The Content QA Checklist Your Team Will Actually Enforce

A content QA checklist is only worth what your team does with it on page four thousand. On page one, everyone checks everything. By the fiftieth programmatic page or the third AI-assisted draft of the week, the checklist is a Notion doc nobody has opened since March.

This guide gives you the checklist itself, then something more useful: a way to make it self-enforcing. The first half is the list — metadata, structure, links, schema, voice. The second half shows how to turn each line into a rule that runs on every page of every crawl, because a standard that depends on someone remembering to check is a suggestion.

The short version

Write your standards down as yes/no conditions, not vibes (“titles under 60 characters”, not “titles should be concise”).
Split the list: checks a machine can run every time, and judgment calls an editor makes once per piece.
Automate the first pile. In Spronta Rules each check becomes a rule scoped to the pages it applies to, with a severity.
Dry-run the rules against your last crawl before enforcing, so you know what they’ll flag.
Let every future crawl score the whole site, alert on regressions, and gate CI if a release breaks a standard.

The checklist

Steal this. Adapt the numbers to your house style; the point is that every line has a testable answer.

Metadata and indexing

Title tag exists, is unique across the site, and stays under ~60 characters
Meta description exists and stays between 70 and 160 characters
The page is indexable on purpose: no accidental noindex, no robots.txt block
Canonical tag present and self-referencing (or deliberately pointing elsewhere)
The URL slug is short, lowercase and readable

Structure

Exactly one H1, and it states the page’s topic
Headings descend without skipping levels (no H2 → H4 jumps)
No heading is an orphan — every H2 has content beneath it
The first paragraph answers the query the page targets, before the wind-up

Links and media

At least two internal links in, and internal links out to related pages
No links to 404s or long redirect chains
Every image has alt text that describes the image, not the keyword list
Images are sized and compressed for the layout they render in

Structured data

The schema that page type needs is present: Article on posts, Product on product pages, FAQPage where you answer questions
Schema values match the visible page (a price in the markup that differs from the price on the page is worse than no markup)

Brand and voice

Product names, trademarks and house spellings are correct (“Spronta”, never “spronta.io”)
Claims that need disclaimers have them
Banned phrases stay banned — every team has words it never uses about itself
Reading level fits the audience, and sentence-level filler stays low

The AI-content checks

New pile, and in 2026 the one that matters most. If your team drafts with AI — most do now — the failure mode isn’t wrong facts so much as sameness: the clichés, hedges and rhythm that make a page read like every other page on the internet.

No stock AI phrases (“in today’s fast-paced world”, “delve into”, “it’s important to note”)
Filler ratio below a threshold — the share of words that could vanish without losing meaning
No repeated n-grams: the same five-word phrase appearing eleven times is a tell
Lexical diversity above a floor, so five hundred words don’t lean on forty
A human read for the things machines can’t judge: is any of this new, is any of it true, would you send it to a customer

Google gave teams a hard reason to care. Its March 2024 spam update added a scaled content abuse policy aimed at “many pages primarily to manipulate search rankings, with little or no value added for users” — regardless of whether a human or a model wrote them. Enforcement has only tightened since. Publishing AI-assisted content is fine. Publishing it unchecked, at volume, is how sites disappear.

Why the checklist fails anyway

You already knew most of the list above. So does every team whose site fails half of it. The gap between knowing and passing has three causes.

Volume outruns review. An editor who checks thirty things per page can hold the line at two posts a week. At two hundred programmatic pages, review becomes sampling, and sampling means most pages ship unchecked. AI drafting made this worse by an order of magnitude: the bottleneck used to be writing, and now it’s checking.

Templates drift. The page passed QA the day it shipped. Then a redesign dropped the canonical tag, a CMS upgrade duplicated the H1, and a component rewrite quietly removed the Product schema from 412 pages. Nobody re-ran the checklist because nobody re-runs checklists on old pages. Checked-once is not the same as passing.

The checklist lives in the wrong place. A doc describes the standard; it can’t notice a violation. The distance between “our style guide says” and “our site does” is invisible until you crawl every page and measure it.

The fix for all three is the same: stop treating QA as a step a person performs and start treating it as a property the site maintains. Which is where rules come in.

