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SkillsMP hat 13 Skills aus pymc-labs/decision-lab gesammelt. Öffne einen Skill, um Quelle und Details zu prüfen.
Es werden 13 von 13 gesammelten Skills angezeigt.
Design agent system prompts, parallel architectures, and methodological guardrails for data science decision-packs. Use when creating orchestrator, subagent, or parallel agent systems for analytical workflows. Covers anti-fabrication rules, epistemic…
Quellsprache: Englisch
decision-lab house figure style for matplotlib. Use whenever creating, styling, or saving any matplotlib figure, chart, or plot. The environment is already styled — this skill covers only the rules the style config cannot enforce.
Quellsprache: Englisch
Complete reference for decision-lab (dlab). Use when the user asks about creating decision-packs, designing data science agents, running sessions, analyzing results, or anything related to dlab CLI, agent architecture, parallel subagents, or decision-pack…
Quellsprache: Englisch
Guide a human through creating a dlab decision-pack by asking questions and then calling generate_dpack(). Use this skill whenever the user wants to create, set up, or scaffold a new decision-pack, agent environment, or Docker-sandboxed config for dlab — even…
Quellsprache: Englisch
How to create a dlab decision-pack directory using generate_dpack() from Python code
Quellsprache: Englisch
Methodology for probabilistic forecasting of when and whether a future event will occur. Covers Bayesian survival models, reference class reasoning, driver threshold models, leading indicator models, scenario decomposition, and causal mechanism models. Use…
Quellsprache: Englisch
Navigate and analyze completed dlab session directories. Use when pointed at a work directory to understand what happened during a run — explore logs, outputs, parallel agent results, and the skills/prompts that shaped the analysis.
Quellsprache: Englisch
Visual language and UX patterns for Textual TUI applications in dlab
Quellsprache: Englisch
Expert on PyMC data management including pm.Data and pm.Minibatch for handling datasets, updating data containers, and mini-batch training. Use for data container errors or dataset handling issues.
Quellsprache: Englisch
Expert on PyMC probability distributions including continuous (Normal, Beta, Gamma), discrete (Poisson, Binomial), multivariate (MvNormal, Dirichlet), mixture, and timeseries distributions. Use when encountering distribution errors, parameter issues, or…
Quellsprache: Englisch
Expert guide on calculating and setting informative priors for PyMC-Marketing MMM models based on data characteristics and domain knowledge. Use when configuring priors for intercept, channel effects, or adstock parameters.
Quellsprache: Englisch
Expert on PyMC-Marketing's Marketing Mix Model (MMM) framework including adstock transformations, saturation functions, hierarchical models, and GAM components. Use for MMM modeling, prior configuration, or pymc-marketing API questions.
Quellsprache: Englisch
Expert on PyMC MCMC sampling methods including NUTS, HMC, Metropolis variants, and pm.sample() API. Use for sampling errors, convergence issues, sampler configuration, or trace-related problems.
Quellsprache: Englisch