claudegoodies
Skill

dataset-curation

From wshobson

Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.

Facts

Repository
wshobson/agents
Status
Actively maintained
Last commit
Source file
plugins/llm-finetuning/skills/dataset-curation/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# Dataset Curation

This skill assumes `finetuning-method-selection`
already routed here — the next step is preparing
data, not choosing a method. What follows: format
selection by target method, the template/packing
mechanics behind the most common silent training
failures, rules for mixing in synthetic data
without collapse, and the dataset card that closes
out Phase 2 before a run starts.

**Input:** raw examples (demonstrations, preference
judgments, or task prompts) plus a routing decision
from `finetuning-method-selection`.
**Output format:** a formatted, packed, validated
JSONL dataset plus a completed dataset card — the
Phase 2 artifact `/finetune` checks before launching
training.

## Format Selection

| Method | Shape | Rows |
|---|---|---|
| SFT, single-turn | Instruct (`instruction`/`response` or `prompt`/`completion`) | ~1,000+ floor |
| SFT, multi-turn | Conversation / ChatML `messages` list | ~1,000+ floor |
| DPO / ORPO | Preference pair (`prompt`, `chosen`, `rejected`) | Method-dependent, see `preference-optimization` |
| KTO | Unpaired (`prompt`, `completion`, `label`) | Method-dependent, see `preference-optimization` |
| GRPO / RLVR | Prompt-only (`prompt` + verifier metadata) | Method-dependent, see `grpo-rlvr-training` |

- **~1,000+ rows is the recommended floor for SFT**,
  not a target. Below it, a handful of low-quality
  or duplicate examples can domin
View full source on GitHub →

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