matlab-analyze-data

Name: matlab-analyze-data
Author: matlab

54installs105forks

How to install matlab-analyze-data?

matlab/matlab-agentic-toolkit/matlab-analyze-data

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-analyze-data

Installs into the current project.

Summary

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 answer

Files · 1

View on GitHub

SKILL.md

1	# MATLAB Data Analysis
2
3	Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables.
4
5	## When to Use
6
7	- Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables
8	- Time-series analysis: resampling, synchronizing, trend detection, smoothing
9	- Answering questions about data in tables (top-N, filtering, group comparisons)
10	- Data cleaning: missing values, outliers, type conversion, normalization
11
12	## When NOT to Use
13
14	- The task has no tabular data context (no tables, timetables, or structured datasets)
15	- The primary goal is visualization or plotting, not data analysis
16	- The task is purely symbolic math, simulation, or app building
17
18	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.
19
20	Before writing code, read the reference file linked at the end of the relevant section below. Reference files contain correct syntax, common pitfalls, and "Avoid" patterns that prevent silent bugs. Skipping the reference risks using a deprecated approach or hitting a known pitfall.
21
22	### Key Functions — Available From
23
24	Most functions in this skill are available in R2023a or earlier. The following require a newer release:
25
26	\| Function \| Available From \| Purpose \|
27	\|----------\|---------------\|---------\|
28	\| `paddata`, `trimdata`, `resize` \| R2023b \| Pad, trim, or resize arrays to target length \|
29	\| `clip` \| R2024a \| Clamp values to a range \|
30	\| `summary` (enhanced) \| R2024b \| Supports arrays (numeric, datetime, duration, logical); adds `Statistics`, `DataVariables`, `Detail` name-value args \|
31	\| `isapprox` \| R2024b \| Tolerance-aware floating-point comparison (use instead of `==` for computed values) \|
32	\| `isbetween` (numeric) \| R2024b \| Check elements within a numeric range \|
33	\| `numunique` \| R2025a \| Count distinct values in a variable \|
34	\| `allbetween` \| R2025a \| Validate all values are within a range \|
35	\| `allunique` \| R2025a \| Validate all values are unique \|
36
37	## Getting Oriented with Data
38
39	When data is already in a workspace variable, start by understanding its structure and contents. Use JSON output for reliable parsing — table display is designed for human-readable grids, but as text it is easy to misinterpret which values belong to which variables:
40
41	```matlab
42	jsonencode(summary(T)) % per-variable stats as nested struct
43	jsonencode(head(T)) % first 8 rows as structured JSON
44	```
45
46	When getting oriented with unknown data, `summary(T)` already contains per-variable type, size, NumMissing, and — for numeric/datetime/duration — Min, Max, Mean, Median, Std. For categoricals it includes category names and counts. This is usually sufficient for an initial overview.
47
48	If producing a standalone script, leave the semicolon off to invoke the `display` method — it shows dimensions, variable names, and a truncated preview. Avoid `disp` (omits headers and prints every row, flooding output on large tables) and `fprintf` in a loop (verbose, old-style):
49
50	```matlab
51	summary(T) % types, ranges, missing counts per variable
52	size(T) % [nRows, nVars]
53	sum(ismissing(T)) % missing count per variable
54	T % dimensions + header + truncated preview
55	```
56
57	For Pearson correlation, use `corrcoef` (base MATLAB) with `Rows="complete"` to handle NaN: `corrcoef(T{:,vartype("numeric")}, Rows="complete")`. For Kendall or Spearman rank correlation, use `corr` (requires Statistics Toolbox): `corr(X, Type="Spearman")`.
58
59	> Systematic exploration checklist (distributions, cardinality, duplicates, outlier screening, `groupcounts`, `corrcoef`, time-based checks): [exploration.md](references/exploration.md)
60
61	## Data Types
62
63	Use modern MATLAB types. These are faster, more readable, and work better with table functions.
64
65	\| Instead of \| Use \| Why \|
66	\|---\|---\|---\|
67	\| `datenum`, `datestr` \| `datetime` \| Proper arithmetic, timezone support \|
68	\| `char`, `cellstr`, `strcmp` \| `string`, `==`/`matches` \| `==` for scalar, `matches` for vector comparison \|
69	\| Numeric codes or strings with few unique values \| `categorical` \| Self-documenting, works wit

