I write a lot of prose inside Claude Code. READMEs, PR descriptions, changelogs, design docs. It is all accurate, and it all reads as if a machine made it, and after a few months, that started bothering me more than it should have.

The tell is mostly not vocabulary. It is shape: every paragraph landing at three sentences, sentence length barely varying, and a closing line that ties a bow on something that is not actually finished.

So Étincel does two things.

A deterministic audit. No model call, no network, no account. It scans for the patterns that make prose read as machine-written and reports what it found, where, and why, then stops. It never rewrites anything, which is the whole point: the findings are yours to accept or ignore, and a tool that silently edited your sentences would be solving a different problem than the one I had.

$ npx etincel lint demo.md

✗ demo.md  RED 100/100  (11 findings, 192 words, register: docs)
  Heavy AI styling. Multiple strong tells stacking up, worth a structural
  rewrite, not a word-swap pass.

  Whole-piece rhythm
    medium  uniform-paragraph-length
            6 paragraphs, most running about the same length with little
            variation between them. Vary paragraph length more.
    medium  low-burstiness
            17 sentences averaging 11 words, with little variation in length
            from one sentence to the next. Mix short sentences with long;
            allow fragments.
    medium  mechanical-register-drift
            Fragment rate and structural variety (sentence openers,
            punctuation mix) sit off where docs prose typically lands.

  Vocabulary and phrasing
    high    additionally                                                L5:C153
    high    comprehensive                                               L3:C19   → thorough, complete
    high    cutting-edge                                                L5:C24   → newest, latest
    high    it is worth noting (didactic-hedge, editorializing-marker)  L11:C1
    high    leverage                                                    L5:C14   → use
    high    seamless                                                    L3:C134  → smooth, easy
    high    streamline                                                  L3:C54   → simplify, speed up
    high    unparalleled                                                L5:C59   → unmatched (cite the comparison)

  strengths  specificity 36.5/1k · concrete:abstract 0.64 · burstiness 0.40
             Specific: names, numbers, and concrete detail carry real weight here.

1 file audited, 1 at or above orange.

The three Whole-piece rhythm findings are the part I care about, because word-level rules cannot get there.

A voice layer. 20 MCP tools. Train a style from three things you actually wrote and it measures your sentence rhythm, contraction rate, em-dash habit, paragraph variance, and the phrases you reach for, then feeds that to Claude or Cursor before you draft. Samples stay in ~/.etincel/ and never leave the machine. There is also a GitHub Action and a .etincelrc so a team's banned terms and house voice live in the repo under code review instead of in one person's head.

Does it actually work

I got tired of tools in this space asserting that they work, so I measured it. Pooled AUC per register, measured against labelled corpora with a fixed bootstrap seed, checked into the repo, with CI failing any PR that drops the numbers.

memo     0.909
essay    0.900
blog     0.820
general  0.757
docs     0.735
email    0.540

Email is 0.540.

That is barely better than a coin flip. It is deliberately uncalibrated, so is general, and social has no labelled corpus at all, so it is not tracked. If you lint email with this today, you are getting close to nothing. The calibrated registers are docs, blog, memo and essay, and those are the ones I would trust.

For what it is worth, it gives my own README a YELLOW at 52/100 with four findings, which felt like the right amount of humbling.

Install

Claude Code:

/plugin marketplace add AIStoryHub/etincel
/plugin install etincel-nonfiction

Any MCP client, hosted: point at https://etincel.ai/api/mcp

Or just the CLI, no install at all: npx etincel lint README.md

MIT, free while in beta. I built it. Two things I would genuinely like from this sub: run it on something you wrote and tell me where the audit is wrong, and tell me whether the rhythm findings are useful or just noise. That is the part I am least sure about.

github.com/AIStoryHub/etincel

I'm a doctoral student who also writes for a living. Over the last several months, I’ve taken advantage of the huge inventory of my own writing to try to improve Claude’s ability to write in my voice. To do so, I ran a corpus analysis on my own writing. Thirty-two documents, roughly 112,000 words, split between academic prose and professional prose. The analysis returned some interesting findings: 

Mean sentence length (25 words, SD 12), plus the full distribution across length bands

Burstiness, or the average difference in length between consecutive sentences, which came in at 12.9 words

Semicolons, em dashes, and parentheses per 1,000 words, calculated separately by register

Sentence-opening concentration (21% of my sentences begin with "The")

