pymc-labs/CausalPy

review-pr

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,…

لغة النص الأصلي: الإنجليزية

pr-workflows

Turn issues into PRs, handle commits, and run prek checks consistently.

لغة النص الأصلي: الإنجليزية

github-issues

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.

لغة النص الأصلي: الإنجليزية

pr-to-green

Bring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.

لغة النص الأصلي: الإنجليزية

choosing-causalpy-methods

Choose the appropriate CausalPy experiment class from a causal or impact question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled, including plain-English questions…

لغة النص الأصلي: الإنجليزية

feature-exploration

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Use when implementation details are unclear and can be resolved by reading docs, inspecting code, and running focused experiments.

لغة النص الأصلي: الإنجليزية

python-environment

Detect, configure, and use a conda-compatible tool. Use before tasks that need the project environment, such as importing project code, running tests, building docs, or invoking repo tooling.

لغة النص الأصلي: الإنجليزية

research-and-planning

Perform structured research and turn findings into an implementation plan.

لغة النص الأصلي: الإنجليزية

working-with-marimo

Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.

لغة النص الأصلي: الإنجليزية

causal-detective

Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks. Use when validating whether a causal effect is real or when the user asks "is this effect real?" or "can I trust this result?"

لغة النص الأصلي: الإنجليزية

example-datasets

Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes. Use when the user needs sample data or asks which demo datasets are available.

لغة النص الأصلي: الإنجليزية