# Agent prompts

These prompts are intentionally tool-neutral and assume PyMC-Marketing 1.x on Python 3.12+. They work as instructions for Codex, Claude Code, Cursor, or another coding agent with terminal access to this repository.

## Inspect a model first

> Read `README.md` and `AGENTS.md`. Confirm the environment is PyMC-Marketing 1.x; do not add 0.x compatibility code. Do not edit the sweep engine. Run `pmm-sweep inspect inputs/model.nc` and tell me which top-level PyMC-Marketing priors have matching posterior variables. In particular, report the current `adstock_alpha`, `saturation_lam`, `saturation_beta`, `intercept`, and control priors when present.

## Try tighter saturation priors

> Use `configs/my_sweep.yaml`. Keep the fitted distribution and dimensions for `saturation_beta`. Test sigma values 0.50, 0.40, 0.35, 0.30, 0.25, and 0.20. Do not change adstock or controls. Require acceptable PSIS overlap, at least 10% reweighting ESS, and no more than 3% relative RMSE in `channel_contribution_original_scale`. Run the sweep. Tell me the recommended candidate, Pareto k, reweighting ESS, contribution movement, and exact `Prior(...)` code for the production validation fit.

## Add adstock candidates

> Extend the prior grid to test `adstock_alpha = Beta(alpha, beta)` with alpha in [1, 2, 3] and beta in [3, 4, 5]. Keep the saturation grid unchanged. Run the product sweep and recommend the smallest prior change that satisfies the configured overlap and distortion constraints.

## Generate approximate future predictions

> Add `inputs/future.csv` under the `prediction.data` setting and run the sweep. Compare the baseline and recommended prior assumptions. Treat the generated future predictions as importance-reweighted approximations only. Do not call them a refitted model. Give me the exact prior patch I should use for the final real PyMC-Marketing MCMC validation run.

## Handle a rejected candidate

> If a candidate has Pareto k above the configured threshold or very low reweighting ESS, do not loosen the guardrail just to obtain a recommendation. Explain that the fitted posterior does not overlap enough with that prior. Propose a real refit with an intermediate prior, then use that refit as the new reference model for a second sweep.
