Build, enrich, distill, and validate local training datasets for the Qwen Training Workbench. Use when dataset export, weighted-study assembly, label distillation, workstation-mode enforcement, or training-input validation change together.
インストール
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Build, enrich, distill, and validate local training datasets for the Qwen Training Workbench. Use when dataset export, weighted-study assembly, label distillation, workstation-mode enforcement, or training-input validation change together.
Qwen Training Dataset Factory
Use this skill for the data-prep lane behind local Qwen teacher-student training work.
Trigger Conditions
Use this skill when the work involves one or more of:
exporting source material into training-ready datasets
enriching media manifests or study metadata
distilling labels from prior model output
weighting or filtering training studies
validating dataset inputs before batch or staggered training runs
Route elsewhere when:
the work is about running or resuming training jobs: use $qwen-training-workbench-ops
the work is about checkpoint comparison or eval behavior: use $qwen-training-checkpoint-eval
the work is about desktop shell behavior only: use $qwen-training-desktop-ops
Workflow
Identify the dataset source surfaces and output artifacts.
Capture the workstation mode and any local-only constraints before mutation.
Run or update the data-factory stages in order:
export
enrich
distill
weight/filter
validate
Keep intermediate manifests deterministic and resumable.
Verify the resulting dataset contract against the intended training lane.
Record what changed in the dataset schema, study weights, or label provenance.
Required Evidence
input source paths or manifest identifiers
output dataset/manifests produced
validation command or test evidence
workstation-mode assumptions used during the build
any skipped or manually reviewed examples
Guardrails
Do not mix unrelated experimental transforms into the same dataset pass.
Keep provenance explicit for generated labels and enriched metadata.
Fail closed when workstation mode, dataset schema, or output directories drift from the expected lane.
Treat training-input validation as required, not optional cleanup.
Scope Boundary
Use this skill for dataset-factory preparation and validation only.
Do not use it for long-running training execution, desktop shell fixes, or checkpoint evaluation.