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…
Install / Use
npx skills add matlab/simulink-agentic-toolkit --skill resolve-design-errorsInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of resolve-design-errors
resolve-design-errors scores 90/100 on our quality scale, 1253rd of 4,615 Development & Engineering skills we index (top 28%).
Its SKILL.md is 8.4 KB long, well organised into 11 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.
With 1,148 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 16 days ago, so resolve-design-errors is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
resolve-design-errors compared with similar skills
All 4 of these similar skills score higher than resolve-design-errors; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| resolve-design-errors (this skill)by matlab | 90 | 1.1k | 16d ago | SKILL.md |
| ai-job-searchby MadsLorentzen | 100 | 44.9k | 1d ago | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 3d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 11d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 11d ago | SKILL.md |
Frequently asked questions
- How do I install resolve-design-errors?
- Run
npx skills add matlab/simulink-agentic-toolkit --skill resolve-design-errors. The install tabs above show the steps for each supported agent. - Which AI agents does resolve-design-errors work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is resolve-design-errors safe to use?
- It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is resolve-design-errors still maintained?
- The repository was last updated 16 days ago, so resolve-design-errors is actively maintained.
Skill content
View source on GitHubname: resolve-design-errors description: 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. license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md metadata: author: MathWorks version: "1.3" toolbox_dependencies: - Simulink Design Verifier - Simulink Check
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 (
.matfile) 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
- Never patch the original model directly. Always clone first before applying any fix.
- 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.
- 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.
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.
rca = sldv_find_de_root_cause("my_model", DedResult=result.DedResult, OutputDir="artifacts/ded")
Then trace each finding to its root cause:
- Follow the counterexample through
rca.SliceBlocksto find which block produces the defect-triggering value - Use
model_overview/model_readto understand each block's role - Prefer high-
Localityblocks (narrow blast radius, safer) and high-FindingCountblocks (fix resolves more defects); checkrca.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— amodel_readdump of the block's surrounding scope. You do not need to callmodel_readagain for this. (If empty — model_read was unavailable — fall back tomodel_overview/model_readyourself 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:
- Look at the root cause block — what value does it produce that makes the branch dead?
- Compare the model context against the patterns in
PatternCatalog. - 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
| Mistake | Fix |
|---------|-----|
| 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 DataFile | Pass one of the two — check result.DedResult from the checker |
| Running on an unsaved model | Save first; DED needs a file on disk |
| Applying fixes without presenting findings to user | Always show root cause analysis results first, get approval before fixing |
| Stopping at a shallow slice | Fall back to model_read / model_overview (Step 2) |
| Running on large models without OutputDir | Set OutputDir to avoid temp-dir clutter |
Copyright 2026 The MathWorks, Inc.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
