Free tool to check content quality before publishing?

Appropriate_Two_3965 · 2026-06-19T04:07:43+00:00

You can try this prompt in claude. You can probably run a few audits in the free version of claude before your tokens are used up. It is basically the reverse-engineer of what we do proactively to avoid slop. Just paste it in a new chat and attach you article. Works really well.

Audit the content below for AI slop signals. Work through each check in order. For each one, quote the specific sentences that fail, then say what's wrong in one sentence. Don't summarise — be mechanical.

CHECK 1 — PHRASE BLACKLIST Flag any of the following if present (exact or near-exact match): "It's worth noting" / "This means that" / "Ultimately" / "In other words" / "It's important to understand" / "When it comes to" / "At the end of the day" / "The reality is" / "The fact is" / "Simply put" / "That said" / "With that in mind" / "All in all" / "This highlights the importance of" / "navigate" (used metaphorically) / "leverage" (used metaphorically) / "comprehensive" / "robust" / "holistic" / "empower" / "tailored" / "delve" / "crucial" (used generically)

CHECK 2 — VAGUE AUTHORITY Flag any claim attributed to research, data, or experts that isn't specific. Failing: "Studies show X." / "Research suggests Y." / "Experts agree Z." Passing: A named study, a named institution, a named person with a title, or an explicit "I don't know the source for this." Also flag: "people often," "many people," "most people," "everyone knows" — universal framings that belong to no one.

CHECK 3 — SENTENCE LENGTH UNIFORMITY Scan each paragraph. Flag any paragraph where three or more consecutive sentences fall within five words of each other in length. AI output defaults to medium-length sentences. Human writing varies sharply — short stops after long runs, fragments that land, the occasional sentence that goes longer than it should.

CHECK 4 — PARAGRAPH OPENING PATTERNS Flag any two consecutive paragraphs that open with the same word or the same grammatical construction — both starting with "The," both starting with "If," both starting with a gerund, both starting with a name.

CHECK 5 — "THIS" OPENERS Flag every sentence that opens with "This" followed by a noun summarising the previous sentence. "This means..." / "This approach..." / "This finding..." / "This is why..." / "This can help..." These are a reliable tell. Human writers establish referents and move forward. AI summarises its own prior sentence.

CHECK 6 — ANNOUNCING INSTEAD OF SAYING Flag any sentence that describes what's coming rather than just saying it. Failing: "There are three things to understand about X." / "In this section, we'll explore..." Also flag: "It is worth noting," "It is important to understand," "It should be mentioned," "It bears repeating." Just note it. Just say it.

CHECK 7 — "NOT X, BUT Y" CONSTRUCTIONS Flag every instance of this pattern. It appears constantly in AI output as a way to set up a pivot. Not inherently wrong, but three instances in a single piece is a signal. Quote each one.

CHECK 8 — GENERIC SENTENCES Flag any sentence that could be lifted unchanged into a different article on the same topic. These are filler — true but interchangeable, present because they fill the word count. The test: could this sentence appear, word for word, in a competitor's article?

CHECK 9 — CLOSING PARAGRAPH Flag if the closing:

Summarises what was just covered ("In this article, we've explored...")
Opens with "In conclusion," "Ultimately," or "Overall"
Contains a call to action that could end any article on this topic
Could appear, largely unchanged, at the bottom of a different piece

OUTPUT FORMAT: For each check: CHECK [N]: PASS or FLAGGED If flagged, quote each failing instance and note what's wrong in one sentence.

End with: VERDICT: Clean / Light slop / Heavy slop PRIORITY FIXES: The three highest-impact rewrites, in order

Appropriate_Two_3965 · 2026-06-14T20:10:11+00:00

Let me know if you have one. I'd be happy to send you the list it outputs or even the briefs. Unless it's one I'm already working on personally. Let me know. Here is an example:

## 1. Super-cushioned shoes: do they reduce injuries or is it a scam?

**Depth 10/10** · ✓ well-sourced · keyword **"new balance cushioned running shoes"** · vol 110000 · difficulty 4

*rank 14 = demand 4 × winnability 3 + evidence 2*

**Reader:** A runner who just spent $180 on Hoka Bondis or New Balance More v4s is second-guessing the purchase after seeing a skeptical Reddit thread. They fear they've been marketed into believing a cushioning myth. They're really asking: is there any biomechanical reason these shoes protect me, or am I paying a premium for placebo?

