What I Do

This skill is the orientation map for the experiment-driven workflow — and the canonical catalog of every skill in this repo. It explains how the data-science commands fit together, tells you which one to invoke next, and lists every skill with its role. It does not implement anything itself — it routes.

Catalog governance: every skill in this repo must be registered here, below, before it is considered final. If a skill changes the experiment flow, the Main Flow and/or Flow Checks sections must be updated to reflect it. The README.md of the repo mirrors this file; this file wins on divergence.

Main Flow: Hypothesis → Notebook Experiment

You have a hypothesis and want to test it. The flow has four steps, in order:

1. /hypothesys — Understand and sharpen the user's hypothesis through a short interview. This is the first step: it clarifies the hypothesis, the premises, and the goal metrics of the experiment before anything is built.
2. /to-doc — Write the experiment doc for the idea to be developed. This fixes and consolidates the ideas, giving the next step a stable contract to implement against.
3. /experiment-pipeline — Transform the doc into a pipeline workflow, breaking it into small slices to run and tracking the experiment rounds.
4. /let-it-rip — Implement the hypothesis safely in a notebook.
   • Follows the rules of /expectation-driven-science so the science code stays rigorous (expectation before trust, vertical slices, deviation-as-information).
   • Every implementation is reviewed by /exp-review to ensure the code state is correct before moving on.