Content
82%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, dense specialist skill: non-obvious domain data (benchmarks, thresholds, sample floors), a concrete audit checklist, and a copy-ready output template, with essentially zero padding. The main gaps are a few non-executable steps (how to pull RevenueCat/ASC metrics), no post-test feedback loop, and no reference split for the larger lookup tables.
Suggestions
Make the metrics-gathering step executable: specify exactly how to pull RevenueCat subscription metrics and cross-check trial counts via `asc-metrics` (exact dashboard path, API call, or a script), instead of the vague "pull subscription metrics first".
Add a post-test validation loop to the A/B Testing Playbook: when to read results (per the sample-size floors), how to judge significance, and when to roll back a losing variant — this would also close the workflow_clarity gap.
Consider moving the conditional lookup tables (placement strategy, pricing display patterns) and the output template into a `references/` file linked one level deep, keeping SKILL.md as the diagnostic overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and dense — nearly all content is domain-specific data Claude does not reliably know (funnel healthy ranges, red-flag thresholds, sample-size floors, test priority order), delivered as compact tables and checklists rather than prose. It assumes competence ("You are a paywall conversion specialist...") and contains essentially no explanation of concepts Claude already knows. It does not fit anchor 4 because there is no over-explanation to trim. | 5 / 5 |
Actionability | Mostly concrete and executable: the 7-element audit has specific criteria ("annual default-selected, savings %", "3–5 max, scannable in <3 seconds"), sample-size floors are quantified, test priorities are ordered, tools are named (Superwall, RevenueCat Experiments, Adapty), and a full output template is provided. It sits between anchors 4 and 5 because a few steps are soft — "If RevenueCat is connected, pull subscription metrics first" and "If `asc-metrics` is available, cross-check trial counts" give no concrete method, command, or screen for pulling the data. | 4 / 5 |
Workflow Clarity | The sequence is clear and gated — Initial Assessment → "Diagnose Before You Redesign" (funnel table with red-flag thresholds, "Optimization targets that stage only") → audit → test playbook ("Test ONE element at a time", "ship one per cycle") → output template — with a checklist for the complex audit. It is not 5 because there is no explicit post-test feedback loop (no step for reading experiment results, deciding significance, or rolling back a losing variant), so validation is front-loaded but the recovery/checkpoint side is thinner than the anchor-5 example. | 4 / 5 |
Progressive Disclosure | Good structure: well-organized sections with clear headers, a self-contained 135-line body, and clearly signaled cross-skill handoffs. At over 50 lines with several conditional lookup tables (placement strategy, pricing display patterns, sample sizes) and a full output template, the skill is somewhat larger than a lean overview, and this content could arguably live in one-level-deep reference files — a minor organization gap consistent with anchor 4 rather than the cleanly split, reference-navigated anchor-5 shape. | 4 / 5 |
Total | 17 / 20 Passed |