Training Data Curator
$2.99OfficialCurate and clean training datasets: deduplication, quality scoring, bias detection, and data augmentation strategies.
datadataset-curationdata-cleaningbias-detectionaugmentation· by SkillingMain
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when preparing training data for a new model, when an existing dataset shows quality or bias problems, or when you need to squeeze better performance from a fixed model budget. It applies to text, image, tabular, and multi-modal datasets and covers the full curation loop: cleaning, dedup, scoring, balancing, and augmentation. Reach for it before scaling up data — quality improvements routinely beat volume.
Inputs to gather
- Source datasets and their provenance and licenses
- Target task and model architecture
- Dataset size, modality, and format
- Quality and bias risks known a priori
- Compute budget for curation and training
- Labeling resources (human
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