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SkillsMP は pymc-labs/decision-lab から 13 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 13 件中 13 件を表示しています。
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…
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
How to create a dlab decision-pack directory using generate_dpack() from Python code
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
Visual language and UX patterns for Textual TUI applications in dlab
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語