Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, highly actionable skill body with executable code, explicit validation checkpoints, and clean one-level-deep references to a real bundle. The main weakness is the conceptual prose in the opening paragraphs, which explains domain context Claude largely already knows and could be trimmed.
Suggestions
Trim the opening prose (lines 17-19) explaining what an LLM 'might' do wrong and general Bayesian/MMM concepts; fold the genuinely non-obvious pitfalls (fit on full data, avoid flat priors, use BudgetOptimizerWrapper) into terse bullets under the relevant sections.
Consider condensing the long 'Plotting Methods Quick Reference' table by linking to references/plot.md sooner and keeping only the most-used methods inline, reducing token load for the common case.
Move the 'why' rationale for each section (e.g. the TVP explanation of when to use HSGP) into the corresponding reference file, leaving the SKILL.md body with just the runnable pattern and a one-line trigger.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is largely lean and code-driven, but the multi-paragraph prose explaining what an LLM 'might' do wrong ('Without it, an LLM might hold out test data ... use flat priors ...') and the conceptual framing of Bayesian inference states context Claude already knows, so it is mostly efficient but could be tightened. | 2 / 3 |
Actionability | Throughout, the skill provides fully executable, copy-paste-ready Python (MMM construction, fit calls, diagnostics like diverging_.sum() and az.summary, BudgetOptimizerWrapper, lift-test integration) with concrete signatures, matching the level-3 anchor. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced with explicit validation checkpoints: divergences 'must be 0', R-hat 'must be < 1.01', re-validate loops, and a full Typical MMM Workflow diagram with feedback gates. | 3 / 3 |
Progressive Disclosure | SKILL.md is an overview with well-signaled one-level-deep references (e.g. 'See [references/model_specification.md] for full constructor reference'), each referencing a real bundle file, and content is appropriately split into 9 reference files for easy navigation. | 3 / 3 |
Total | 11 / 12 Passed |