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new-validator — Add a new performance validator rule
Creates a new usd-validation-nvidia rule class that wraps a Usd Optimize
analysis-mode operation, registers it, tests it, and regenerates the docs.
The backing operation must already support analysis mode. If it doesn't, add
executeAnalysisImpl() to the operation first (see PLUGINS.md § Analysis Mode).
What this skill covers
How validators work — the base class, key class variables, and lifecycle.
Step 1 — create the rule class.
Step 2 — implement _CheckStage.
Step 3 — add a fix suggestion (optional).
Step 4 — add user-tunable parameters (optional).
— register the rule.
Step 5
Step 6 — add tests.
Step 7 — build and verify.
Companion skills: new-operation (create the
backing operation), run-validators (run the
validator and apply fixes).
How validators work
All rule classes subclass BaseUsdOptimizeChecker from
source/validators/python/usd_optimize/validators/base_usd_optimize_checker.py.
The base class handles running the operation in analysis mode via
CheckStage(). It calls _AnalyzeStage() to execute the operation, then
passes the analysis payload from the result to _CheckStage() — the one
method every subclass must implement.
Key class variables:
Variable
Required
Purpose
OPERATION_NAME
Yes
The operation key string (matches the C++ constructor first arg).
OPERATION_ARGS
No
Static dict of args forwarded to the operation every run. Defaults to {}.
PARAMETERS
No
User-tunable args exposed through Asset Validator's parameter UI. See Step 4.
Look at the existing checkers under
source/validators/python/usd_optimize/validators/ as the primary reference.
empty_leaf_checker.py is the simplest example; small_mesh_checker.py
shows PARAMETERS; base_duplicate_geometry_checker.py shows a shared base
class pattern.
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.# SPDX-License-Identifier: Apache-2.0from pxr import Usd
from usd_validation_nvidia import capabilities, register_requirements
from .base_usd_optimize_checker import BaseUsdOptimizeChecker
@register_requirements(capabilities.<FamilyRequirements>.<RULE_ID>)class <Name>Checker(BaseUsdOptimizeChecker):
"""
One-sentence description of what this rule checks.
"""
OPERATION_NAME: str = "<operation_key>"
OPERATION_ARGS: dict = {"argName": value, ...} # omit if no static args neededdef_CheckStage(self, usdStage: Usd.Stage, analysis_data: dict):
...
@register_requirements binds the rule to an Asset Validator capability ID
(from usd_validation_nvidia.capabilities). Pass override=True if
overriding an existing base rule for the same capability.
Step 2 — Implement _CheckStage
analysis_data is the dict returned by the operation's analysis mode (the
"analysis" key from the result). Its schema is specific to each operation —
read the operation's executeAnalysisImpl() in the C++ source to see what it
returns.
Call self._AddWarning() (most common) or self._AddFailedCheck() for each
issue. The at argument takes a Usd.Stage or Usd.Prim:
To also report individual prim-level issues, iterate the paths and add a
separate warning per prim with at=usdStage.GetPrimAtPath(path).
Step 3 — Add a fix suggestion (optional)
Rules that can auto-fix attach a Suggestion to the warning. The suggestion's
callable receives (usdStage, at_prim) and applies the fix via
analysis.optimize(). Use functools.partial to bind extra args:
from functools import partial
from usd_optimize.core import analysis
from usd_validation_nvidia import Suggestion
@classmethoddef_fix_stage(cls, usdStage: Usd.Stage, _: Usd.Prim) -> None:
analysis.optimize(usdStage, [analysis.OperationConfig(cls.OPERATION_NAME, args=cls.OPERATION_ARGS)])
def_CheckStage(self, usdStage: Usd.Stage, analysis_data: dict):
...
self._AddWarning(
message="...",
at=usdStage.GetPrimAtPath("/"),
suggestion=Suggestion(
message="Fix using Usd Optimize",
callable=self._fix_stage,
),
)
When the fix should use the operation configs derived from the analysis result
(rather than static args), use self.suggested_operations — it is populated
by _AnalyzeStage before _CheckStage is called. See small_mesh_checker.py
and zero_extent_checker.py for this pattern:
suggestion=Suggestion(
message="Fix using Usd Optimize",
callable=partial(self._fix_stage, operation_configs=self.suggested_operations),
)
Step 4 — Add user-tunable parameters (optional)
Declare PARAMETERS to expose per-rule settings in Asset Validator's parameter
UI. Each entry maps a display name to a Parameter that names the underlying
operation arg and its default:
from .base_usd_optimize_checker import BaseUsdOptimizeChecker, Parameter, ParameterFromOpArg
classMyChecker(BaseUsdOptimizeChecker):
PARAMETERS = {
"SIZE_THRESHOLD": Parameter(op_arg="threshold", default=0.001,
description="Minimum size before a mesh is flagged."),
}
Use ParameterFromOpArg instead of Parameter to pull the default and
description directly from the operation's argument metadata (avoids
duplication):
In _CheckStage, read the effective value (with any user override applied) via
self._effective_args()["threshold"] rather than OPERATION_ARGS directly.
Step 5 — Register the rule
In source/validators/python/usd_optimize/validators/__init__.py:
Add an import at the top:
from .<snake_case>_checker import <Name>Checker
Add to _RULE_CATEGORIES:
(<Name>Checker, "Usd:Performance"), # or "Omni:Geometry"
Use "Usd:Performance" for stage/scene-level conditions; "Omni:Geometry"
for low-level geometric defects.
Add to __all__.
Step 6 — Add tests
Add test cases for the new rule to the existing test files in
source/tests/test.python/:
test_checkers.py — functional tests using ValidationTestCaseMixin.assertRule().
Add a test method that supplies a .usda fixture and asserts the expected
warnings. Add the required fixtures to the test data directory.
test_validator_plugin_entry_point.py — no changes needed; it imports
from _RULE_CATEGORIES and covers all registered rules automatically.
test_checker_parameters.py — add a test only if the rule declares
PARAMETERS and has non-trivial parameter logic to verify.
Step 7 — Build and verify
./repo.sh build
./repo.sh test -s python
./repo.sh docs_gen --autogen_only
