Discover MATLAB Parallel Computing Toolbox clusters on the network and in the cloud, and manage their profiles — list, inspect, import, export, set default, validate, and delete.
SkillScore95Maintained
Automatic
◆Coding pack
Last verified 8/16/2026
What it changes
Passed 21 of 22 test cases with the skill, and 5 of 22 without it.
Without skill23%
With skill95%
Checks it passed that the model alone missed
Passes TimeoutSeconds=15 to discoverClusters
Uses parallel.deleteProfile to purge the profile
Skips the Threads profile before calling parcluster
and 23 more in the full report
17 fixed4 passed either way0 still failing1 broke
Pass-rate lift
+73 pp
Tokens
+211%
Agent turns
0%
Response time
-40%
Model gemini-3.6-flashMeasured 8/16/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 17
Prompt
I am using MATLAB R2026a and have instantiated a cluster object `c = parcluster('HPCProfile')`. Write a MATLAB command to validate this cluster object using 4 workers.
What was checked, without and with the skill
Checked
Without skill
With skill
Calls validate(c, ...) as a method on the cluster object c
Passed
Passed
Passes NumWorkersToUse=4 or equivalent option to cap worker count during validation
Missed
Passed
case-17 · 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.md276 lines2 filesincludes scripts
Discover MATLAB Clusters
A cluster profile is a saved set of properties (name, type, host, number of
workers, scheduler arguments) that lets MATLAB connect to a compute
resource. This skill covers the full profile lifecycle: discovering
clusters on the network, importing shared profiles, setting a default,
validating, and deleting.
When to Use
The user asks what parallel computing resources, clusters, or queues are available to them.
The user wants to find clusters they can submit jobs to.
The user has an .mlsettings profile file from an admin and needs to import it.
The user wants to list, inspect, set the default for, validate, or delete a cluster profile.
The user wants to export a cluster profile to share with colleagues.
The user is calling parcluster or parpool and wants to know which resource it will use or which profile name to pass.
When NOT to Use
Submitting jobs (batch, createJob, submit, parfeval) — this skill stops at "the cluster is ready to use."
Pool lifecycle beyond profile selection — parpool startup/shutdown, gcp, and pool destruction are separate concerns. Identifying which profile to pass to parpool is in scope.
GPU or parfor workflows — covered by separate skills.
Workflow
List existing profiles with parallel.listProfiles to see what is already configured.
If the user is missing an expected resource or wants to search for additional clusters, discover them with the bundled discoverClusters script (network and cloud).
Add the cluster as a profile — either by creating it from a discovered cluster (saveAsProfile) or importing a shared .mlsettings (parallel.importProfile).
Set the default profile with parallel.defaultProfile if appropriate.
Validate with parallel.validateProfile (R2025a+) or validate(c) on the cluster object before relying on it. Pass NumWorkersToUse (e.g. 2) or confirm with the user first — leaving it unset runs validation on the cluster's full worker count.
Delete obsolete profiles with parallel.deleteProfile (R2026a+) — confirm with the user first; deletion is irreversible.
After every step, verify before moving on — show the updated profile list, confirm the type/host/worker count, or check the validation report.