decision-lab

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 if they don't use the word "decision-pack" explicitly. Trigger on phrases like "set up a new agent", "create an environment for X", "I want to run opencode for Y", "scaffold a project", or "make a new config".

How to create a dlab decision-pack directory using generate_dpack() from Python code

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 configuration. Covers the full workflow from scaffolding to analysis.

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 for any question of the form "When will X happen?" or "What is the probability that Y occurs by date Z?"

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 humility, when to stop, conflict detection, uncertainty reporting, retry protocols, prompt design principles, and the decision-lab runtime mechanics.

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 migrating PyMC3 distribution code.

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.