Conciseness

The body is lean and every token earns its place: it carries only CausalPy-specific knowledge Claude cannot already know (lazy construction, GroupNotSampledException behavior, IPW stub methods, panel-regression caveats) with zero padding or explanation of basic concepts. It fully matches the 'lean and efficient' anchor.

Actionability

Guidance is highly concrete with real API calls and arguments ("exp.sample_prior_predictive()", "exp.plot(group='prior')", "exp.model = cp.pymc_models.LinearRegression(priors={...})") plus an explicit validation criterion ("P(effect > 0) should sit near 0.5"). It stops short of anchor 5 because there is no complete copy-paste-ready code block and constructor arguments are left as placeholders deferred to references.

Workflow Clarity

The seven-step workflow is clearly sequenced with an explicit pre-MCMC validation checkpoint (prior predictive check with a numeric criterion), a feedback loop for revising priors ("Revise priors by assigning a fresh model... assignment resets all results"), and error-recovery guidance via named exceptions that tell the user which call is missing. This matches the anchor 5 pattern of clear sequence, explicit validation, and feedback loops.

Progressive Disclosure

The SKILL.md body is a clean overview holding only cross-cutting guidance (workflow, guardrails, exceptions), while per-method detail is split into 13 well-labeled, one-level-deep references listed in a dedicated References section. This matches the anchor 5 pattern of a clear overview with well-signaled shallow references and easy navigation.

Specificity

The description lists several concrete actions ("Fit, summarize, plot, and interpret") tied to a named domain (a CausalPy experiment), with only minor coverage gaps such as sensitivity analysis. It exceeds anchor 3's 1-2 actions but falls short of anchor 5's comprehensive multi-action coverage.

Completeness

It explicitly answers both what ("Fit, summarize, plot, and interpret a chosen CausalPy experiment") and when ("Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors"). The trigger guidance is explicit and concrete, matching the anchor 5 pattern rather than the weaker 'when' of anchor 4.

Trigger Term Quality

Good keyword coverage including "CausalPy experiment", "PyMC", "sklearn", "scale-aware custom priors", and "causal method", but a few natural user terms are missing (e.g., "causal inference", "difference-in-differences", "synthetic control"). Coverage is clearly better than anchor 3's partial keyword set but not the comprehensive synonym coverage of anchor 5.

Distinctiveness Conflict Risk

It occupies a clear niche (executing CausalPy experiments) and explicitly sequences itself after the sibling method-selection skill ("Use after the causal method has been selected"), minimizing conflict risk. Named libraries (CausalPy, PyMC) make it hard to confuse with generic data-analysis skills.