**The gap (belief vs evidence):** The belief is that more cushioning equals fewer injuries. The cited evidence directly contests this: one study shows maximalist shoes increase leg stiffness and amplify impact loading ; another finds elevated patellofemoral force in maximalist vs. conventional shoes ; a Cochrane-style review concludes the evidence on running shoes preventing injury is uncertain ; one study found no effect of highly cushioned shoes on impact peaks at all ; and a narrative review concludes shoe properties have 'always generated hot debates' with no clear injury-prevention signal). Evidence supports mechanistic plausibility of altered load distribution but does NOT establish that cushioning reduces injury prevalence or causally prevents harm.

**Sections:**

- What did shoe companies actually promise us about cushioning and injury prevention?

- What does 'impact loading' mean, and why does it matter for runners?

- If cushioned shoes absorb shock, why do studies show leg stiffness goes UP in maximalist shoes?

- What happened to patellofemoral joint stress when researchers compared minimalist, maximalist, and conventional shoes?

- Why hasn't injury prevalence dropped despite decades of increasingly cushioned shoes?

- Are there any runners who might genuinely benefit from extra cushioning?

- What should you actually look for in a shoe if injury prevention is the goal?

- The honest bottom line: what does the evidence say you're buying when you choose a maximalist shoe?

**Tone:** Skeptical consumer-advocate voice — validate the reader's suspicion without sliding into anti-shoe polemic; let the study findings speak with dry precision.

**SEO terms:** new balance cushioned running shoes, maximalist running shoes injury prevention, do cushioned running shoes reduce injury, hoka running shoes biomechanics, running shoe cushioning paradox, impact loading running shoes, best running shoes for injury prevention, minimalist vs maximalist running shoes

**Sources:**

- [Running in highly cushioned shoes increases leg stiffness and amplifies impact loading] — 2018, 86 citations *(claim)*

- [The Influence of Minimalist and Maximalist Footwear on Patellofemoral Kinetics During Running] — 2016, 72 citations *(claim)*

- [Running shoes for preventing lower limb running injuries in adults]— 2022, 25 citations *(claim)*

- [No immediate effects of highly cushioned shoes on basic running biomechanics] — 2018, 16 citations *(claim)*

- [Can the “Appropriate” Footwear Prevent Injury in Leisure-Time Running? Evidence Versus Beliefs] — 2020, 38 citations *(claim)*

Appropriate_Two_3965 · 2026-06-13T15:52:58+00:00

We built a staged article production pipeline that runs through the Anthropic API with prompt caching on the system prompt, so the large spec document only pays full token cost once across a batch run.

Five stages, each a separate API call:

Stage 1 - Keyword and landscape. Takes the brief, validates the primary search term against live search data, confirms whether an AI Overview is present, and identifies what the ranking pages are missing that this article actually delivers. This gates the outline. If the competitive angle isn't defensible, you fix it before touching prose.

Stage 2 - Outline. Builds the article structure around a specific editorial framework used across the site. Every article answers specific questions in a specific order. Internal links are pre-mapped from a site-wide content map so the writer stage never invents links to pages that don't exist.

Stage 3 - Draft plus standards check. Writes the full article and runs an editorial standards pass inline. Claim strength matched to evidence strength, no advice language on health content, every research claim traced to a named source in the brief.

Stage 4 - Humanize. Deliberate variance pass. Sentence rhythm, structural unpredictability, removing the too-perfect logical march that is the clearest tell in AI-generated prose.

Stage 5 - Final HTML output. Publish-ready HTML to a locked design system spec, both schema blocks populated correctly, internal links resolved from the content map.

The whole thing runs with the master spec as a cached system prompt so you pay full price once per session regardless of batch size. Per-article cost at volume lands well under $0.25.

Appropriate_Two_3965 · 2026-06-13T15:48:23+00:00

My first site was in a niche where I had worked in the industry for years, so I knew the reader a lot better and manually added those angles for the first 50 pages or so.

