working-with-marimo
Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.
Explorez les skills IA publiés de pymc-labs et consultez leurs instructions source.
Skills indexés
8Étoiles des dépôts des skills indexés
5,526Forks du dépôt
476Moyenne des étoiles par skill indexé
691Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Interactive development in marimo notebooks with validation loops. Use for creating/editing marimo notebooks and verifying execution.
Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos.
Selects the appropriate quasi-experimental method (DiD, ITS, SC) based on data structure and research questions. Use when the user is unsure which method to apply.
Performs placebo-in-time sensitivity analysis to validate causal claims. Use when checking model robustness, verifying lack of pre-intervention effects, or ensuring observed effects are not spurious.
Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.
A clear description of what this skill does and when to use it. Include specific trigger keywords and task types that should activate this skill. Be specific about capabilities so the AI can decide when to load...
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-e...
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Ju...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.