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gallop


Three riders carried on one galloping horse

install · quick start · docs

MIT license latest stable release documentation site on GitHub Pages

Skills that make your coding agent think like a product data scientist.
They decide if a question deserves an analysis, pick the method, and check the result.

Install

# Claude Code
/plugin marketplace add 0trm/gallop
/plugin install gallop@gallop

# Any other agent, or none: a skill is a directory of markdown
git clone https://github.com/0trm/gallop
cp -r gallop/skills/reading-experiments .claude/skills/

# The package; the import name is gallop
pip install gallop-pds

Quick start

One command, a synthetic dataset, and every check once:

python3 -m gallop.examples.quickstart

The full loop, from a question arriving to the prior store changing on disk, is python3 examples/end-to-end/run_loop.py.

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

Map

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

The eight skills placed on the method map: routing at the entry, a can-you-randomise diamond, experimentation and causal inference to the right, exploratory analytics and statistical modeling below the path, the measurement framework as the floor and the theory layer as the ceiling

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 readout depends on a metric nobody has validated
sizing-opportunities 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 readout
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
automating-decisions 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-readouts 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

License

MIT. What it covers, what gallop does with your data, and how to contribute: docs/model.md.

Metadata

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