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Experiment Design

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Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The experiment-design skill provides a structured framework for end-to-end experiment management. It helps teams translate business outcomes into specific problem classes, identify and prioritize risks, and design testable, falsifiable hypotheses. By distinguishing between problem-class framing and experiment execution, it ensures that teams focus on resolving critical unknowns rather than building premature solutions. The pack covers various experiment types, including data feasibility, architecture, LLM, and UX, while emphasizing reproducibility and decision-ready outcomes.

What it does

  • Translates business outcomes into candidate data-science problem classes.
  • Provides criteria for vetting hypotheses and identifying red flags.
  • Facilitates risk-based prioritization of experiments.
  • Guides the design of Minimum Viable Experiments (MVEs).
  • Ensures experiment results are reproducible and comparable.
  • Separates problem-class framing from experiment design.

When to use it

  • When you need to translate a business goal into a technical problem class.
  • When you have competing unknowns and need to prioritize which to test first.
  • When you need to validate assumptions before committing to full-scale production.
  • When you need to design a test that is falsifiable and decision-ready.
  • When you need to distinguish between a genuine experiment and a demo.

Advantages

  • Reduces project risk by validating assumptions early.
  • Prevents wasted effort on demos disguised as experiments.
  • Provides a clear, repeatable process for experiment design.
  • Ensures experiments are tied to actionable business decisions.
  • Promotes knowledge transfer and independent ownership in collaborative teams.

Usage examples

“I have a business goal to reduce customer churn. How do I frame this as a data science problem?”
The agent will use the problem-framing reference to identify candidate problem classes, such as classification or regression, while documenting the reasoning and any gaps without selecting a specific model.
“We are unsure if our current architecture can handle the expected load. How should we test this?”
The agent will guide you through forming a falsifiable hypothesis, vetting it against MVE criteria, and designing an architectural feasibility experiment to resolve the uncertainty.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/experiment-design/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip experiment-design.zip -d .agents/skills

Documentation by Microsoft

Experiment Design Reference Pack

Goal

Support experiment work end to end: turning unknowns into testable hypotheses, screening out work that is not a real experiment, and scoping it so the result is comparable and decision-ready.

Support the step that precedes it as well: translating a stated business outcome into candidate data-science problem classes with the reasoning that produced them, so a practitioner knows what kind of problem is on the table before deciding what to test.

The two concerns stay distinct. Problem-class framing exposes candidates and never selects one. Experiment work assumes a candidate direction already exists and concludes by selecting an experiment with the team.

This pack is general purpose. It applies to data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments, not to data science alone.

Inputs

  • The problem statement, customer context, and business driver

  • The stated business outcome, when the active concern is problem-class framing

  • Known unknowns, assumptions, and risks

  • The decision the experiment is meant to unblock

  • Prior experiment results, when a sequence of experiments is in flight

Reference index

Read only the reference that matches the active concern.

ReferenceRead this when
problem-framing.mdTranslating a stated business outcome into candidate data-science problem classes, applying per-paradigm entry tests, ordering discriminating questions, or recording assignable gaps
mve-coaching.mdFraming an MVE, forming or sharpening hypotheses, applying vetting criteria and red flags, designing the experiment, evaluating results, or producing session and backlog-bridge artifacts
experiment-readiness.mdDeciding which experiment to run at all: turning a risk landscape into candidates, prioritizing among competing unknowns, comparing options with evidence, or re-prioritizing mid-flight
rpi-research-preparation.mdPreparing an experiment with bounded prior-art or current-constraint Research while keeping validation and experiment verdicts with their current owners
provenance.mdConfirming what is upstream guidance, what is HVE Core derivation or repository convention, and where upstream is silent

A confirmed problem-class framing request reads problem-framing.md only. Experiment requests read mve-coaching.md or experiment-readiness.md by concern.

Success criteria

  • Problem-class framing returns the surviving candidate problem classes with the entry-test reasoning that kept or discarded each one, and never returns a selection, a ranking, or a confidence score.

  • Every framing claim is visibly either a cited entry test or repository-original routing, and no claim implies that a universal external standard governs cross-paradigm routing.

