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GitHub リポジトリ

CausalPy

CausalPy には pymc-labs から収集した 11 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
11
Stars
1.2k
更新
2026-07-22
Forks
112
職業カバレッジ
3 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

review-pr
ソフトウェア品質保証アナリスト・テスター

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.

2026-07-22
pr-workflows
ソフトウェア開発者

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

2026-07-22
github-issues
ソフトウェア開発者

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.

2026-06-10
pr-to-green
ソフトウェア開発者

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

2026-06-10
choosing-causalpy-methods
データサイエンティスト

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 about whether a campaign, policy, or intervention worked.

2026-06-10
feature-exploration
ソフトウェア開発者

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.

2026-06-08
python-environment
ソフトウェア開発者

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.

2026-06-08
research-and-planning
ソフトウェア開発者

Perform structured research and turn findings into an implementation plan.

2026-06-08
working-with-marimo
ソフトウェア開発者

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

2026-06-08
causal-detective
データサイエンティスト

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?"

2026-06-08
example-datasets
データサイエンティスト

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.

2026-06-08