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editorial-qa

Pre-publish QA framework for content. Brief adherence, voice consistency, fact accuracy, structure and clarity, AI-content audit, SEO and AEO compliance, internal linking and schema validation, QA at scale via sampling, the QA workflow, and the discipline that distinguishes catch-problems QA from checkbox QA. Triggers on editorial QA, content review, pre-publish review, content audit, content QA process, AI-content audit, hallucination check, content sampling, programmatic QA, voice consistency check, brief adherence check. Also triggers when a content team is shipping sloppy work, when AI-co-authored content is reaching publish unaudited, when a QA process burns reviewers out, or when a programmatic SEO set needs sampling discipline. On a voice consistency check, this skill owns checking a draft against an existing voice system at the publish gate; use `brand-voice` when the voice system itself needs defining or documenting.

skills/editorial-qa/SKILL.md

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# Editorial QA

A senior editor's playbook for pre-publish content QA. The discipline that catches problems BEFORE content ships, not after.

Most content QA is broken in one of two directions. The thin version is "did I read it once and is the spelling OK," which catches typos but misses brief drift, voice inconsistency, hallucinated facts, AI tells, and structural problems that reach readers as "this is fine but not memorable." The thick version is a 47-item checklist that nobody completes honestly because it is process theater: checkboxes nobody actually believes catch problems.

This skill is the discipline of catch-problems QA. Each check earns its keep by catching a specific class of failure that would reach readers if missed. Checks that do not catch anything get cut. Checks the production volume cannot sustain get redesigned (sampling instead of full audit, automated instead of manual). The QA framework is what is left when you remove the theater.

The skill covers three production shapes: single editorial pieces (one at a time, full QA), AI-generated drafts (with the AI-content audit that did not exist as prominently 2 years ago), and programmatic SEO sets at scale (sampling discipline, threshold gating). Each needs its own QA shape; the underlying methodology composes across all three.

When to use this skill: building a content QA process from scratch, auditing an existing QA process that ships sloppy work or burns out the team, designing QA gates for an AI-assisted workflow, or building sampling discipline for programmatic SEO sets.

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