People who liked this also viewed

6 picks

Type

matlab-analyze-data

54installs105forks

View on GitHub

How to install matlab-analyze-data?

matlab/matlab-agentic-toolkit/matlab-analyze-data

$npx -y skills add matlab/matlab-agentic-toolkit --skill matlab-analyze-data

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# MATLAB Data Analysis
2
3	Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables.
4
5	## When to Use
6
7	- Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables
8	- Time-series analysis: resampling, synchronizing, trend detection, smoothing
9	- Answering questions about data in tables (top-N, filtering, group comparisons)
10	- Data cleaning: missing values, outliers, type conversion, normalization
11
12	## When NOT to Use
13
14	- The task has no tabular data context (no tables, timetables, or structured datasets)
15	- The primary goal is visualization or plotting, not data analysis
16	- The task is purely symbolic math, simulation, or app building
17
18	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.
19
20	Before writing code, read the reference file linked at the end of the relevant section below. Reference files contain correct syntax, common pitfalls, and "Avoid" patterns that prevent silent bugs. Skipping the reference risks using a deprecated approach or hitting a known pitfall.
21
22	### Key Functions — Available From
23
24	Most functions in this skill are available in R2023a or earlier. The following require a newer release:
25
26	\| Function \| Available From \| Purpose \|
27	\|----------\|---------------\|---------\|
28	\| `paddata`, `trimdata`, `resize` \| R2023b \| Pad, trim, or resize arrays to target length \|
29	\| `clip` \| R2024a \| Clamp values to a range \|
30	\| `summary` (enhanced) \| R2024b \| Supports arrays (numeric, datetime, duration, logical); adds `Statistics`, `DataVariables`, `Detail` name-value args \|
31	\| `isapprox` \| R2024b \| Tolerance-aware floating-point comparison (use instead of `==` for computed values) \|
32	\| `isbetween` (numeric) \| R2024b \| Check elements within a numeric range \|
33	\| `numunique` \| R2025a \| Count distinct values in a variable \|
34	\| `allbetween` \| R2025a \| Validate all values are within a range \|
35	\| `allunique` \| R2025a \| Validate all values are unique \|
36
37	## Getting Oriented with Data
38
39	When data is already in a workspace variable, start by understanding its structure and contents. Use JSON output for reliable parsing — table display is designed for human-readable grids, but as text it is easy to misinterpret which values belong to which variables:
40
41	```matlab
42	jsonencode(summary(T)) % per-variable stats as nested struct
43	jsonencode(head(T)) % first 8 rows as structured JSON
44	```
45
46	When getting oriented with unknown data, `summary(T)` already contains per-variable type, size, NumMissing, and — for numeric/datetime/duration — Min, Max, Mean, Median, Std. For categoricals it includes category names and counts. This is usually sufficient for an initial overview.
47
48	If producing a standalone script, leave the semicolon off to invoke the `display` method — it shows dimensions, variable names, and a truncated preview. Avoid `disp` (omits headers and prints every row, flooding output on large tables) and `fprintf` in a loop (verbose, old-style):
49
50	```matlab
51	summary(T) % types, ranges, missing counts per variable
52	size(T) % [nRows, nVars]
53	sum(ismissing(T)) % missing count per variable
54	T % dimensions + header + truncated preview
55	```
56
57	For Pearson correlation, use `corrcoef` (base MATLAB) with `Rows="complete"` to handle NaN: `corrcoef(T{:,vartype("numeric")}, Rows="complete")`. For Kendall or Spearman rank correlation, use `corr` (requires Statistics Toolbox): `corr(X, Type="Spearman")`.
58
59	> Systematic exploration checklist (distributions, cardinality, duplicates, outlier screening, `groupcounts`, `corrcoef`, time-based checks): [exploration.md](references/exploration.md)
60
61	## Data Types
62
63	Use modern MATLAB types. These are faster, more readable, and work better with table functions.
64
65	\| Instead of \| Use \| Why \|
66	\|---\|---\|---\|
67	\| `datenum`, `datestr` \| `datetime` \| Proper arithmetic, timezone support \|
68	\| `char`, `cellstr`, `strcmp` \| `string`, `==`/`matches` \| `==` for scalar, `matches` for vector comparison \|
69	\| Numeric codes or strings with few unique values \| `categorical` \| Self-documenting, works wit

People who liked this also viewed

6 picks

Type