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Slci Inspection Incompatibility Analysis

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matlab
slci-inspection-incompatibility-analysis

Use when SLCI code inspection has failed due to compatibility issues and you need to diagnose the incompatibilities. Traces inspection failures back to root-cause incompatibility settings or known limitations by matching failed code lines against known incorrect code generation patterns and documented limitation scenarios. Hands off to slci-compatibility-analysis for fixes. Trigger keywords: inspect, inspection failed, trace inspection errors, why did inspection fail, SLCI verification failures, limitation.

Overview

Publishermatlab
Repositorysimulink-agentic-toolkit
Skill nameslci-inspection-incompatibility-analysis
Stars
1.2K
Forks
112
Bundled files
2
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.

  • 2 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 Slci Inspection Incompatibility Analysis 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/code-generation/slci-inspection-incompatibility-analysis
  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/code-generation/slci-inspection-incompatibility-analysis .claude/skills/slci-inspection-incompatibility-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Slci Inspection Incompatibility Analysis 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 Slci Inspection Incompatibility Analysis 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 Slci Inspection Incompatibility Analysis 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.

SLCI Inspection Incompatibility Analysis

Analyze SLCI code inspection results and trace failures back to compatibility constraint violations or known limitations by matching failed code lines against known incorrect code generation patterns and documented limitation scenarios.

When to Use

  • User asks why SLCI inspection failed or has verification errors
  • User wants to trace inspection failures to their root-cause settings
  • User has an inspection report with Failed/Error/Unverified items
  • User asks to diagnose code inspection issues before re-running
  • User wants to understand which compatibility settings caused inspection failure

When NOT to Use

  • User wants to check model compatibility before running inspection (no inspection failure context) — use slci-compatibility-analysis instead
  • User confirms the inspection failure is not related to a compatibility issue
  • User wants general Simulink model building or code generation help

Workflow

0. Clarify Intent

Ask the user whether the inspection failure might be related to a compatibility issue before taking any other action. Do not search for files or run MATLAB until the user confirms.

  • Yes → proceed to Step 1.
  • No → stop this skill.
  • Unsure → explain what compatibility issues are and ask again.

1. Locate the Inspection Report

Get the report folder from MATLAB:

matlab
c = slci.Configuration('<model_name>');
reportFolder = c.getReportFolder();

Look for <model_name>_report.html (or .pdf/.doc). If found, compare the model's timestamp against the report's — warn if the report may be stale.

If the report does NOT exist, stop and warn the user. Suggest c.inspect(). If the user explicitly asks to run inspection, first check whether generated code is stale or missing and regenerate if needed before inspecting.

Code regeneration commands (used throughout this skill):

  • Top model: rtwbuild('<model_name>')
  • Reference model: c.setTopModel(false); slbuild('<model_name>', 'ModelReferenceRTWTargetOnly')

Pre-inspection settings: If the user asks for advanced loop analysis support, set c.setApplyAdvancedLoopSupport(true) before calling c.inspect(). This enables verification of algebraic loop constructs that SLCI skips by default.

2. Parse the Inspection Report

Extract the text content and search for items with Failed, Error, or Unverified status, capturing 2 lines of context before and 5 lines after each match.

For each failed item, identify:

  • The code file and line number where verification failed
  • The block path the code maps to (from comments)
  • The failure reason (e.g., "unable to verify", "no traceability", "ambiguous mapping")

3. Read the Failed Code Lines

For each failed code location, read the generated C source file at the failed line, capturing 5–10 lines of surrounding context to understand the pattern.

Generated code is located at:

  • Top model: <model_dir>/<model_name>_ert_rtw/<source_file>.c
  • Reference model: <model_dir>/slprj/ert/<model_name>/<source_file>.c

For Stateflow failures, consult stateflow_failure_patterns.md for traceability comment format, merge junction patterns, and a quick-triage table before reading full constraint reference files.

4. Match Against Incorrect Code Generation Patterns

Read the constraint files from the slci-compatibility-analysis skill's references/ folder.

See the Constraint Categories table in slci-compatibility-analysis for the full list of constraint files and their categories.

