dataset-curation
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.
Profile →npx skills add wshobson/agents --skill dataset-curation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Format Selection
- Chat Templates and Loss Masking
- Packing
- Synthetic Data Rules
- The Dataset Card
- Phase 2 Exit Checklist
- References
What does the dataset-curation skill do?
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.
How do I install it?
Run `npx skills add wshobson/agents --skill dataset-curation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.