sliceFall back to model_read / model_overview (Step 2)
Running on large models without OutputDirSet OutputDir to avoid temp-dir clutter

Copyright 2026 The MathWorks, Inc.


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 Resolve Design Errors AI skill do?

Use when asked to run Design Error Detection (quick defect scan), find design errors in a Simulink model, perform root cause analysis on DED findings, fix division-by-zero, overflow, dead logic or out-of-bounds defects detected by SLDV, or diagnose why missing coverage cannot be achieved (dead logic blocking coverage objectives). Do NOT use for requirement verification, test generation, Inf/NaN detection, active logic analysis, or coverage measurement.

Why use Resolve Design Errors on TypingMind?

Because you install it once and use it with any model. Resolve Design Errors 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 Resolve Design Errors in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matlab/simulink-agentic-toolkit/tree/main/skills-catalog/verification-validation-and-test/resolve-design-errors. 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 Resolve Design Errors?

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 Resolve Design Errors?

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

Is the Resolve Design Errors AI skill free?

It is published on GitHub by matlab. 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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