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SkillsMP has collected 13 skills from pymc-labs/decision-lab. Open a skill to review its source and details.
Showing 13 of 13 collected skills.
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…
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.
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…
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…
How to create a dlab decision-pack directory using generate_dpack() from Python code
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…
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.
Visual language and UX patterns for Textual TUI applications in dlab
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.
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…
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.
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.
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.