Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory.
SkillScore96Maintained
Automatic
◆Coding pack
Last verified 8/16/2026
What it changes
Passed 24 of 25 test cases with the skill, and 15 of 25 without it.
Without skill60%
With skill96%
Checks it passed that the model alone missed
Configures tabularTextDatastore with ReadSize set to 'file'.
Uses transform to apply a custom function to each file read.
Sets ReadSize to 'sheet' so each read returns one sheet's data.
and 12 more in the full report
10 fixed14 passed either way0 still failing1 broke
Pass-rate lift
+36 pp
Tokens
+138%
Agent turns
0%
Response time
-59%
Model gemini-3.6-flashMeasured 8/16/2026v124 of 25 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 10
Prompt
In standard `readtable`, extra columns in a text file can be handled with `ExtraColumnsRule`. How do you ignore extra unneeded columns at the end of lines when configuring a `tabularTextDatastore` in MATLAB?
What was checked, without and with the skill
Checked
Without skill
With skill
Explains that tabularTextDatastore does not support ExtraColumnsRule.
Missed
Passed
Recommends appending '%*[^
]' to TextscanFormats to ignore trailing extra columns.
Missed
Passed
case-25 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md371 lines3 files
Choose Big Data Solution
Help users and agents select the right MATLAB tool for large tabular data in
file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF). The
skill encodes a decision flowchart — recommend one clear path, not a menu of
options.
When to Use
User or agent has large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF)
User or agent says data is "large", "big", or "huge" (even without a specific size)
User or agent hits an out-of-memory error with readtable, parquetread, or similar
User or agent has multiple tabular files to process as a single dataset
User or agent has multiple tabular files to process independently (per-file)
User or agent asks how to scale up an existing workflow on tabular file data
User or agent asks about datastores, tall arrays, or mapreduce
User or agent needs to build a custom datastore class for a proprietary or non-standard format
User or agent has large XML, JSON, HTML, or Word document files — readtable supports these formats but the built-in datastores do not. Scaling these requires a custom datastore (see "Formats Without a Built-in Datastore" and "Custom Datastores" sections below)
When NOT to Use
Single file that fits in memory (file-size-to-RAM ratio < 0.5) — see Pre-Flight Check and matlab-import-export-data skill
User or agent is working with MAT files — matfile provides partial I/O for large .mat files, different workflow
User or agent is working with databases (SQL, ODBC) — use Database Toolbox. See the matlab-read-database and matlab-use-duckdb skills in the reporting-and-database-access category
User or agent needs GPU acceleration — different domain
Deep learning training workflows — datastore is correct for data loading, but use minibatchqueue for batching (not tall or transform)
Pre-Flight Check
ALWAYS run this check BEFORE recommending datastore or tall array patterns.
Estimate the file-size-to-available-RAM ratio (accounting for in-memory
expansion of the file format).
Condition
Route
Rationale
Single file, ratio < 0.5
Native MATLAB I/O (readtable, parquetread)
Fits in memory; datastore/tall adds unnecessary complexity
Single file, ratio ≥ 0.5, OR user/agent reports OOM
Continue to Decision Flowchart below
Data may not fit in memory
Multiple files
Continue to Decision Flowchart below
Datastore patterns provide unified multi-file access
Size unknown and user/agent describes data as "large", "big", or "huge"
Computes multi-constellation Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) positions from RINEX v3 data using rinexread, gnssmeasurements, receiverposition, and gnssoptions. Filters by constellation, elevation mask, C/N0, and observation code. Reports DOP, scatter RMS, and satellite count. Use when processing GNSS data, computing positions from RINEX files, analyzing accuracy, comparing constellations, or evaluating satellite geometry. Do NOT use for carrier-phase