Conciseness

The body is lean with no padding: it assumes familiarity with CausalPy, PyMC, ITS/SC, and pipelines, and every line either states a workflow step or a domain-specific concept the model would not already know. No verbosity to trim.

Actionability

Concrete API entities are named — 'PlaceboInTime with n_folds', '.run(experiment)', 'Pipeline + SensitivityAnalysis', 'theta_new', 'p_effect_outside_null' — but there is no executable code, no import path, and no example constructor or parameter values. This sits between minimal high-level hints (2) and mostly executable guidance (4): real, specific API names but missing key execution details.

Workflow Clarity

The four-step sequence (Fit → Configure → Run → Evaluate) is clear and ordered, with step 4 naming what to inspect. Not 5 because there are no validation checkpoints or interpretation criteria (e.g., what value of p_effect_outside_null indicates robustness), and no error-recovery guidance.

Progressive Disclosure

The single reference is clearly signaled with a description of its contents ('Core API reference, usage examples, and hierarchical status-quo modeling'), but the referenced file reference/placebo_in_time.md does not exist in the bundle — no references/, scripts/, or assets/ directory is present — so the pointer is broken. Structure and signaling are good, but navigation fails against the actual bundle.

Specificity

Names several concrete actions — 'placebo-in-time sensitivity analysis', 'hierarchical null model', 'optional Bayesian assurance' — each tied to a concrete capability, though coverage of the full analysis output is moderately narrow. Not 5 because it lists three actions without the breadth of the comprehensive example; not 3 because more than 1-2 specific actions are named.

Completeness

Explicitly answers both: 'Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance' (what) and 'Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power' (when, with three concrete trigger phrases).

Trigger Term Quality

'model robustness', 'verifying lack of pre-intervention effects', and 'estimating study power' are phrases a user would naturally say. Not 5 because common synonyms like 'placebo test', 'falsification test', or 'power analysis' are missing; not 3 because keyword coverage goes well beyond a single generic term.

Distinctiveness Conflict Risk

A clear niche — placebo-in-time sensitivity analysis with Bayesian assurance — with distinct trigger phrases that no general-purpose skill would claim. Conflict risk is minimal.