pymc-labs

Apply PyMC Labs' house style to every artifact you produce — reports, PDFs, slide decks, charts, images, notebooks. Use whenever you generate something a person will look at, before you deliver it.

Obtain CLI access tokens via the daimon MCP server's get_cli_token tool.

First-time workspace setup and agent-roster operations for a Daimon workspace — working repo, keys, skills, MCP servers, routines, and which changes need an admin.

Read images and large files without destroying the conversation. Use before reading any image — a screenshot you took, a chart you rendered, a photo the user sent — and before reading a file you have not sized. Reading an oversized image ends the conversation…

Publish interactive marimo notebooks via the daimon MCP server. Mint a one-time upload URL with create_notebook_upload_url, get the .py into a sandbox file, and curl -X PUT --data-binary it to the URL — source never goes through a tool argument, which…

Keep channels clean after posting. Use when a post, reply or status card of yours is out of date, empty, wrong, duplicated or failed, before posting a correction or a new status, when a thread you opened is finished, and when someone asks you to tidy a thread.

Publish a finished report as a page a named set of people can read and ask questions about. Covers gathering recipients and a cap, building the one archive the reading room expects, the size discipline for a large bundle, and the mint-then-upload-then-share…

Use when the user asks about team context, project status, decisions, or discussions that live in Slack or Discord, or names a daimon. Routes the question to the right daimon servers and merges their answers.

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Use when implementation details are unclear and can be resolved by reading docs, inspecting code, and running focused experiments.

Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns. Use when asked to review a PR, assess a branch before merge, summarize PR risks,…

Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks. Use when validating whether a causal effect is real or when the user asks "is this effect real?" or "can I trust this result?"

Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.

Choose the appropriate CausalPy experiment class from a causal or impact question, data structure, treatment assignment, and identification assumptions. Use before writing analysis code when the method is not yet settled, including plain-English questions…

Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes. Use when the user needs sample data or asks which demo datasets are available.

Bring a pull request to green by syncing with main, resolving conflicts safely, and fixing failing checks with CausalPy conventions.

Detect, configure, and use the project's Python environment (uv by default, conda-compatible tool as a fallback). Use before tasks that need the project environment, such as importing project code, running tests, building docs, or invoking repo tooling.

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

Use whenever creating or editing PyMC-Marketing documentation, including small updates to existing notebooks. Apply the repository's documentation conventions and notebook visibility policy.

Fixing and validating Jupyter notebooks in pymc-marketing. Use when notebooks fail CI, need arviz_plots 2.x migration fixes, or when source code changes require notebook output updates.

Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with…

Learn how to work in the current folder (repository)

PyMC-Marketing coding conventions, preferred implementations, and style guidelines.

Create git commits for changes made during the session.

Create an implementation based on a plan.

Create a plan based on document research through an interactive, iterative process.

Bayesian path analysis (observed-variable SEM) in PyMC. Compiles a lavaan-inspired formula DSL into a generative PyMC model, then layers introspection, identification diagnostics, the `do()` operator, and causal estimands (ATE/CATE/ATT/ATU/prob) on top. Use…

Autonomous bug-fix workflow. Guides the orchestrator through state detection, implementation, test/lint validation, and a bounded fix/review loop with an independent reviewer subagent. Use when fixing a GitHub bug report, or when the user says "fix bug",…

Author, configure, and distribute Agent Skills for a Great Docs site. Covers the three scenarios: automatic skill generation, adding a single hand-written skill, and distributing multiple named skills with a switcher page. Use when creating, editing, or…

Configure a Great Docs documentation site through great-docs.yml. Covers theming (navbar gradients, content glow, dark mode), hero sections, logos, announcements, sidebar options, page tags, page status badges, SEO, analytics, and deployment settings. Use…

Generate documentation sites for Python packages with Great Docs. Covers init, build, preview, configuration (great-docs.yml), API reference, CLI docs, user guides, theming, deployment, and the llms.txt agent-context files. Use when creating, configuring,…

Write and maintain narrative user-guide pages for a Great Docs site. Covers page creation, QMD frontmatter, section grouping, sidebar ordering, callouts, executable code cells, cross-references, and content guidelines. Use when adding, reorganizing, or…

A skill

A skill

A skill

Guide for using the dhub CLI — the AI skill manager for data science agents. Covers authentication, publishing, installing, running skills, managing API keys, eval reports, and troubleshooting. Use when users ask about dhub commands, skill publishing…

Guide for creating effective skills for Claude Code agents. Covers skill design, implementation, validation, packaging, and optionally runtime environments and automated evaluations for Decision Hub publishing. Use when users want to create, improve, or…

Build, revise, and debug Bayesian models with PyMC. Use when choosing likelihoods and priors, specifying model/data dimensions, fitting hierarchical models, GPs, time series, mixtures, BART or splines, using pymc-extras distributions, marginalization,…

Track PyMC experiments with MLflow, record sampling configuration and diagnostics, save and retrieve DataTree artifacts, and compare runs. Use for pymc_marketing.mlflow autologging, MMM model persistence and MLflow registry preparation, or CLV fit tracking.…

Elicit and check Bayesian prior assumptions in meaningful units. Use for expert quantiles, PreliZ, constrained priors, prior-predictive plausibility and prior sensitivity, including power scaling, regularized horseshoe and R2D2 assumptions.

Diagnose existing Bayesian inference output and evaluate fitted predictions with modern ArviZ DataTree summaries, divergences, rank R-hat, ESS and Monte Carlo precision. Use for posterior predictive checks, calibration, LOO/ELPD, Pareto k, stacking, grouped…

Build, differentiate, compile, and debug PyTensor symbolic computations for models. Use for symbolic tensor shape or dtype errors, named dimensions, indexing and broadcasting, vectorization and scan, numerical or sparse operations, symbolic RNG state, graph…

Load when the user is choosing priors, running prior predictive checks, calling find_constrained_prior, using PreliZ, or otherwise eliciting domain knowledge into a Bayesian model. Covers weakly informative priors, constrained priors, sensitivity analysis,…

Load when the user is working with pymc-extras (pmx) features: splines / BSplineBasis, distributional regression / GAMLSS, R2D2M2CP or horseshoe priors, discrete variable marginalization, or Laplace approximation via fit_laplace. Triggers include:…

Load whenever the user is working on code that imports pymc, pytensor, or arviz, or asks about Bayesian modeling, MCMC, priors, posteriors, sampling, or model diagnostics. Covers PyMC 6+, PyTensor 3+, ArviZ 1.1+ (DataTree API), pymc-bart, pymc-extras, nutpie,…

Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mock_sample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior…

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 Jupyter (.ipynb) to marimo, or when user mentions…

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-extras, nutpie, and JAX/NumPyro backends. Triggers…

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 Jupyter (.ipynb) to marimo, or when user mentions…

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 it. Maximum 1024 characters.

Open and operate the Mixlab MCP workbench to explore synthetic marketing mix data, adjust priors, run TyMC sampling and inspect convergence diagnostics. Use for explicit Mixlab requests or an active Mixlab session.