gallop

Why

Statistics libraries assume you already know which question you are answering. Product management skill packs know which question matters and carry no statistics. With gallop, every question passes routing first before any query runs, and goes to one of three method buckets, on a measurement floor and under a knowledge ceiling.

Bucket	Asks	Hands back
Description	What happened?	A hypothesis
Causation	Did this change cause that?	An effect size
Prediction	What will happen? Who gets what?	A forecast, a ranking, an allocation

gallop's sibling, bluML, builds and runs the models that will live inside the product, from framing to monitoring, when it is installed; gallop keeps the questions around them: whether a model fits the question, uplift targeting, and whether the model moved anything.

Map

A question enters at the left and leaves as a decision. Measurement is a foundation because what ships changes the data. Knowledge is a ceiling because what you learn has to outlive the test that produced it.

Read the full map

Skills

Skill	What it decides	Reach for it when
routing-questions	Whether this becomes work at all, and which skill it becomes	a product, analytics, or experimentation request first arrives, when someone asks for a deep dive or a dashboard, or before opening a query editor on any question about impact, lift, or whether something worked
defining-metrics	A metric turned into a computation, a source of truth, a registry entry, and a statement of how it will be gamed	defining a north-star or guardrail metric, when two dashboards disagree on the same number, when arbitrating between conflicting metric definitions, or when a report depends on a metric nobody has validated
forming-hypotheses	A what-happened question turned into a localised, sized hypothesis, with the floor checked first and the gap never quoted as the prize	a metric moved and someone asks what happened, when asked for a deep dive, a funnel or segment analysis, a root cause, or an opportunity size before a roadmap commitment, or when an observed gap between two groups is about to be quoted as the value of closing it
designing-experiments	The four choices that cannot be repaired after launch, with the MDE from the prior store	planning, powering, or pre-registering an experiment, when deciding whether a question is testable at the available traffic, or when a feature is about to ship without a flag
reading-experiments	Whether the result is a result: SRM, exposure, the sequential bound, CUPED, shrinkage	analysing or reviewing A/B test results, when a test looks like a winner, when someone reports a lift, or when deciding whether to ship on an experiment report
choosing-causal-designs	The method that matches how assignment happened, and the exit that says there is no comparison group	measuring the impact of something already rolled out, a launch, a migration, a pricing change, or a campaign that reached everyone at once
building-models	Whether a forecast or a repeated decision belongs to a model, validated out of time, and the holdout that measures its impact	someone asks for a churn, propensity, LTV, scoring, forecasting, uplift, recommendation or allocation model, when a model's offline accuracy is offered as evidence that something worked, or when deciding who gets an offer, a discount or an intervention
writing-reports	The decision rule first, the result last; the belief filed where the next question starts	a test finishes, when documenting a shipped or killed decision, when writing up a null result or a rollback, or when a question needs an entry someone can find in a year

Install

Each skill is a directory of markdown: SKILL.md is the procedure, the reference/ files one level down are the depth. Read them as prose, or copy them into your agent's skills folder:

git clone https://github.com/0trm/gallop
mkdir -p .claude/skills
cp -r gallop/skills/* .claude/skills/

How the skills hand off to each other, and the state they share: docs/orchestration.md.

License

MIT. What it covers and how to contribute: docs/model.md.