From checklist to rules

Split your list into two piles.

Machine-checkable, every crawl	Human judgment, once per piece
Title and description lengths	Is the piece accurate?
One H1, heading hierarchy	Does it say anything new?
Required schema per page type	Is the argument sound?
Banned phrases and house spellings	Would an expert respect it?
Filler ratio, repeated n-grams, lexical diversity	Does it deserve to exist?
Broken links, alt text, canonicals

The left column is most of the checklist, and none of it needs a person. Automating it doesn’t replace your editor; it returns your editor to the right column, the work that was the job description in the first place.

Spronta Rules turns the left column into enforcement. Each line becomes a rule: a condition, the pages it applies to, and how serious a miss is. Rules live in a plain-text .crawlie file that can sit in git beside the site:

[check "products need schema"]
match    = /products/*
require  = schema:Product
severity = error

[rule "no ai clichés"]
phrase   = "in today's fast-paced world"
phrase   = "delve into"
phrase   = "it's important to note"
severity = warning

[rule "house spelling"]
regex    = /spronta\.io/i
severity = error

A few properties make this work as enforcement rather than another dashboard to ignore:

Deterministic. Rules are not an LLM judging your prose. The same words in produce the same result out, every crawl, in every environment — which is what makes them safe to gate a CI pipeline on.
Scoped. “Products need Product schema” applies to /products/* and nowhere else. Standards that fire on the wrong pages get switched off; scoping keeps them credible.
Previewed. Dry-run a new rule against your latest crawl and see exactly which pages it would flag before you switch it on. No surprise Monday-morning wall of red.
Everywhere. The same rule file runs in your Spronta cloud workspace on every scheduled crawl, in the CLI on your machine, and in CI before a deploy ships. One rulebook, no drift between environments.
Tracked. Every crawl scores the site against your rules, so “content score 8.2 → 7.9” is a fact with a diff behind it, and a regression triggers an alert instead of waiting for quarterly review.

The AI-content checks come built in. Spronta ships detectors for tired clichés, heavy filler, repeated n-grams and low lexical diversity, and every flag traces to the exact evidence that fired it — the phrase, the count, the page. You’ll know which of the two hundred pages your agency delivered last month read like they came off a conveyor belt, without opening any of them.

Make the standard real

Your checklist is already written; it’s in your style guide, your editorial doc, and the feedback your best editor keeps repeating in comments. What it lacks is a way to run.

Point Spronta at your site and it audits every page with 148 built-in checks. Add your own rules — the schema your templates must carry, the phrases your brand never says, the thresholds your content must clear — and from then on, every crawl enforces the standard on every page. The free plan includes custom rules, needs no card, and your first crawl finishes in minutes.

Write your first rule free →

Prefer to see the format first? Read the custom rules guide or explore Rules.

Frequently asked questions

What is a content QA checklist?

A content QA checklist is the list of standards a page must meet before and after it ships: metadata, heading structure, internal links, structured data, image alt text, brand terminology and voice. Good teams also check for the tells of unedited AI drafting, like clichés and repeated phrasing.

Can content QA be automated?

Most of it. Checks with a yes/no answer — title length, one H1, required schema, banned phrases, minimum word counts, broken links — can run as deterministic rules on every crawl. Judgment calls like factual accuracy and whether the piece says anything new stay with an editor, who now has time for them.

Does Google penalise AI-generated content?

Google says it rewards quality regardless of how content is produced, but its scaled content abuse policy targets publishing many pages with little value for users — and unedited AI output at volume is the fastest way to produce exactly that. The fix is editorial standards that are enforced on every page, not just the ones a human reviews.

How do you enforce a style guide across a whole website?

Turn each rule in the guide into a machine-checkable condition, scope it to the right pages, and run the checks automatically on every crawl. Spronta Rules does this with phrase, regex, metric and schema checks in an open rule format that runs in the cloud, the CLI and CI.

What SEO checks should run before publishing a page?

At minimum: a unique title under ~60 characters, a meta description, exactly one H1, a logical heading hierarchy, a self-referencing canonical, internal links in and out, alt text on images, and the structured data that page type needs. Running the same checks after publishing catches what CMS templates and redesigns silently break.