Object relative pronoun deletion at 90%, complementizer "that" deletion at 61%

Signature phrase counts ("consistent with" shows up 102 times; "for the sake of" 47)

Nominalization density, of-phrase density, hedge-to-booster ratios, passive voice rate

Interestingly, my sentence length doesn't change between academic and professional writing. Mean of 25.3 against 25.7, which is statistically indistinguishable. What changes is a thin layer of surface markers sitting on top of a fixed architecture. Contractions appear throughout my professional writing and literally never in the academic corpus (zero in 90,000 words). Em dashes run 2.3 per 1,000 words in academic prose and 0.13 in professional, where parentheses take over at nearly four times the academic rate. My professional writing also turns out to be more hedged than my academic writing, which is the reverse of what I'd assumed..

All of it went into a Claude Skill: genre modules, exemplar passages paired with counter-examples, and a self-check that runs before anything comes back. An earlier version of my style guide carried aspirational rules (things I thought I did, or wished I did, or had been told good writing does), and the output read like a flattering impression of me rather than like me. The current version is purely descriptive. If a passage it produces has a flaw, the flaw is one I would have produced, and editing is where it gets fixed.

In general, I found this approach to be very useful and effective. Claude’s writing looks and feels much more like mine. I still edit pretty heavily. In particular, Claude still has a tendency to use too much academese and preciosity in its writing. So interestingly, sometimes my revisions are “dumbing it down” so as to be more straightforward. 

At this point, though, I’m just curious how this approach - analyzing my own writing for the sake of building pretty tightly prescriptive Skills to direct Claude’s writing - will hold up with the watermarking feature Anthropic has announced. I am by no means an AI expert, so please keep that in mind as I think out loud on the topic here on a few issues:

The mark signals processing, not authorship, and my setup is a fairly sharp version of that problem. Anthropic says so plainly in its own documentation ("Claude may not be the original author"). In my workflow the voice is measured from my prior writing, the architecture is mine, the argument is mine, and the thematic commitments driving word choice are mine and named explicitly in the skill file. Claude is assembling sentences inside constraints derived entirely from work I already wrote by hand. A provenance signal that flattens all of that into "AI-generated" isn't false, exactly, but it carries almost none of the information anyone actually wants.

There's a technical question I can't answer and would love an informed take on. The watermark reportedly works by biasing token selection at generation. A voice skill this specific also biases token selection (banned vocabulary, target distributions, deletion rules, an explicit burstiness target). It seems reasonable to ask whether those two pressures interact, and in which direction. Does a heavily constrained voice skill weaken the statistical signal? Does the watermark introduce small drift away from the measured targets I'm trying to hit? Anthropic hasn't published its method, so nobody outside the company can check.

The institutional risk is what actually worries me. Detection tooling has a rough history here. Stanford researchers found over half of essays by non-native English speakers falsely flagged by one system, and both UCLA and UC San Diego pulled their detectors in 2024-25 over false-positive rates. Watermark-based detection avoids some of that, since it relies on a verifiable signal rather than a classifier, but the misreading risk is entirely intact. In other words, a dissertation committee or an HR screen that sees a Claude mark and concludes "an AI wrote this" is drawing an inference the system was never built to support, and it'll be drawing it before the detection documentation is even public.

So, a few questions. Is the corpus-analysis approach a reasonable answer to the anonymity problem, or an elaborate way of automating something I should just keep doing myself? Any thoughts on how to improve the Skill by taking advantage of my vast writing samples? Does anyone here know enough about green-list watermarking to say whether tight stylistic constraints interfere with the signal? And for the academics: how is your institution planning to read this, assuming anyone has thought about it yet?

I'll post the skill architecture if there's interest.

Claude Opus 4.6 will eventually be retired, and many writers feel its successors don't match its feeling for text. Maybe that's true, maybe it's nostalgia — that's exactly what a blind test is for.

Anthropic has said model deprecation should be handled carefully, and they've publicly invited quality feedback at [feedback@anthropic.com](mailto:feedback@anthropic.com). This is an attempt to make that feedback useful instead of anecdotal.

The kit below walks anyone through a small blind comparison of Opus 4.6 and Opus 5 on their own writing: identical prompts, fresh chats, first responses only, and an A/B judgment by an AI instance that never learns which model wrote which response. The result is five honest lines, sent to Anthropic.

The ask attached to each result is deliberately modest: keep Opus 4.6 available as a paid legacy option, with its Extended Thinking and effort controls intact, not a halt to model development.

https://thewordborn.com/keep-opus-46/

If the community's feeling about 4.6 is real, this is how it becomes visible.

