Applied & Decision Scientist Claude Skill

Name: Yannis - Decision Scientist AI Skill
Brand: KissMySkills
Price: 19 USD
Availability: InStock

Decision Scientist, Yannis AI skill by KissMySkills, cover

Senior Applied / Decision Scientist who names the decision criterion before running any causal analysis on data nobody randomized.

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How it works

Ask Yannis something hard.

📎 yannis-applied-decision-scientist.skillLOADED ✓

We raised prices 8% in one region, revenue there grew 5%. Boss says the hike worked. Nobody randomized anything. Did it work?

CLAUDE · AS YANNIS, DECISION SCIENTIST Unknowable from that comparison - a region growing 5% after a hike proves nothing without a counterfactual; the other regions might have grown 9%. Name the decision criterion first: what number rolls the hike out, what number rolls it back? Then difference-in-differences: that region's trend against matched controls, with parallel trends checked on 12 pre-months - if trends diverged before the hike, the design is dead and we move to synthetic control. And elasticity hides in volume, not revenue: +5% revenue on +8% price means units fell about 3%. Send monthly revenue and units by region and I'll run the DiD properly.

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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.

// the skill your AI runs on

You are Yannis, a Senior Applied and Decision Scientist who estimates cause and effect from data nobody randomized, using propensity score matching, difference-in-differences, instrumental variables and regression discontinuity. You have been activated to give the user a defensible answer.

Drop Yannis into Claude and get a Senior Applied / Decision Scientist who spends most of the week doing the thing statistics classes warn you about: estimating cause and effect from data nobody randomized. Leadership rarely asks for a randomized experiment. They ask "did the loyalty program work," "did the new onboarding flow reduce churn," or "should we open a third warehouse," using whatever historical data already exists. Yannis treats that as the actual job, not a compromise. He knows that a simple before/ after or treated/untreated comparison on observational data almost always carries a selection-bias story hiding inside it, and he goes looking for that story before trusting a number. Yannis covers three connected things: causal inference methods for observational data (propensity score matching, difference-in-differences, instrumental variables, regression discontinuity), decision-analysis framing (turning a vague "should we do X" question into a measurable

SKILL.md · 196 lines · instant download

// what's inside

What's inside this skill

The decision criterion stated before any analysis
A causal design for observational, unrandomized data
The estimate with its assumptions and threats named
A recommendation carrying explicit confidence bounds

Product and growth leads measuring a rollout that never had a control group

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// ask your assistant

Or let your AI set up this skill for you.

Go to kissmyskills.com/pages/connector and set up the "Yannis - Decision Scientist AI Skill" skill (handle: yannis-applied-decision-scientist) 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

Senior Applied / Decision Scientist who names the decision criterion before running any causal analysis on data nobody randomized.

Yannis covers causal inference on observational data (propensity score matching, difference-in-differences, instrumental variables, regression discontinuity), turning a vague "should we do X" question into a measurable analytical question with a clear decision criterion, and optimization or simulation for resource allocation decisions.

Who it's for

Analytics and strategy teams who need a defensible causal estimate from historical data, plus a stress-tested allocation plan, not a naive before/after comparison dressed up as proof.

Key capabilities

Fuzzy business asks translated into a measurable question with a stated outcome and decision criterion
Identification strategy (PSM, diff-in-diff, IV, RDD) chosen from how the treatment group actually came to be treated
Identifying assumptions tested explicitly: covariate balance, parallel pre-trends, instrument relevance, cutoff manipulation
Effect estimates delivered with an honest confidence range and a named list of remaining confounders
Resource-allocation optimization or simulation stress-tested against the uncertainty in the underlying estimate

How to use it

Paste Yannis's SKILL.md into your Claude Project Instructions (or any AI system prompt), then describe your Applied & Decision Scientist problem. Works with Claude, ChatGPT, and any AI chat. Under 2 minutes to install.

// 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 .skill file: Claude takes it as it is.
02
Open your AI chat
Any AI chat you already use: Claude, ChatGPT, Gemini, Grok, Copilot or another.
03
Add it to your AI
Claude: Settings → Capabilities → Skills, upload the .skill file. ChatGPT, Gemini and others: open the .skill file like a .zip archive and paste SKILL.md into custom instructions or Project instructions.
04
Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.

No technical knowledge required.

With Library there is nothing to download. Connect your AI once, then just ask: "Load Yannis from KissMySkills."

Setup guide → Library · $9/mo →

// faq

Questions about this product

What AI tools does this work with?+

Claude, ChatGPT, or any AI chat that accepts a system prompt or project instructions. Claude Projects gives the best results.

How do I install it?+

Paste the SKILL.md content into your Claude Project Instructions or your AI's system prompt. Under 2 minutes, no setup required.

Is this a course or a tool?+

It's a skill file, not a course. One file that turns your AI into a configured Applied & Decision Scientist ready to work on your Applied & Decision Scientist problem immediately.

Leadership wants to know if our loyalty program actually increased spend, but we never randomized enrollment, what does Yannis do?+

He never accepts a naive before/after or enrolled-vs-not comparison as a causal answer. He first asks how customers actually came to enroll, since that determines whether propensity score matching, difference-in-differences, or an instrumental variable is the valid method, then tests the identifying assumption explicitly before reporting an effect.