Content
87%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 tight, highly actionable reference for a simple single-script skill: copy-paste commands, complete flag documentation, and a clear output schema with no wasted tokens. The only meaningful gap is operational safety — the paid, batch nature of the scraping runs gets no validation or error-recovery guidance, capping workflow clarity at 3.
Suggestions
Add a short validation/error-handling step, e.g. "If the run times out or returns zero reviews, check the actor run status and retry with a higher --timeout or lower --max-reviews" — this would lift workflow clarity past the batch-operation cap.
Add one line of cost awareness before the first command (e.g. "Capterra is pay-per-result; keep --max-reviews low when testing") since the platform table shows metered pricing.
Show a one-line example of the --output summary format so users can verify the scrape produced usable data.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and dense — a short Quick Start with runnable commands, a platform/actor table, a flag table, and an output schema — with no explanation of concepts Claude already knows and no padding, matching anchor 5 ("Lean and efficient; every token earns its place"). | 5 / 5 |
Actionability | Copy-paste-ready commands for all three platforms with realistic URLs and flags, plus a complete CLI reference table with defaults and a concrete normalized output schema, matching anchor 5 ("Fully executable; copy-paste ready code or commands; specific examples cover the common cases"). The referenced script scripts/scrape_reviews.py exists in the bundle and its usage matches the documented examples. | 5 / 5 |
Workflow Clarity | The single action (run the script with --platform and --url) is unambiguous, but this is a batch operation against a paid, metered Apify API (--max-reviews 50, pay-per-result actors, --timeout 300) and the body includes no validation or verification guidance — no error handling for failed/timed-out runs, no check that output was produced before re-running. The rubric's batch-operation cap ("a destructive or batch skill without validation cannot score above 3, even if single-purpose") applies, overriding the simple-skill exception. | 3 / 5 |
Progressive Disclosure | A compact, single-purpose skill with clearly organized sections (Quick Start, Supported Platforms, CLI Reference, Normalized Output Schema) where all inline content is essential reference material and nothing belongs in a separate file. The one bundle file (scripts/scrape_reviews.py) is referenced with a correct, working path — matching the simple-skill pattern the rubric allows to score 5. | 5 / 5 |
Total | 18 / 20 Passed |