quark-onnx-doc-drift-check

name	quark-onnx-doc-drift-check
description	Compare ONNX skill contracts and guidance against current Quark ONNX documentation and source entry points. Use when maintainers need to verify that ONNX install docs, custom-op registry, QConfig fields, preset and calibration lists, AutoSearchPro presets, deployment-target gates, or example-script invocations still match upstream Quark ONNX reality. Trigger for "check ONNX doc drift", "are ONNX skills still accurate", "verify against Quark ONNX docs", "fact-check quark-onnx-* skills", or after a Quark release when upstream ONNX documentation may have changed.
layer	meta
backend	onnx
primary_artifact	validation_report.md
source_knowledge	["docs/source/install.rst","docs/source/onnx/basic_usage_onnx.rst","docs/source/onnx/user_guide_config_description.rst","docs/source/onnx/appendix_full_quant_config_features.rst","docs/source/onnx/user_guide_auto_search_pro.rst","docs/source/onnx/gpu_usage_guide.rst","docs/source/onnx/onnx_examples.rst","quark/onnx/quantization/config/custom_config.py","quark/onnx/quantization/config/algorithm.py","quark/onnx/quantization/config/config.py","quark/onnx/calibration/methods.py","quark/onnx/operators/custom_ops/__init__.py","quark/onnx/quantization/auto_search/auto_search_pro.py","examples/onnx/yolo_quantization/quantize_yolo.py","examples/onnx/model_support.md","tools/ci/install_onnxruntime.sh"]

quark-onnx-doc-drift-check

Purpose

Provide a lightweight ONNX-side governance check focused on documentation accuracy. While quark-onnx-skill-sync audits source-code changes broadly and applies the actual updates, this skill focuses specifically on whether the ONNX skills' user-facing guidance — preset names, calibration methods, custom-op names, QConfig field names, AutoSearchPro presets, ORT install matrices, example-script invocations — still matches what Quark's own ONNX docs and source say. Think of this as a fact-checker for the quark-onnx-* skill family.

Inputs

• Quark upstream ONNX docs and source code (read-only)
• Current ONNX skill SKILL.md files under .claude/skills-impl/{l1-atomic,l2-workflows,l3-recipes}/onnx/
• ONNX-side shared schemas under .claude/skills-impl/shared/contracts/

Outputs: validation_report.md

Lists where current ONNX skill guidance has drifted from upstream Quark ONNX docs and source.

Schema: validation_report.schema.json

# ONNX Documentation Drift Report

## What to Check

### 1. Quantization Presets Still Exist

Skills reference specific preset names in their decision tables. Verify each is still defined in
`quark/onnx/quantization/config/custom_config.py`:

**Critical presets to verify** (referenced by `quark-onnx-quant-plan`, `quark-onnx-ptq-workflow`,
`quark-onnx-autosearch-pro`):

- `XINT8` (and the `EnableNPUCnn=True` companion flag)
- `A8W8`, `A16W8`
- `BF16`, `BFP16`
- `MX*` family (e.g. `MXINT8`, `MXFP8_E4M3`, `MXFP8_E5M2`, `MXFP4`, `MXFP6_E3M2`, )
 Weights-only INT4 path (e.g. )



Check that  (and ) still contain every
calibration method named in :





Verify that  still exposes every algorithm config
class referenced by the skills:

 , 
 , , 
 , , 



The  surface is copied into skill decision tables, generated scripts, and the workflow
example. Verify every field is still present in  / :

 , , 
 , 
 
 , 



Skills reference Quark ONNX custom ops by name in error messages, debug guidance, and the
 opset domain check used by . Verify each is still
registered in :

 
 
  family
 The  domain string



Check that the  recipe's preset names still exist in
:

 , , , 



Compare 's ORT package/version matrix with:

  (the authoritative matrix)
  installation instructions
  (GPU/EP guidance)
  core ONNX deps (, , )

Verify the supported  tuples cited by the skill still match.
Common EP names that must be consistent: , ,
, .



 lists a deployment-target compatibility table (CPU / CUDA / ROCm /
AMD NPU CNN / AMD NPU Transformer). Verify the gating logic still matches what
 accepts (e.g. BFP16 forbidden on NPU CNN, XINT8 + ).



Verify that the JSON schemas in  match what the ONNX
workflow actually produces:

  — matches the ONNX  with ?
  — includes ONNX-specific fields used by  (preset, calibrationconfig, EnableNPUCnn, usedatanodes)?
  — matches 's manifest (generated script path, exact  command, resolved )?



Check that the example invocation in  and the
walkthrough in  still agree on:

 the  field names used in the worked example,
 the  argument order,
 the imports from ,
 the calibration data reader pattern from .



Verify the  rule cited by  and
the workflow still matches what  /  enforce.







  — report findings but do not modify skills. Modifications go through
  .
  — preset names, calibration-method names, custom-op names, QConfig
  field names, ORT versions, and example-script signatures matter most because users will copy-paste
  them into scripts the workflow will run.
  — a skill that recommends a deleted preset, calibration method,
  custom op, or QConfig field will cause immediate user failures at script generation or runtime.
  — a new preset, calibration method, or AutoSearchPro preset
  that is not yet documented in skills is a coverage gap, not an error.
  — never touch  skills. Cross-cut findings (e.g. a shared
   field) are surfaced but deferred to the torch maintainer.



 Quantization presets: 11 checked, 10 match, 1 new (not in skills)
 Calibration methods: 6 checked, 6 match
 Algorithm configs: 8 checked, 7 match, 1 drift
 QConfig fields: 8 checked, 8 match
 Custom-op registry: 4 checked, 4 match
 AutoSearchPro presets: 4 checked, 4 match
 ORT install matrix: 5 rows checked, 4 match, 1 drift
 Deployment-target gates: 5 checked, 5 match





  in skills → now  in  (case change)
 : Generated scripts in  and  will
  fail at  (ImportError)
 : Update the import + plan tables in  and
   SKILL.md, then re-validate the YOLOv8 worked example



 Skills recommend 
  now pins 
 : Copy-paste install commands in  will downgrade users on a
  freshly installed environment
 : Update the ROCm row in 's ORT install matrix



  added to 
 : Users asking about MXFP6 won't see it in 's preset table
 : Add  row to the preset table; consider whether AutoSearchPro should expose
  a matching 



  now accepts a  mapping
 : Skill mentions  but not the new parameter
 : Optional — extend  calibration row when a real user request lands