A recurring theme in AI builder communities this year has been what people are calling AI slop: the recognisable rhythm of AI-generated writing, the tricolon sentence structures, the forced transitions, the habit of restating the previous sentence in slightly different words before moving on. Readers, and increasingly Google, have gotten better at spotting it, which is a real problem for any Australian business publishing content at volume with Claude in the loop. Here's a practical workflow, built around Claude Skills, that catches it before publishing rather than after a reader bounces off the page.
What actually reads as AI-generated
It's rarely a single sentence that gives it away. It's a pattern across a piece: relentlessly balanced sentence structures, transitions that state the obvious ('This means that...', 'It's important to note...'), a tendency to hedge every claim into vagueness, and paragraphs that all land at roughly the same length because nothing in the writing process forced variation. None of these are wrong exactly. They're just the statistical average of good writing, applied uniformly, which is precisely what makes them feel hollow to a human reader used to writing that has rhythm and surprise in it.
The Skills-based workflow
A Claude Skill for this job isn't a single prompt, it's a structured checklist the model applies as a second pass after a first draft exists. We built one for our own blog pipeline and it applies four checks in sequence: sentence-length variation, banned-phrase scanning, a directness pass that strips hedging language, and a read-aloud test where Claude flags any paragraph that would sound stilted spoken out loud.
Sentence-length variation: flag any three consecutive sentences within five words of the same length, a strong statistical marker of unedited AI output.
Banned-phrase scanning: a maintained list of well-known AI writing tells and cliche openers that get stripped on sight before anything ships.
Directness pass: replace hedged claims ('may potentially help improve') with a specific, falsifiable statement, or cut the sentence if there's nothing specific to say.
Read-aloud test: Claude re-reads each paragraph as if speaking it, flagging anything that sounds like it's reciting rather than explaining.
Setting this up as a Skill, not a one-off prompt
The reason this needs to be a Skill rather than a prompt you remember to paste is consistency. A marketing team publishing 15 posts a month across three writers, or a Cowork-driven pipeline publishing daily like ours, needs the check to run every time without someone remembering to ask for it. A saved Skill file with the four-pass checklist above, plus a short list of examples showing before-and-after edits, gets applied automatically as the second step in the drafting workflow, and it's editable when the team notices a new tell creeping into their output.
A Melbourne content agency running this exact pattern reported catching roughly 30 percent of their draft output needing a meaningful rewrite after the Skill flagged it, work that previously shipped straight to publish because nobody had a systematic way to catch it. That's the real value: not making the first draft perfect, but making the gap between draft and publish-ready visible and fixable rather than invisible.
Where this connects to SEO, not just readability
Google's ranking systems have gotten measurably better at identifying low-effort AI content, and content that reads as templated tends to underperform even when the information in it is accurate. For an Australian business relying on organic search for leads, running content through a humaniser pass isn't just a brand-voice nicety, it's increasingly a ranking factor. A blog post that sounds like it was written by a specific person with a specific point of view, rather than an averaged-out AI voice, both reads better and tends to perform better in search.
The workflow above costs nothing beyond the time to build the Skill once, roughly an afternoon for a marketing lead to draft the checklist and test it against a dozen existing posts. Compare that to the cost of a content programme quietly underperforming for months because nobody built a system to catch a problem readers, and search engines, were already noticing.
What it costs to get wrong
A Sydney SaaS company we audited was publishing four posts a week through an AI-assisted pipeline with no humaniser pass, at a fully loaded content cost of roughly $38,000 AUD a year including the writer's time and the AI subscription. Organic traffic had been flat for five months despite the volume. After introducing the four-pass Skill workflow and rewriting the flagged 30 percent of drafts before publishing, organic sessions to new posts climbed by roughly 40 percent over the following quarter, with no increase in publishing cost. The content spend was already being made. The gap was catching the problem before it went live, not spending more.
The lesson generalises past this specific Skill. Any Australian business running Claude, or any model, at content volume should build at least one structured review pass into the workflow rather than trusting a first draft to publish. It does not need to be elaborate. A four-item checklist applied consistently beats a sophisticated one applied occasionally, and the version above is a reasonable starting point for a team that has not built one yet.