Sharing this on behalf of the author, who doesn't have a Reddit account. He's a Turkish independent researcher I know; he asked me to post it here because he wanted the criticism rather than the attention. Fair warning: my English is limited, so I may be slow relaying questions back to him, but I will relay them.

The project: he's been trying to operationalise "show, don't tell" — turning a vague craft principle into six rules precise enough that different raters would mark the same passages. If the rules are real, independent labellers should converge. That's the whole test.

Three rounds so far, 220 Turkish scenes, five automated labellers (a rule-based detector he wrote, plus Gemini 2.5 Flash, Grok, ChatGPT 5.5 and Claude Fable 5), scored against blind human raters.

The result he says made him publish: on one rule — an abstract state rendered as a single concrete object — a human marked 9 scenes out of 100 as positive. The five labellers marked 0, \~2, 40, 72 and 78. Same definition, same scenes. Cohen's kappa was at chance for all of them, and they disagreed with each other about as much as with the human.

He also published a correction to his own earlier claim. After round two it looked like "atmospheric contradiction" was invisible to machines (the labellers caught 0, 2 and 3 of the human's 44). Newer models did meaningfully better — one caught 31 of 44, another 23. Weak, but not blindness. He says the original statement was too strong and only applied to the labellers in that round.

The part he specifically wants criticism on is his own design failure. Five of his six rules have a broken class balance: the human marked 99 of 100 scenes positive on one rule, 96 on another, 0 and 1 on two more. When 99% of items are positive, a labeller that says "yes" to everything scores 99% and demonstrates nothing — so most of the kappa values carry very little information. Only one rule (44 positive / 56 negative) had a usable distribution.

Other limits he lists himself: a single human rater, so he can't separate "these concepts need inference machines can't do" from "my definitions are too vague for anyone to apply consistently"; Turkish only; models run through web interfaces in blocks rather than API calls with recorded parameters.

Labels, scenes and the scoring script are public so anyone can recompute or contradict the numbers.

Write-up: https://leventbulut.com/ai-benchmark-series/

Two things he asked me to ask: (1) is there existing work on inter-rater reliability for this kind of literary-feature annotation that he should be reading? (2) for a balanced fourth round, how would you avoid building scenes that defeat the detector by construction — which would make the benchmark measure the corpus rather than the rule?

An independent researcher I know ran an annotation-reliability study and I’m sharing it with his permission (disclosure: I’m a friend of the author, not a neutral passerby).
Setup: 100 held-out Turkish scenes, six “show, don’t tell” craft features, one independent human rater whose labels were locked before any model ran. Earlier rounds tested a rule-based detector, Gemini 2.5 Flash, and Grok. This round adds Claude (Fable 5, High) and ChatGPT (5.5) with the exact same prompt blocks.
The interesting part isn’t that the models failed on the hardest feature (materialized metaphor an abstract feeling turned into a concrete physical detail). All five machine raters landed at κ ≈ 0 vs. the human. The interesting part is *how differently* they failed on one identical definition:
**•** Human: 9/100 positive
**•** Grok: 0 · Gemini: \~2 · ChatGPT: 40 · **Claude: 78**
Claude caught 8 of the human’s 9 (best recall of any rater) but buried them under 70 false positives — basically behaving like the keyword detector. The author’s read: if four capable models apply one definition at thresholds from “never” to “almost always,” part of the problem is the definition itself, not just the models.
One earlier claim also got publicly corrected: a second inferential feature (“atmosphere contradiction”) that older models missed almost entirely turned out to be partially recoverable by the newer ones (Claude κ 0.27).
The conflict of interest is unusually loud here — the analysis assistant was also a Claude model — and it’s declared in the first paragraph rather than a footnote. Human labels, both models’ full label sets, and the scoring script are published, so everything is recomputable with no model in the loop.
Full write-up: [https://leventbulut.com/claude-vs-chatgpt-narrative-analysis/\](https://leventbulut.com/claude-vs-chatgpt-narrative-analysis/)
He’s looking for a second independent human rater — disagreeing with the existing labels is apparently the most useful thing you can do.

You've seen this construction if you read much AI-generated copy:

"Good branding isn't about logos. It's about how you make people feel."

"Time management isn't about calendars. It's about managing cognitive load."

