---

name: structured-label-issues description: "Route cleanlab token classification, object detection, and semantic segmentation label-issue workflows." disable-model-invocation: true metadata: disco-role: operating root-skill: cleanlab license: Apache 2.0

Structured Label Issues

Use this sub-skill for cleanlab workflows where each dataset example has structured labels rather than one scalar label:

• Token classification: each sentence/document is a list of tokens with one class label and one probability vector per token.
• Object detection: each image has annotated bounding boxes/classes and detector predictions grouped by class.
• Semantic segmentation: each image has a pixel mask and per-pixel class probabilities.

Keep this file as a router. Read the bundled references for schemas, API calls, and troubleshooting before writing code.

Route quickly

User asks for	Route here?	First reference
Named-entity recognition, POS tagging, or other per-token labels	Yes	references/token-classification.md
Finding/ranking/displaying token label issues	Yes	references/token-classification.md
Object-detection label issues, bounding-box ranking, or box visualization	Yes	references/object-detection.md
Semantic-segmentation mask label issues or pixel/image ranking	Yes	references/segmentation.md
Exact input schemas for all three families	Yes	references/data-formats.md
Function signatures and return types	Yes	references/api-reference.md
Shape/schema/backend failures	Yes	references/troubleshooting.md

Route elsewhere

• If the user asks about span classification specifically, route to the sibling experimental sub-skill (../experimental/). Span classification is intentionally not owned here.
• If the user wants a broad audit across one dataset with many issue types or a single audit/report wrapper, route to the sibling datalab sub-skill (../datalab/). Use this sub-skill only when the user wants the direct task-specific token/object/segmentation APIs.
• For standard multiclass/binary classification, use ../classification/SKILL.md.
• For multilabel classification or regression labels, use ../tabular-label-issues/SKILL.md.
• For multi-annotator consensus/quality, use ../multiannotator/SKILL.md.
• For out-of-distribution or outlier scoring, use ../outlier/SKILL.md.

Operating workflow

1. Identify the structured-output family: token classification, object detection, or semantic segmentation.
2. Validate data format first. Most failures are nested-list length mismatches, class-order mismatches, wrong bounding-box schema, or mask/probability shape errors.
3. Prefer out-of-sample model predictions/probabilities when available. Cleanlab scores are most meaningful when predictions were not obtained from a model fit on the same labels without cross-validation/holdout safeguards.
4. Use the family-specific filter.find_label_issues function when the user wants estimated issues; use rank.get_label_quality_scores plus issues_from_scores when the user wants ranking/thresholding control.
5. Use summary/visualization helpers only after confirming optional plotting dependencies are available or setting a non-interactive matplotlib backend for scripts and CI.
6. Interpret outputs at the right level: token tuples for token classification, image-level masks/indices plus per-box helper scores for object detection, and pixel masks plus image-level summaries for segmentation.

Bundled helper

Run scripts/smoke_structured_label_issues.py to exercise deterministic tiny fixtures for all three families. It is distilled from the public tutorials and native tests rather than copied from a source script: tutorial notebooks require external downloads/interactive visualization, while native pytest files are test harnesses rather than safe reusable runtime helpers.