model-implementation-review

name	model-implementation-review
description	Review statistical, machine-learning, simulation, or data-transformation implementations before merging.

Model Implementation Review

Use this skill for changes to likelihoods, optimizers, training loops, prediction paths, simulation engines, or nontrivial data transformations.

Review Checklist

• Is the public contract documented before the internal mechanics?
• Are constrained parameters represented on stable internal scales?
• Are matrix dimensions, grouping levels, feature columns, and prediction-time shapes checked?
• Are constants, offsets, weights, missing values, and factor levels handled consistently?
• Are random seeds, stochastic schedules, and resampling splits reproducible?
• Do tests cover ordinary cases, boundary cases, and malformed inputs?
• Does the user-facing output report quantities on interpretable scales?
• Does the roadmap avoid describing planned behaviour as implemented?

For statistical models, also check:

• likelihood constants and link functions;
• gradients or optimizer diagnostics when available;
• simulation recovery;
• comparison to independent calculations when possible.

For machine-learning models, also check:

• leakage guards;
• calibration or uncertainty claims;
• train-time versus predict-time preprocessing parity;
• serialization and reload behaviour.