What does the Thibaut skill do?

Build trustworthy eval and training data: representative sampling, annotation quality, synthetic generation with real gates. Load it once into Claude Projects and you get a configured Eval Dataset & Synthetic Data Engineer without re-explaining context at the start of every session. Validate changes against a held-out eval set and real production traces before rolling out to all traffic.

How do I install this skill file?

Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.

Which AI tools does this skill work with?

Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.

What is included in this download?

One .skill package delivered instantly after purchase: the full SKILL.md role configuration plus a worked-example file with a real scenario so you see the quality before you rely on it. No subscription, yours permanently.

How is this different from using Claude without the Thibaut skill?

Without a skill file your AI starts every session as a general assistant. With Thibaut loaded it applies Eval Dataset & Synthetic Data Engineer methodology from the first message, with consistent quality every time. Validate changes against a held-out eval set and real production traces before rolling out to all traffic.

Eval Dataset & Synthetic Data Engineer AI Skill for Claude & ChatGPT

Name: Thibaut — Eval Dataset & Synthetic Data Engineer AI Skill
Brand: KissMySkills
Price: 14.99 USD
Availability: InStock

Thibaut — Eval Dataset & Synthetic Data Engineer AI Skill

$14.99

Thibaut — Eval Dataset & Synthetic Data Engineer AI Skill

$14.99 this skill vs $130+/hr, hiring one

⚡ Instant download ◆ Claude & ChatGPT ∞ Keep forever

Instant download · 30-day money-back guarantee. Pay once, keep forever — no subscription. Refund policy

Build trustworthy eval and training data: representative sampling, annotation quality, synthetic generation with real gates.

Representative held-out sampling and failure-mode stratification
Annotation guidelines and inter-annotator agreement
Synthetic generation with diversity and label-noise gates
Contamination checks, versioning and golden-set upkeep

Teams whose eval scores look great but do not predict production behavior at all.A senior data curation engineer bills $130+/hr, this is one file, yours forever.

// what's inside

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

→Representative held-out sampling and failure-mode stratification
→Annotation guidelines and inter-annotator agreement
→Synthetic generation with diversity and label-noise gates
→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.

thibaut-eval-dataset-synthetic-data-engineer.skill

# Thibaut - Eval Dataset & Synthetic Data Engineer

You are Thibaut, an Eval Dataset & Synthetic Data Engineer. A score on an unrepresentative eval set is worse than no score.

## How you work
1. Compare the eval set against real production traffic distribution
2. Stratify by the failure modes that actually matter
3. Write annotation guidelines; measure inter-annotator agreement
4. Generate synthetic data behind diversity and label-noise gates

Check for train/eval contamination before trusting any improvement.

Excerpt from the actual file you'll download.

// try it

prompt

$Our eval scores are 94% but users complain constantly. Rebuild our eval dataset so it actually predicts production.

Thibaut returns a gap analysis between the current eval set and production traffic, a stratified resampling plan by failure mode, annotation guidelines with a measured agreement score, a synthetic-generation run including a batch that fails its own noise gate and how it was fixed, and a contamination check with results.

// how to install Under 2 minutes

Four steps. Any AI chat.

01
Download the file
After checkout, the download link lands in your inbox. Save the file anywhere on your device.
02
Open your AI chat
Claude, ChatGPT, Gemini, Grok, or Copilot — whichever one you already use.
03
Paste the file contents
Drop it into the system prompt, Project instructions, or custom instructions field.
04
Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.