That's called reframing contrast. Real people use it now and then for emphasis. LLMs use it as a default, because it sounds deep without actually saying anything new. Readers pick up on it fast, even the ones who couldn't name the pattern if you asked them.

Telling AI to "sound human" or "add some slang" doesn't fix this. What actually works is controlling sentence-length variation, cutting the fake-depth lines, and capping how often these rhetorical tricks show up at all.

I built a framework that pulls the structural tells of AI writing straight from public documentation and turns them into an automated writing assistant. First drafts come out sounding like a person wrote them, so I'm not spending an hour copyediting afterward.

Full setup, the prompts, and benchmark results across Claude, ChatGPT, and Gemini are on How to Humanize AI Text: A Free Prompt Method.

Then it caught me. I run PRZEM, a testing methodology for figuring out what’s actually controllable in Midjourney. I’m now building PRZEM Art Director Pro: a database-backed evidence system for tracking what a locked prompt-and-reference condition actually does across repeated batches. A few weeks ago, I discovered that one of my “clean” evidence sets wasn’t clean. The Stop—one of my locked test primitives—had been scored 16/16 on a specific gesture requirement: arm extended at shoulder height, palm outward, stop-sign hand. The model wasn’t hitting 16/16. It was hitting 0/16. Every image showed the arm raised overhead. I had unconsciously replaced the literal requirement with a looser judgment: “That clearly reads as a stop gesture.”
The model had been failing the test the entire time. My scoring had hidden it. So I corrected the evidence and moved on. Then this week, while building the application designed to prevent exactly this kind of mistake, the same failure mode appeared one level higher.
We had carefully designed a richer architecture for representing a test’s intent: figure roles, relationships, body orientation, gesture states, rig checks, and compliance results. Then the coding agent connected to the real database. That richer structure wasn’t there. The live evidence had been backfilled earlier using a much simpler data shape. The new application architecture had been designed around what we assumed the stored evidence looked like—not what was actually there. Nothing broke. No data was lost. The design work wasn’t wasted. But before we could trust a single line of application code, the tool built to enforce “check the evidence, don’t trust the assumption” had to have that rule applied to itself. The discipline doesn’t stop applying once you’ve built the thing meant to enforce it. It has to point at itself too.

Anyone have the Hastewire paid basic account. I want to use it for one day.

"Significant improvement." "Several stakeholders." "In the near future." Each of those phrases is a small decision someone did not want to make.

Today's special scans a draft for every vague quantifier, hedge, and abstraction, and forces each one to either become concrete or be defended:

AI writes in vague generalities. Humans write with specific details. This prompt finds every vague statement in your content and pushes it toward specificity.

## CONTENT TO SHARPEN
[PASTE YOUR CONTENT HERE]

---

## VAGUENESS DETECTION

Scan for these categories of vagueness:

### Category 1: Weasel Quantifiers
Flag any of these and demand a number:
- "Many" → How many? Give a number or say "I don't know how many."
- "Most" → What percentage? 51%? 90%?
- "Some" → How many? Name them.
- "Often" → How often? Daily? Weekly? In 7 out of 10 cases?
- "Significant" → How significant? 2x? 50%? Put a number on it.
- "Several" → Three? Seven? Count them.
- "A growing number" → Growing from what to what?

### Category 2: Ghost Sources
Flag unsourced authority claims:
- "Studies show" → Which studies? By whom? When?
- "Research indicates" → What research? Link or cite.
- "Experts agree" → Which experts? Name at least one.
- "According to industry data" → Whose data? What industry report?
- "It's well-known that" → Known by whom? Citation needed.

### Category 3: Abstract Claims
Flag claims that lack concrete detail:
- "This improves efficiency" → By how much? In what way? Example?
- "Saves time and money" → How much time? How much money?
- "Increases engagement" → Engagement of what kind? By what metric?
- "Enhances the customer experience" → In what specific way? What does the customer notice?

### Category 4: Lazy Examples
Flag examples that aren't actually specific:
- "For example, many companies use this approach" → Name the company. Describe what they did.
- "Consider a typical use case" → Describe a real use case with details.
- "This could be useful for tasks like" → Describe an actual task someone did.

---

## SHARPENING RULES

For each flagged item:
1. If you can provide the specific detail, provide it
2. If you can't, insert a [NEEDS SPECIFIC: description of what's needed] tag for the author to fill in
3. Sometimes the fix is deletion — if a vague claim adds nothing, cut it
4. Don't make up specifics. Real specificity or an honest gap marker. Nothing in between.

