finetune-dataset-builderlisted

The dataset is the model — so this skill treats building it as the real work, not a preprocessing afterthought. It takes raw examples and produces a clean, correctly-formatted, deduplicated dataset with a leak-free eval split, ready to hand to a trainer. Get this right and the training run is mechanical; get it wrong and no amount of tuning saves the result. ## When to use this skill - You have raw examples (logs, labeled pairs, exported conversations) and need them formatted, cleaned, and split before fine-tuning. - An existing dataset gave a disappointing fine-tune and you suspect duplicates, leakage, PII, or off-distribution noise. - Standing up a repeatable dataset pipeline so each fine-tune is reproducible. ## Instructions 1. **Fix the target format first.** Determine the trainer's expected schema (commonly JSONL chat records: system/user/assistant, or instruction-response) and that it matches how the model is called in production. Normalize every example to that exact shape — the training format must mirror the inference format. 2. **Deduplicate, including near-duplicates.** Remove exact duplicates and fuzzy/near-duplicates (normalized text, embedding similarity). Near-duplicates are the main cause of memorization and the silent leak that inflates eval scores, so be aggressive here. 3. **Clean and correct.** Fix label/answer errors, drop malformed records, normalize whitespace/formatting, and **strip PII and secrets**. A wrong target teaches the wrong thing; sensiti