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SkillsMP has collected 11 skills from pymc-labs/CausalPy. Open a skill to review its source and details.
Showing 11 of 11 collected skills.
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,…
Turn issues into PRs, handle commits, and run prek checks consistently.
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.
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…
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.
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.
Perform structured research and turn findings into an implementation plan.
Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.
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?"
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.