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data-labeling

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Design annotation pipelines that produce reliable training labels — guidelines, agreement measurement, and quality control.

skills/data-labeling/SKILL.md

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## Overview

Data labeling turns raw examples into training signal: class labels, preference
rankings, bounding boxes, span annotations, or structured judgments. The
uncomfortable truth of the field is that model quality is bounded by label quality
— a state-of-the-art architecture trained on noisy labels underperforms a simpler
model trained on clean ones. Professional labeling is not "pay people to click";
it's a measurement operation with guidelines as the spec, agreement metrics as the
QC, and adjudication as the error correction.

The pipeline has four parts: task design (what exactly is being labeled and what
the edge cases are), guidelines (the written contract between you and annotators),
the labeling pass itself (with redundancy for measurement), and quality control
(agreement metrics, gold questions, adjudication). Whether annotators are in-house
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