Content
61%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.
The body is well-structured and actionable with concrete Apify actor usage and a fallback mode, but is held back by duplicative output templates and missing validation checkpoints for batch/metered scraping operations.
Suggestions
Collapse the dual JSON + Markdown output sections into one consolidated output spec (or move the verbose templates to a reference file) to reduce token redundancy.
Add explicit validation checkpoints for batch/cost-incurring runs — e.g. verify each Apify actor result before proceeding, and confirm cost/competitor count before expanding Phase 5.
Provide runnable parsing snippets for backlink and keyword-rank actor outputs rather than only describing the extracted fields.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with compact command tables and per-phase actor inputs, but the intro restates the description and the full JSON plus full Markdown output templates are partly duplicative padding. | 3 / 5 |
Actionability | Provides concrete CLI commands, named Apify actors with input JSON, an explicit output schema, and a documented fallback — mostly executable with minor gaps in parsing glue. | 4 / 5 |
Workflow Clarity | Phases 1–6 are clearly sequenced with a priority-ordered competitor step, but batch/metered scraping runs lack validation checkpoints beyond a brief fallback note, so the batch-operation cap of 3 applies. | 3 / 5 |
Progressive Disclosure | Good section structure with a real referenced bundle file (scripts/analyze_domain.py) in Quick Start; inlined output templates are reasonable for a single-script skill, with only minor organization gaps. | 4 / 5 |
Total | 14 / 20 Passed |