Content
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with executable commands and a clear report template, but it is verbose (overlapping platform guidance) and monolithic with no progressive disclosure, and the batch scrape workflow omits an explicit status/validation checkpoint.
Suggestions
Collapse the two platform tables and the Quick Selection Rules into a single selection guide to remove redundancy and save tokens.
Add an explicit checkpoint in Step 2 (e.g. run `requesthunt scrape status <job_id>` and only proceed to report generation once complete) so the batch scrape workflow has a validation loop.
Move the detailed command reference and/or API Info into a separate REFERENCE.md referenced one level deep, keeping SKILL.md as a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Commands and the report template are efficient, but platform selection is covered three overlapping ways (Platform Strengths table, Recommended Platforms by Category table, and Quick Selection Rules) that could be tightened into one. | 2 / 3 |
Actionability | Fully executable, copy-paste-ready commands with concrete flags (e.g. `requesthunt scrape start "smart home devices" --platforms youtube,reddit --depth 2`) plus a structured report template. | 3 / 3 |
Workflow Clarity | The three-step research workflow is sequenced, but the batch scrape step lacks an explicit validation checkpoint — `scrape status` appears only in a later Commands section and is not wired into the workflow before report generation. | 2 / 3 |
Progressive Disclosure | Well-organized with sections, but it is a ~210-line monolithic SKILL.md with no bundle files; the full command reference and platform tables are inline rather than split into one-level-deep references. | 2 / 3 |
Total | 9 / 12 Passed |