Content
61%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.
The body is strongly actionable with executable script invocations, clear preconditions, and useful output-interpretation examples, and it is organized into clean sections. Main weaknesses are the promotional footer wasting tokens, absent validation/feedback steps around the expensive training and budget-optimization workflows, and unenumerated option values for the script arguments.
Suggestions
Remove or trim the promotional "About this skill" footer to save ~12 lines of non-operational tokens.
Add explicit validation checkpoints, e.g. verify mmm_prepare_data passes before training, check the trained model's output before running optimization, and guidance on what to do when training or data validation fails.
Enumerate valid values for key arguments (platform options for --platform, allowed --kpi-type, --period, and --constraint-type values) so invocations need no guesswork.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The tables and script invocations are lean and operational, but the ~12-line promotional footer ("Want this skill (and 40+ more) running on autopilot... Try Synter"), Key Concepts definitions of standard MMM terms (Adstock, Hill Saturation), and arithmetic explanation ("Median 3.2 = every $1 spent returns $3.20") are unnecessary tokens that could be trimmed. Not 4 because the promotional footer alone is a substantial block of non-operational padding. | 3 / 5 |
Actionability | Provides copy-paste ready JSON invocations for the three main workflows (train, optimize, projections) with concrete args, plus credits, timing, and data requirements. Not 5 because valid option values are unenumerated (e.g. alternatives to "--constraint-type fixed", platforms beyond GOOGLE, other --kpi-type values), leaving minor gaps. | 4 / 5 |
Workflow Clarity | A clear numbered sequence exists (train first, then optimize, then projections) with stated prerequisites (52 weeks of data, connected platform), but there are no explicit validation checkpoints or error-recovery guidance. Model training is a batch operation (~30 min, 50 credits) and budget optimization affects spend decisions, so per the scoring notes the missing validation/feedback loops cap workflow clarity at 3. Not 2 because the sequence and preconditions are well defined, not just a rough outline. | 3 / 5 |
Progressive Disclosure | No bundle files exist, so the skill is a single well-sectioned SKILL.md with clear headers (When to Use, Scripts, Workflow, Data Requirements, Interpreting Results) and content appropriately placed inline at its size (~110 lines). Not 5 because the skill exceeds the under-50-line simple-skill threshold and bulkier material (full script argument reference, interpretation details) could be split into reference files. | 4 / 5 |
Total | 14 / 20 Passed |