// Perform structured research and turn findings into an implementation plan.
Choose the appropriate CausalPy experiment class from a causal question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled.
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.
Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns. Use when asked to review a PR, assess a branch before merge, summarize PR risks, or request changes.
Create, evaluate, and triage GitHub issues for CausalPy. Use when filing a bug, proposing an enhancement, analyzing existing issues, or splitting large work into parent-child sub-issues.
Bring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.
Turn issues into PRs, handle commits, and run prek checks consistently.
| name | research-and-planning |
| description | Perform structured research and turn findings into an implementation plan. |
Use this skill to clarify requirements and produce a plan before implementation.