For each failed code line, use the block path from its traceability comment to identify the block type, then compare the code against the constraint descriptions and Why explanations in the matching constraint files.

Critical: Verify applicability before declaring a match. A structural resemblance alone is NOT sufficient. For each candidate match, verify:

  1. Scope: Does the constraint apply to the specific block type in the failing model?
  2. Mechanism: Does the constraint's parameter actually control the observed code generation behavior? Read the Why section in the constraint entry to confirm.
  3. Contradictions: Check whether another constraint gives opposing guidance for the same scenario.

If no constraint satisfies all three checks, report the unmatched failures to the user and ask whether to proceed to Step 4b (limitation matching). If the user declines, classify those failures as UNMATCHED and include them in the report with the constraints that were considered and why each was ruled out. Do NOT check limitations without explicit user approval.

4b. Match Against Known Limitations (requires user permission)

Only run this step after the user has approved proceeding from Step 4.

Compare unmatched failures against known limitations in the slci-compatibility-analysis skill's references/code_inspection_limitations.md. Constraint violations take priority — only classify as LIMITATION if no constraint is a better explanation. If the failure matches neither a constraint nor a limitation, classify as UNMAPPED and document why each candidate was ruled out. Do NOT report a "closest match" as the root cause — an incorrect mapping is worse than an honest "unmapped."

5. Cross-Reference with Compatibility Report

Skip this step if inspection was terminated (e.g., missing headers or compile errors).

Use slci-compatibility-analysis to obtain or verify compatibility results. Cross-reference the compatibility warnings against the matched constraints to confirm the mapping. When a warning could map to multiple conditions, check the model's actual settings to identify the true trigger rather than relying on the warning text alone.

6. Present Results

In the context window, show only a brief summary table (columns: #, Location, Pattern, Root Cause, Type) and summary counts.

Save detailed results to: <model_dir>/<model_name>_inspection_analysis_YYYY-MM-DD.md

If constraint violations were found, ask the user if they want to apply fixes. If yes, delegate to the slci-compatibility-analysis skill to apply fixes, then regenerate code and re-run c.inspect(). Do not fix the model yourself — no set_param, no fixIncompatibilities, no model modifications of any kind.

If failures remain after fixes, re-run this analysis on the fixed model. If only UNMATCHED, UNMAPPED, or LIMITATION failures remain, inform the user that automatic fixing is not possible.

If no failed items are found, inform the user that all checks passed and save a brief report. If compatibility warnings exist despite passing, include a Warning Impact Analysis explaining why each warning did not cause a failure and under what conditions it could become one.

Guardrails

  • NEVER skip Step 0 (Clarify Intent). You must ask the user whether the failure is compatibility-related and receive confirmation before taking any diagnostic action. Do not search for files, run MATLAB, or read reports until the user confirms.
  • NEVER report a "closest match" as a root cause. An incorrect mapping is worse than an honest "unmapped." All three checks (scope, mechanism, contradictions) must pass before declaring a constraint match.
  • NEVER check limitations without explicit user permission. Step 4b requires the user to approve before proceeding.
  • NEVER apply fixes directly. This skill only diagnoses — it does not modify the model. Delegate all fix application to slci-compatibility-analysis. If that skill is unavailable, stop.

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 Slci Inspection Incompatibility Analysis AI skill do?

Use when SLCI code inspection has failed due to compatibility issues and you need to diagnose the incompatibilities. Traces inspection failures back to root-cause incompatibility settings or known limitations by matching failed code lines against known incorrect code generation patterns and documented limitation scenarios. Hands off to slci-compatibility-analysis for fixes. Trigger keywords: inspect, inspection failed, trace inspection errors, why did inspection fail, SLCI verification failures, limitation.

Why use Slci Inspection Incompatibility Analysis on TypingMind?

Because you install it once and use it with any model. Slci Inspection Incompatibility Analysis 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 Slci Inspection Incompatibility Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matlab/simulink-agentic-toolkit/tree/main/skills-catalog/code-generation/slci-inspection-incompatibility-analysis. 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 Slci Inspection Incompatibility Analysis?

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 Slci Inspection Incompatibility Analysis?

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

Is the Slci Inspection Incompatibility Analysis 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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