  • An unanswered framing question becomes a recorded gap with an answer holder and a coordinator, and the remaining analysis continues.

  • Each hypothesis is testable, specific, falsifiable, and tied to a stated rationale.

  • The experiment selected is the one whose result changes the most downstream work, chosen from a candidate list rather than from the first idea proposed.

  • Work that is a demo, a mini-MVP, or an already-answered question is named as such rather than run as an experiment.

  • Every input that could change the result is recorded precisely enough that another person could repeat the experiment and get a comparable answer. An identifier without a version is a label, not a reproducible reference.

  • Experiment scope stays minimum-but-sufficient, and experiment code is treated as disposable.

Constraints

  • Reproduce only the minimum text necessary for a specific technical point, and paraphrase everything else. Attribute every reference and describe accurately what each reference reproduces.

  • Label repository conventions as substitutions rather than upstream recommendations.

  • Keep experiment framing separate from experiment infrastructure. This pack decides whether and how an experiment is worth running; it does not stand up tooling.

  • Keep problem-class framing separate from experiment framing. Problem-class framing asks what kind of problem this is; experiment framing asks what to test about a direction already chosen. Do not apply experiment vetting criteria, red flags, or readiness prioritization to a confirmed problem-class framing request.

  • Never rank, score, threshold, or recommend a candidate problem class. The practitioner is the subject-matter expert who chooses.

Ownership boundaries

ConcernOwner
Candidate problem classes for a stated business outcome, entry-test reasoning, discriminating questions, and assignable framing gapsThis pack, through problem-framing.md
Whether a proposed outcome is achievable with available data and evidencefeasibility, the evidence-led feasibility study reference
MVE session directory, artifact filenames, placement, and tracking-file hygieneexperiment-designer.instructions.md, applied automatically to MVE tracking paths
Phase order, gates, session writes, and coaching flowThe consuming experiment agent
ML environments, reproducibility, tracking, model evaluation, abstractions, and readinessml-experimentation, the ML-specific experimentation reference
Pipeline mechanics, data tiering, replay, validation, and DS/MLOps test techniquedataops, the DataOps and testing reference
Metric names, instruments, units, cardinality, and PII-safe telemetrytelemetry-foundations, the OpenTelemetry-aligned instrumentation skill
Data sensitivity classification and DPIA thresholdsprivacy-standards, the privacy classification reference

Stop rules

  • For a confirmed problem-class framing request, an unstated business outcome is the sole stop before candidate analysis. problem-framing.md owns that stop and its job-specific exceptions. The experiment stop rules below govern experiment requests only.

  • Stop and name the red flag when the request is a demo, a scaled-down product build, or a question already answered elsewhere. Then offer either the falsifiable hypothesis hiding underneath it or an explicit non-experiment path, rather than halting on the refusal.

  • Stop and separate concerns when the request is production implementation rather than experiment design.

  • Stop and state the gap when the request depends on guidance this pack does not provide, such as a universal framework, tool, or metric choice made without project context.

Attribution

This pack declares CC-BY-4.0.

mve-coaching.md is repository-original content under CC BY 4.0. It is not derived from any upstream source and cites no upstream URL.

problem-framing.md layers three source treatments. Its machine-learning entry tests are informed by Google for Developers machine-learning guidance, licensed CC BY 4.0; that material is paraphrased and reorganized, both upstream URLs are cited, and changes are stated. It points readers to the NEOS Guide for optimization problem types as a citation only, and states no NEOS classification of its own. Its cross-paradigm routing, the broader analytical-fit judgement, and the gap and output contract are repository-original.

experiment-readiness.md is HVE Core guidance informed by two Microsoft CSE Code With Engineering Playbook documentation pages, which are licensed CC BY 4.0. It paraphrases rather than reproduces, generalizes the upstream practices beyond engagement-shaped engineering work, cites both upstream URLs, and states that changes were made.

See provenance.md for the consolidated source map and derivation labels.

Files

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Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Needs: Claude Code frontmatter

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Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/project-planning/experiment-design · commit f425e94
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Creative Commons Attribution 4.0 International (redistribution allowed)
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