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This is 2026.9 documentation. Coverage here is incremental: a page exists only where 2026.9 differs or is newly documented. For any other topic, switch to Current (2026.8 and earlier) in the version selector.
Checking every incoming model by hand against your own standard doesn’t scale — someone has to remember to run it, on every submission, on every model. A Validation Check with auto trigger removes the remembering: author the rule once, and it runs itself against every new version of the tracked model. This page covers that specific pattern — a standard applied at every submission milestone, not a one-off spot check.
This is a rules-based check, not a Chat conversation — Data Validation runs the same WHERE / CHECK predicates every time rather than reasoning about a question. For plain-language questions about a model, use Ask questions about your models with Speckle Intelligence instead.

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What this workflow does

  • Encode the standard once — required parameters, naming pattern, or a value that must match a related object, as WHERE / CHECK rules.
  • Run it without anyone asking — auto trigger fires the check against tracked models when a new version lands, so validation happens at the moment of submission, not whenever someone remembers.
  • Score the submission — a pass rate against your threshold, not just a pass/fail per object.
  • Keep a history — every run is recorded, so you can see whether a consultant’s compliance is improving or not across a project.

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Walkthrough

1

Author the check against your standard

Open Run check in the project sidebar and draft your rules — for example, every element from the incoming discipline must carry a required parameter, follow a naming pattern, or match a value on a related host object. See Checks for rule structure, predicates, and how to check an object against something it’s related to.
2

Preview against a real submission

Run Preview against the model you’re building the check for. Tighten WHERE first if the scope is too broad — a check that’s too loose floods the result with noise nobody will read.
3

Set the trigger to auto and save

Save the check with auto trigger, and set your pass (and optional warn) threshold. From this point, every new version of the tracked model runs this check without anyone starting it manually.
4

Review results as submissions land

Each new version’s result appears in Checks with its pass rate. A consultant’s or subcontractor’s model is validated against your standard the moment it’s published — not at whatever point someone gets around to checking it.

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Reuse the standard elsewhere

A saved check is tied to its tracked models. If the same standard applies on other projects, recreate it there rather than expecting it to follow a model automatically — 2026.9 does not yet have a rollup from one project’s checks into a workspace-wide standard; see Project standards for where that’s headed.

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FAQ

No — after saving, only the check’s name and description are editable. Tracked models, rules, predicates, and trigger mode stay frozen. Create a new check for different behaviour. See Checks.
Data Validation preview and saved-check runs need a version in the 2026.9 data format. If a submission is still on the old format, ask the consultant or subcontractor to publish with a current connector. See Speckle’s data model is changing.
The result appears in Checks with its pass rate; this page does not cover a separate notification channel. Build a Model Validation or Property checker widget into a dashboard if you want the pass/fail state visible somewhere your team already looks — see Run a quick validation check.
No. A skill is an opt-in playbook someone arms with /skill-name in a conversation. A Validation Check with auto trigger runs on its own schedule (every new version) independent of Chat. They can complement each other — for example, ask Chat to summarise a check’s recent failures in plain language — but the check itself is authored and run through Data Validation.

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See also

Last modified on September 21, 2026