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Dépôt GitHub

CausalPy

CausalPy contient 11 skills collectées depuis pymc-labs, avec une couverture métier par dépôt et des pages de détail sur le site.

skills collectés
11
Stars
1.2k
mis à jour
2026-07-22
Forks
112
Couverture métier
3 catégories métier · 100% classifié
explorateur de dépôts

Skills dans ce dépôt

review-pr
Analystes en assurance qualité des logiciels et testeurs

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
Développeurs de logiciels

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

2026-07-22
github-issues
Développeurs de logiciels

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
Développeurs de logiciels

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
Scientifiques des données

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
Développeurs de logiciels

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
Développeurs de logiciels

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
Développeurs de logiciels

Perform structured research and turn findings into an implementation plan.

2026-06-08
working-with-marimo
Développeurs de logiciels

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

2026-06-08
causal-detective
Scientifiques des données

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
Scientifiques des données

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