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 concrete, executable API guidance and a clear phased workflow, but it is weakened by duplicated sections, missing error-recovery checkpoints across the multi-API batch process, and a monolithic structure that could offload detail to reference files.
Suggestions
Remove the duplicated Prerequisites/Tools Required and When to Use/Trigger Phrases sections to tighten token usage.
Add explicit error-recovery checkpoints for the batch scanning phase (e.g., what to do on empty web_search results, rate limits, or failed Apify runs) to lift workflow clarity.
Move the long per-platform API-call blocks and the full output template into reference files (e.g., references/api-calls.md, references/output-template.md) and summarize them in SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and concrete, but duplicates Prerequisites vs. Tools Required and When to Use vs. Trigger Phrases, plus minor motivational fluff ("before your competitors do") — the kind of tightening the score-2 anchor describes. | 2 / 3 |
Actionability | Provides fully executable, copy-paste-ready guidance: real web_search query templates, exact Apify HTTP endpoints with request bodies, Algolia HN API URLs with parameters, named output fields, and a weighted scoring table. | 3 / 3 |
Workflow Clarity | Phases are clearly sequenced with one explicit validation loop (Apify poll-until-SUCCEEDED), but the broader multi-API batch process lacks error-recovery checkpoints for failed queries or rate limits, capping it at 2 for batch operations. | 2 / 3 |
Progressive Disclosure | A single well-organized file with no nested references, but at ~290 lines much of the per-platform API-call detail and output template could be split into reference files rather than kept inline. | 2 / 3 |
Total | 9 / 12 Passed |