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Thibaut - Synthetic Data Engineer AI Skill
$7.00
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Thibaut - Synthetic Data Engineer AI Skill
Build trustworthy eval and training data: representative sampling, annotation quality, synthetic generation with real gates.
$7.00
this skill
one payment
yours forever
Complete skill package instant downloadthibaut-eval-dataset-synthetic-data-engineer.md
Unlimited
Get this and 2,500+ more with Unlimited
$15/mo
Connect your AI once and every skill, prompt and agent loads when you ask. Unlimited also includes the AI CRM your Claude or ChatGPT can run.
AI CRM includedThis skill$7once, this skill only
Unlimited$15/mothis + 2,500+ more + AI CRM, first 7 days free
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Ask Thibaut something hard.
Our eval scores are 94% but users complain constantly. Rebuild our eval dataset so it actually predicts production.
CLAUDE · AS THIBAUT, SYNTHETIC DATA ENGINEER
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.
Or have every skill inside your AI.
Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com
// the file itself
Read it before you buy it.
The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.
Use the name
Once the file is loaded, talk to it by name: “Serge, check this page.” That is what the name is for. It also keeps several skills apart in one chat.
Once the file is loaded, talk to it by name: “Serge, check this page.” That is what the name is for. It also keeps several skills apart in one chat.
// the skill your AI runs on
You are Thibaut, a Senior Eval Dataset and Synthetic Data Engineer who writes the sampling frame first and generates synthetic cases against an attribute grid. You have been activated to build a golden set with measured agreement and a contamination check.
// what's inside
What's inside this skill
- 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.
// two ways to get it
Buy this file, or open the whole library.
Every skill, prompt and agent, inside your chat
$15/mo cancel anytime, or $129 a year
- All 2,500+ files in the library, loaded the moment you ask, plus 10 catalog files a month to download
- Nothing to download or paste: connect once in Claude, ChatGPT, Claude Code or Cursor
- Works on every Claude plan, free included · 30-day money-back guarantee on the first charge
Subscribers can still buy single files and keep them after they cancel.
// ask your assistant
Or let your AI set up this skill for you.
Go to kissmyskills.com/pages/for-ai-agents and set up the "Thibaut - Synthetic Data Engineer AI Skill" skill (handle: thibaut-eval-dataset-synthetic-data-engineer) for me
Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →
What you're actually buying
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.
// how to install
Under 2 minutes
Four steps. Any AI chat.
- 01Download the file
After checkout, the download link lands in your inbox. Save the file anywhere on your device.
- 02Open your AI chat
Claude, ChatGPT, Gemini, Grok, or Copilot - whichever one you already use.
- 03Paste the file contents
Drop it into the system prompt, Project instructions, or custom instructions field.
- 04Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.
No technical knowledge required.
With Unlimited there is nothing to download. Connect your AI once, then just ask: "Load Thibaut from KissMySkills."
// faq
Questions about this product
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.
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.