matlab-analyze-data
Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.
npx skills add matlab/matlab-agentic-toolkit --skill matlab-analyze-data --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables.
How it works
The skill describes using core MATLAB functions for tabular and time-series workflows. It emphasizes: understanding data with summary(T) and related commands for orientation; using modern types (datetime, string, categorical); working with tables and timetables via functions like fillmissing, isoutlier, retime, synchronize, lag, timerange, and related table utilities. It provides code patterns for missing data handling, outlier detection, range checks, aggregation, and data transformation, including examples for filtering, sorting, and binning. It also advises to read reference files for syntax and pitfalls before coding and to prefer newer functions (e.g., groupsummary, datetime, fillmissing) over legacy ones.
When to use it
- Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables
- Time-series analysis: resampling, synchronizing, trend detection, smoothing
- Answering questions about data in tables (top-N, filtering, group comparisons)
- Data cleaning: missing values, outliers, type conversion, normalization
What it can touch
Data touched includes tables and timetables (RowTimes/Time variables), numeric arrays, and other tabular representations. It references functions and methods like paddata, trimdata, resize, clip, fillmissing, isoutlier, isbetween, group aggregation with groupsummary, and timetable operations like retime, synchronize, lag, timerange, and eventtable utilities. The skill guides using DataVariables, vartype, and specific table expressions, and discusses applying methods per variable.
Caveats
License is linked; the skill notes which functions are available by MATLAB release (e.g., R2023a+ for some features, newer for others). It cautions using reference material to avoid deprecated patterns and mentions potential pitfalls with certain patterns (e.g., avoiding some legacy functions).
# MATLAB Data Analysis Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables. ## When to Use - Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables - Time-series analysis: resampling, synchronizing, trend detection, smoothing - Answering questions about data in tables (top-N, filtering, group comparisons) - Data cleaning: missing values, outliers, type conversion, normalization ## When NOT to Use - The task has no tabular data context (no tables, timetables, or structured datasets) - The primary goal is visualization or plotting, not data analysis - The task is purely symbolic math, simulation, or app building This skill covers core MATLAB functions for tabular and time-series workflows. These functions work natively with `table` and `timetable`, handle missing data correctly, and are performance-optimized. Prefer the modern functions recommended here (e.g., `groupsummary`, `datetime`, `fillmissing`) over legacy alternatives (e.g., `accumarray`, `nanmean`, `datenum`). Override only if the user explicitly requests otherwise. **Before writing code, read the reference file linked at the end of the relevant secti
- When to Use
- When NOT to Use
- Key Functions — Available From
- Getting Oriented with Data
- Data Types
- Tables and Timetables
- Data Cleaning
- Missing values
- Outliers and range checking
- Aggregation statistics and missing values
- Data Transformation
- Row filtering and sorting
- Binning
- Normalization and scaling
What does the matlab-analyze-data skill do?
Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.
How do I install it?
Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-analyze-data --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/matlab-agentic-toolkit, a repository with 868 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.