Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an excellent skill document that demonstrates best practices across all dimensions. It provides a complete, actionable workflow for overlay detection and removal with executable code, clear validation steps, and appropriate progressive disclosure. The verification loop in Step 9 explicitly acknowledges limitations and provides agent-driven fallback strategies.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, assuming Claude's competence with Node.js, browser automation, and DOM manipulation. No unnecessary explanations of basic concepts; every section provides actionable information. | 3 / 3 |
Actionability | Provides fully executable bash commands, JavaScript code snippets, and complete JSON schemas. The workflow steps are copy-paste ready with concrete selectors, commands, and code examples. | 3 / 3 |
Workflow Clarity | Clear 10-step workflow with explicit validation (Step 9 verification loop), decision points (Step 6 strategy selection), and feedback loops for error recovery. The verification step explicitly handles edge cases the automated detection misses. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections (Workflow, Browser Tool Examples, Detection Report Format, Recipe Manifest Format, Agent Fallback, Watch Mode, Tips). References external file (known-patterns.md) appropriately for CMP-specific patterns without deep nesting. | 3 / 3 |
Total | 12 / 12 Passed |