Content
88%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A strong, highly actionable skill body: executable code with explicit diagnostic thresholds, a clear end-to-end workflow, and a well-wired one-level reference bundle. Weakest points are token efficiency (repeated full-dataset emphasis, narrative intro) and slightly oversized inline API listings that belong in reference files.
Suggestions
State the 'final fit always uses the full dataset; time-slice CV is only for stability assessment' rule once (e.g., in Data Preparation or Inference and Diagnostics) and remove the duplicate mentions in the intro and Quick Start comment.
Move the mmm.summary.* method listing out of SKILL.md into a reference file (e.g., references/summary.md or an extended references/plot.md), keeping only 3-4 most-used methods inline.
Trim the two introductory narrative paragraphs into a compact 'Common mistakes' bullet list (hold-out test data for final fit, flat saturation priors, scaled-space contributions, calling BudgetOptimizer directly) to preserve the same warnings in fewer tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean, code-forward, and assumes PyMC competence (no basic-concept explanations), but the 'fit on the full dataset' warning is repeated four times (intro, Quick Start comment, Data Preparation, Inference) and the two introductory narrative paragraphs could be condensed into a short pitfalls list. Efficient overall with minor trimmable instances. | 4 / 5 |
Actionability | The Quick Start is fully executable copy-paste code, and every section gives exact imports, API calls, and hard thresholds ('divergences must be 0', 'R-hat must be < 1.01', named lift-test columns 'channel, x, delta_x, sigma'). Specific examples cover the common cases end-to-end. | 5 / 5 |
Workflow Clarity | The 'Typical MMM Workflow' section gives a clearly ordered sequence (EDA → specification → build → prior predictive → fit on full data → diagnostics → posterior predictive → analysis → optional CV → optimization) with explicit validation checkpoints (divergences, R-hat, trace plots) and optional steps marked. Fit troubleshooting is signaled via the model_fit.md reference. | 5 / 5 |
Progressive Disclosure | All nine references/*.md files exist and every link in the body resolves one level deep with clear 'See references/...' signaling. However, the 22-row plotting method table and the 20-line mmm.summary.* method listing are bulk API detail inlined in SKILL.md that would fit better in references/plot.md or a dedicated summary reference, leaving minor organization gaps. | 4 / 5 |
Total | 18 / 20 Passed |