gallop

Routes a product question to the method it deserves, then runs that method with checks in place.

A product data science system, packaged as agent skills. Most questions leave at intake without an analysis. The ones that stay get the method that matches how treatment was assigned, and the checks that decide whether the result is a result. What they teach is written back, so the next question starts smaller.

The map

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

Three method buckets, a floor underneath them, a memory above them, and a layer of judgment on top. Each bucket has its own question, its own output, and its own failure mode:

Experimentation and causal inference share all three, so they are one bucket separated only by who did the randomising: you, or the world.

Read the full map · Read the intake algorithm

Install

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

# any other agent, or none: a skill is a directory of markdown
cp -r gallop/skills/reading-experiments .claude/skills/

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

The five-minute path

One command, one synthetic dataset, every check once:

python -m gallop.examples.quickstart

Fifteen seconds of runtime: an MDE, an SRM verdict, an exposure ratio, a CUPED-adjusted effect with an always-valid interval, and the same effect shrunk toward a seeded prior store. The full loop, from a question arriving to the prior store changing on disk, is python examples/end-to-end/run_loop.py.

The skills

What this is not

Not an experimentation platform. It does not assign traffic, hold flags, or replace your warehouse. It assumes those exist and writes the part that decides whether the number they produced is true.

More

The site renders the skills and the two arguments: the map · the intake · the theory layer · install

Licence

MIT, see LICENSE.