Agent skill · Data & Analytics

simulink-single-precision-conversion

Converts a double-precision Simulink system or subsystem to single precision using DataTypeWorkflow.Single (Fixed-Point Designer). The single conversion replaces all user-specified double-precision data types, as well as output data types that compile to double precision, with single-precision data types. Use this skill when converting Simulink systems to single precision, reducing memory usage of a Simulink system, optimizing for embedded targets. Do NOT use for standalone MATLAB .m code single conversion.

matlabgithub.com/matlabGitHub ↗
claude-codecodexcopilotNOASSERTION
Install
npx skills add matlab/simulink-agentic-toolkit --skill simulink-single-precision-conversion --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 6 KB
Bundled scripts: none
Version: 1.7
Declared author: MathWorks
Path: skills-catalog/code-generation/simulink-single-precision-conversion/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 903
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Single-Precision Conversion for Simulink Models (Fixed-Point Designer) Toolbox: **Fixed-Point Designer** Function: `DataTypeWorkflow.Single.convertToSingle` Converts user-specified double-precision data types — across block settings, Stateflow chart settings, signal objects, and bus objects — to single precision. **Boolean, built-in integer (int8, uint8, int16, …), and fixed-point types are left unchanged.** ## When to Use - Converting a Simulink model (`.slx`) or subsystem from double-precision to single-precision - Reducing memory footprint for embedded hardware with native single-precision support - Batch/CI conversions, checking conversion compatibility, or verifying no stowaway doubles remain - Summarizing the single conversion compatibility report and conversion result for the user in plain language ## When NOT to Use - **Standalone MATLAB `.m` code** — use the `convertToSingle` function with `coder.config('single')` instead - **Fixed-point data type optimization** — use `DataTypeWorkflow.Converter` instead --- ## Public API ```matlab ConversionReport = DataTypeWorkflow.Single.convertToSingle(systemToConvert) ``` - `systemToConvert` (char) — full block path of the **loaded*

What's inside
Steps it walks through
  1. When to Use
  2. When NOT to Use
  3. Public API
  4. Rules
  5. Report Structure
  6. report.CheckInfo
  7. report.ConvertInfo
  8. report.VerifyInfo
  9. Generating the Conversion Report
  10. Prerequisites & Limitations
Ships with 3 files
  • manifest.yaml
  • references/edge-cases.md
  • references/reporting-format.md
More from simulink-agentic-toolkit
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About this skill
What does the simulink-single-precision-conversion skill do?

Converts a double-precision Simulink system or subsystem to single precision using DataTypeWorkflow.Single (Fixed-Point Designer). The single conversion replaces all user-specified double-precision data types, as well as output data types that compile to double precision, with single-precision data types. Use this skill when converting Simulink systems to single precision, reducing memory usage of a Simulink system, optimizing for embedded targets. Do NOT use for standalone MATLAB .m code single conversion.

How do I install it?

Run `npx skills add matlab/simulink-agentic-toolkit --skill simulink-single-precision-conversion --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From matlab/simulink-agentic-toolkit, a repository with 903 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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