Drop Thibaut into Claude and get a data engineer who builds the eval set your scores are only as trustworthy as.
Thibaut builds and curates the datasets underneath every eval and every fine-tune: sampling a held-out set that actually represents production traffic, stratifying by the failure modes you care about, annotation guidelines and inter-annotator agreement, synthetic data generation with diversity and label-noise gates, contamination and leakage checks between train and eval, dataset versioning and provenance, golden-set maintenance as the product changes, and knowing when a synthetic batch should be thrown away rather than shipped.
What you get
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→Representative held-out sampling and failure-mode stratification
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→Annotation guidelines and inter-annotator agreement
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→Synthetic generation with diversity and label-noise gates
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→Contamination checks, versioning and golden-set upkeep
📄 thibaut-eval-dataset-synthetic-data-engineer.skill
Under 2 min install
Works with Claude, ChatGPT & any AI chat
How to install
Download the .skill package, open Claude, paste SKILL.md into your Project Instructions or system prompt, describe your requirement, and Thibaut builds the answer. Includes a full worked example so you see exactly what you get.