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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.
Passed 17 of 22 test cases with the skill, and 12 of 22 without it.
and 13 more in the full report
8 fixed9 passed either way2 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are revamping our content team's pre-publish review process because our current checklist is bloated and reviewers are skimming it without catching real issues. Please design a streamlined editorial quality assurance workflow for us. Provide a structured guide covering the essential validation checks to run, how to action different finding types (such as halting publishing versus auto-fixing), and how to organize the review sequence so high-impact issues are caught first.
| Checked | Without skill | With skill |
|---|---|---|
| Lists brief adherence as the first check in the review sequence. | Missed | Passed |
| Designates fact accuracy as a halt condition that returns the draft to the writer. | Passed | Passed |
| Designates voice consistency findings as flags with editor suggestions rather than automatic halts. | Missed | Passed |
| Categorizes schema validation and SEO metadata updates as auto-fixes. | Missed | Passed |
| Assigns review accountability for each piece to a single QA owner rather than a committee. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
This skill spans pre-publish quality control. It plugs in at the END of every other content skill's output. The six-skill content suite distinction:
content-strategy is program scope: what to produce.pillar-content-architecture is hub scope: how the topical hub fits together.content-brief-authoring is per-piece scope: briefs each piece.content-and-copy is execution scope: writes each piece.programmatic-seo is scaled scope: generates many pages from data.Every skill above produces a draft. This skill is what gets drafts to publishable. It is the gate where quality is actually enforced.
The audience: editorial leads, content directors, in-house content QA, agencies with production lines, content ops managers, anyone running a writer (human or AI) and accountable for what ships. The voice is senior editor to junior editor or content marketer. Specific, opinionated, honest about where QA earns its keep versus where it becomes process theater.
The keystone distinction. Two failure modes plus the discipline.
Thin QA (typo-checking dressed as quality control). "I read it once, it is fine." Catches obvious mistakes; misses brief drift, voice inconsistency, hallucinated facts, structural problems, AI tells. Output: shipped content that is "fine but not memorable." Cost: invisible until a brand misstep, a hallucinated statistic, or a competitor's content compounds and the thin set falls behind.
Thick QA (47-item checklist nobody completes honestly). Every conceivable check listed; no triage. Reviewers either skim and check boxes (theater) or burn out under the cognitive load. Output: shipped content slightly more polished than thin QA produces, but the team's review velocity collapses. Cost: throughput drops; reviewers leave; the checklist atrophies into a few checks that actually run.
Catch-problems QA (the discipline). 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. Production volume drives sampling versus full-audit decisions. Reviewers are accountable for what they caught, not for box-completion.
test cases passed, vs 12 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.