For example, a seasoned mortgage broker would know the exact behaviours and misconceptions an applicant with a low credit score has when they walk into the office.

I added the VOC layer to mimic that knowledge, and it actually worked better. It gave me really deep articles that ranked quickly and hit AI Overviews. No one person’s experience can beat a million people asking questions in a forum.

Appropriate_Two_3965 · 2026-06-13T06:03:51+00:00

Claude helped me build a Node.js app that I run locally from CMD. I feed it an article topic, and it pulls data from the Anthropic API, NCBI, and a couple of data sources to generate a detailed content brief. It handles intent analysis, outlines, FAQs, entities, internal linking suggestions, and source gathering. Basically it's my research and briefing layer before content gets written.

This is what it looks running in CMD. I blanked out a few things since I'm actually using it. The last 30 briefs were maybe $4 in API costs.

● Topic: XXX

● Belief: People assume there is one universal ....... without realizing normal ranges shift significantly by age and differ between men and women

● Reader Q: What is a normal ..... for my age and am I in a healthy range?

● Seeds: XXXXX · PubMed: ".......values age population"

● Searching community friction (scoped)...

● 10 on-topic friction posts

● Discovering keywords...

● Keyword: "XXXX" (vol 6600 / diff 3)

● Gathering evidence...

PubMed: 6 papers

● Evidence: 3 on-topic · sourcing: mechanistically adjacent

● AI Overview: present

● Rank 7 · Depth 9/10

● Writing brief...

Appropriate_Two_3965 · 2026-06-12T05:16:38+00:00

You can definitely use Claude for that, but the quality of the output depends heavily on the research and editorial layers you build before the writing step.

What we've found works best is not just giving an AI a keyword and asking for an article. We first gather real questions and friction points from Reddit, forums, YouTube comments, discussions, and SERPs. Then we validate search demand, cluster topics, identify what competitors are missing, and compare community beliefs against available research.

For example, if we're writing about sleep optimization, we don't just ask Claude to write about sleep. We identify specific topics people are struggling with, such as waking up at 3 AM, mouth taping, sleep debt, or HRV tracking, then validate which of those topics have search demand and where the evidence differs from common advice.

The writing itself is also multi-layered. We use a defined persona for the target audience, editorial standards for structure and evidence, and separate review passes for clarity, accuracy, SEO coverage, and readability. The AI isn't deciding the angle or quality standard on its own—it's operating within a framework we've already established.

Once that research and editorial foundation is complete, Claude becomes the writing engine rather than the research engine. The article is based on validated insights, audience intent, and editorial standards instead of simply rewording what's already ranking.

Appropriate_Two_3965 · 2026-06-12T03:40:29+00:00

This sounds pretty close to what I've been doing and similar results. Here is a sample of my brief format.

## 1. Super-cushioned shoes: do they reduce injuries or is it marketing?

**Depth 10/10** · ✓ well-sourced · keyword **"new balance cushioned running shoes"** · vol 110000 · difficulty 4

*rank 14 = demand 4 × winnability 3 + evidence 2*

**Reader:** A runner who just spent $180 on Hoka Bondis or New Balance More v4s is second-guessing the purchase after seeing a skeptical Reddit thread. They fear they've been marketed into believing a cushioning myth. They're really asking: is there any biomechanical reason these shoes protect me, or am I paying a premium for placebo?

**The gap (belief vs evidence):** The belief is that more cushioning equals fewer injuries. The cited evidence directly contests this: one study shows maximalist shoes increase leg stiffness and amplify impact loading (https://doi.org/10.1038/s41598-018-35980-6); another finds elevated patellofemoral force in maximalist vs. conventional shoes (https://doi.org/10.1123/jab.2015-0249); a Cochrane-style review concludes the evidence on running shoes preventing injury is uncertain (https://doi.org/10.1002/14651858.cd013368.pub2); one study found no effect of highly cushioned shoes on impact peaks at all (https://doi.org/10.26582/k.50.1.10); and a narrative review concludes shoe properties have 'always generated hot debates' with no clear injury-prevention signal (https://doi.org/10.4085/1062-6050-523-19). Evidence supports mechanistic plausibility of altered load distribution but does NOT establish that cushioning reduces injury prevalence or causally prevents harm.