Content
76%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 well-structured, highly actionable skill body with concrete commands, config, and output templates. The main gap is the absence of validation/verification checkpoints for a batch scraping workflow, which caps workflow clarity.
Suggestions
Add a validation checkpoint after the scraping phases (e.g. verify each KOL returned results, retry or flag on empty/error responses) to lift workflow clarity above 3.
Move the Phase 4 output-format template into a reference file (e.g. references/output-template.md) and link to it, improving progressive disclosure.
Tighten editorial passages like 'Core principle' and the 'broad consensus vs. individual takes' note to improve token efficiency.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and actionable with minimal concept restating; minor editorializing ('Core principle', the 'broad consensus vs. individual takes' aside) could be trimmed. | 4 / 5 |
Actionability | Copy-paste-ready bash commands with exact script paths and flags, a concrete config JSON, explicit engagement thresholds, and a full output template cover the common cases. | 5 / 5 |
Workflow Clarity | Phases 0–5 are clearly sequenced, but this batch operation across multiple KOLs and platforms has no validation/verification checkpoints (e.g. empty-result or rate-limit handling), capping the score at 3 per the batch-operation rule. | 3 / 5 |
Progressive Disclosure | Well-organized into clearly labeled phases with no nested references and no bundle files to navigate; the long inline output template (Phase 4) is the main piece that could live in a separate reference file. | 4 / 5 |
Total | 16 / 20 Passed |