Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill with excellent token efficiency. The quick reference tables and decision matrices make it easy to navigate. The main weakness is the lack of explicit error handling and validation steps in the workflows, particularly for operations that could fail (blocked sites, empty results, API errors).
Suggestions
Add error handling guidance to workflows: what to do when Exa returns no results, when Firecrawl fails to scrape, or when API calls return errors
Include a validation step in the Deep Research workflow to verify content quality before synthesis (e.g., check if extracted text is meaningful, not just boilerplate)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, using tables for quick reference, minimal prose, and assuming Claude understands the concepts. No unnecessary explanations of what APIs or web scraping are. | 3 / 3 |
Actionability | Every endpoint has complete, copy-paste ready bash commands with full JSON payloads. Options are clearly documented with defaults and constraints. Examples are executable, not pseudocode. | 3 / 3 |
Workflow Clarity | Workflows are presented as checklists with clear sequences, but lack explicit validation/error handling steps. The 'Deep Research' workflow doesn't specify what to do if searches return poor results or if content extraction fails. | 2 / 3 |
Progressive Disclosure | Clear structure with quick reference tables upfront, detailed sections below, and appropriate references to external files (rules/getting-started.md, rules/when-to-use.md) that are one level deep and well-signaled. | 3 / 3 |
Total | 11 / 12 Passed |