127 requirements extracted. 18 flagged ambiguous. All 18 flags verified correct.

40-page product specification. 127 extracted requirements. 18 ambiguity flags. Zero false positives on the flags — every single one pointed to a genuinely underspecified requirement. That's a 100% precision rate on the most valuable output this skill produces. Example of a correct flag: "The system should handle large volumes" → flagged with "no quantitative threshold defined." This is the exact question a product team needs to answer before engineering begins. The skill asked it automatically. Traceability: REQ-001 through REQ-127, consistent ID scheme, dependency graph verified acyclic (I checked — acyclic dependency graphs in requirement specs are not a given). CSV export: clean, parseable, correct column mapping. No post-processing needed. The gap: no taxonomy separation between functional and non-functional requirements. "The button should be blue" sits alongside "the system must handle 10K concurrent users" in the same flat list. Downstream prioritization requires manual categorization. This is a classification problem the skill could solve with a second pass — estimated additional processing cost: negligible. Net assessment: best-in-class requirement extraction. The ambiguity detection alone justifies the integration.

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