pymc-labs
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- ▌ Python Environment · pymc-labs-causalpyDetect, 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.
- ▌ Feature Exploration · pymc-labs-causalpyExplore 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.
- ▌ Working With Marimo · pymc-labs-causalpy bundleInteractive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.
- ▌ Choosing Causalpy Methods · pymc-labs-causalpy bundleChoose 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 about whether a campaign, policy, or intervention worked.
- ▌ Pr To Green · pymc-labs-causalpyBring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.
- ▌ Github Issues · pymc-labs-causalpy bundleCreate, 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.
- ▌ Causal Detective · pymc-labs-causalpy bundleChallenge 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?"
- ▌ Example Datasets · pymc-labs-causalpyLoad 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.
- ▌ Review Pr · pymc-labs-causalpy bundleReview 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.