Every section earns its place. The taxonomy table is dense and useful, the worked example demonstrates without padding, and there's no explanation of concepts Claude already knows. The 'Do not' section efficiently prevents common mistakes.

Highly actionable with concrete taxonomy, the two-interpretations test as a clear decision procedure, a fully worked example showing exact analysis, and a specific output format template. Claude can immediately apply this to any requirement.

The workflow is implicit but crystal clear: scan for signal words → apply two-interpretations test → generate structured output with questions and template. The worked example demonstrates the complete process with validation (checking context-dependent false positives).

Well-organized with clear sections progressing from taxonomy → test → example → caveats → output format. At ~100 lines, appropriately self-contained. References to 'requirement-enhancer' skill shows awareness of skill ecosystem without deep nesting.

Lists multiple specific concrete actions: 'Detects ambiguity', identifies 'weak words, dangling references, underspecified quantities, conflicting interpretations'. Provides concrete examples of what it catches.

Clearly answers both what (detects ambiguity in requirements with specific types listed) AND when (explicit 'Use when...' clause with multiple trigger scenarios: reviewing requirements, specs with vague words, conflicting interpretations).

Includes natural keywords users would say: 'requirements', 'spec', 'appropriate', 'fast'. The phrase 'two engineers read the same requirement and built different things' captures a real-world scenario users would describe.

Clear niche focused specifically on requirements ambiguity detection. Distinct from general code review, documentation, or other analysis skills. The specific focus on 'weak words', 'dangling references', and 'underspecified quantities' creates a unique trigger profile.