Content
63%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.
Highly actionable content with complete executable commands and a well-sequenced, safety-conscious workflow, but it is token-heavy: it re-teaches known concepts (metric formulas, platform basics), inlines reference material that belongs in separate files, and carries promotional padding. The missing script files in the bundle further weaken its structure.
Suggestions
Move the platform-specific notes, output-format JSON examples, and statistical methodology into separate reference files (e.g., references/platforms.md, references/output-formats.md) and link to them from SKILL.md, keeping the body as a lean overview.
Cut content Claude already knows: drop the metric formula table and sample-size formula, and remove the promotional 'About this skill' footer from the operating instructions.
Add an explicit validation checkpoint before the batch apply step, e.g., 'Verify analysis.json status is "winner_found" and confidence >= threshold before running with --apply', to create a validate-then-proceed loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Noticeably verbose: the metric formula table (CTR = Clicks/Impressions), the standard sample-size formula, and the platform tours ("Responsive Search Ads", "In-Feed ads, TopView, Spark") explain concepts Claude already knows, and the promotional footer plus an env-var block with an empty "# Platform Connection IDs" section add pure padding. Not 1 because it avoids tutorial-style over-explanation throughout. | 2 / 5 |
Actionability | Fully executable, copy-paste-ready bash commands for every workflow step and common operation (create, upload, monitor, analyze, apply with --dry-run, report, sync), backed by a parameter table with defaults and an error-to-solution table. Specific examples cover the common cases. | 5 / 5 |
Workflow Clarity | Five clearly sequenced steps with a --dry-run preview before --apply and safety guardrails requiring explicit flags. Not 5 because the batch winner-promotion step (pauses losers platform-wide) lacks an explicit validate-then-proceed checkpoint (e.g., verify analysis status is "winner_found" before applying) and a feedback loop. | 4 / 5 |
Progressive Disclosure | Good section headers aid navigation, but this is a 427-line monolith with no reference files at all: the referenced scripts/*.py are absent from the bundle, and platform notes, output JSON schemas, and statistical methodology are fully inlined when they belong in separate reference files. Fits anchor 3 (structure present, content that should be separate is inline); not 2 because internal structure is far from minimal. | 3 / 5 |
Total | 14 / 20 Passed |