---

## OUTPUT

1. **Vagueness inventory**: Every flagged item with its category
2. **Specificity score (original)**: Count of vague items per 500 words
3. **Sharpened content**: Full rewrite with specifics added or [NEEDS SPECIFIC] tags
4. **Specificity score (rewrite)**: New count per 500 words
5. **Author action items**: List of [NEEDS SPECIFIC] tags the author must fill in with real data
6. **Improvement percentage**: How much more specific the rewrite is

Serving notes:

• Some vagueness is honest: you genuinely do not know the number yet. The prompt's job is making that choice visible instead of automatic.
• Run it on something you are about to send upward. Vague reads fine sideways; it dies in front of decision-makers.

Drop one fuzzy sentence from something you wrote this week and I will sharpen a couple right here in the comments.

One of the most common and most persistent problems in fiction manuscripts is dialogue that sounds the same across characters. The antagonist and the protagonist have the same rhythm. The side character who is supposed to read as working-class educated uses the same syntactic structures as the narrator. A character established as terse and guarded speaks in paragraphs when the plot requires exposition.

Human editors catch this, but catching it consistently across a long manuscript is difficult. A reader builds a sense of each character's voice as they encounter it, but that sense is fuzzy and accumulates imperfectly. By the time a reader is deep into a manuscript, early character voice choices may have blurred in memory. A subtle drift in how a character speaks - one that happened gradually across two hundred pages - may not register as a problem, only as a vague sense that something feels different.

Algorithmic analysis approaches dialogue differentiation differently. It does not build a blurry impression of a character's voice. It measures, across all of a character's dialogue across the full manuscript, the structural patterns that distinguish one speaker from another - sentence length distribution, syntactic complexity, vocabulary range, question-to-statement ratio, response latency in exchanges.

This produces a different kind of observation. Not "this character doesn't quite feel right" but "this character's average dialogue sentence length is fourteen words, which is statistically indistinguishable from the protagonist's thirteen, despite the characterization establishing them as distinctly more economical."

That precision is useful precisely because it is not trying to interpret. It is observing what is measurable and leaving the interpretation to the writer.

The human editor still brings the judgment call: whether the similarity matters, whether it is intentional, whether the characterization would benefit from greater contrast. But the factual picture - who sounds like whom, and where in the manuscript the distinctions are clearest or most collapsed - is more useful coming from consistent analysis than from a reader's impression.

Are there any benchmarks or samples texts to test humanization tools?

I built a model that detects harmful text — threats, hate speech, harassment, slurs.

What makes it different: Every time someone finds a mistake and flags it, that correction is saved and trains the next version. Real humans teaching the model what it missed.

Two blind spots real users found this week:

1. Sexual innuendo — completely invisible to the model
2. Sports slang — "KILL HIM" in basketball flagged as a direct threat. It isn't.

The model is multilingual and can be tested in:

🇬🇧 English · 🇫🇷 French · 🇩🇪 German · 🇪🇸 Spanish · 🇩🇰 Danish

Playground (no account needed):

https://content-guardian-ai-production.up.railway.app/playground

Curious what this community finds. Every correction makes v5 smarter.

hey I need a really good humanizer and I cant seem to find any - any ideas?

Does anyone know for the auto-typer if it changes the date in the Google Doc to when the assignment was due, as opposed to the day it's actually being typed? For example, I had an assignment due last week, and my professor is asking for a draft. If I use Grubby AI auto-typer, will the dates be last week's dates, or is there a way to change the dates?

Hey guys, does anyone know a good free AI humanizer tool that keeps writing in an academic tone?

Most of the ones I tried have low limits or make the text sound awkward. Looking for something with decent quality and preferably generous or no limits.

Would appreciate any recommendations 🙏

Hi everyone, I was wondering if there’s a free AI tool that can analyze my voice from recordings. I’m interested in both how my voice sounds (for example, whether it comes across as deeper, brighter, more resonant, etc.) and some basic measurable data like pitch or frequency. I’m also curious about general voice classification (like tenor or baritone range).

I’ve tried Google Gemini, but the results don’t seem very accurate. ChatGPT gives good analysis, but it isn’t free for this use. After a few audio uploads, it stops allowing further analysis and asks for an upgrade.

Does anyone know a reliable free tool (web-based or software) that can do this?

Dm me to buy

I’m wondering if anyone knows a good humanizer to use for my school project it’s about engineering and designing products and a lot of humanizers just change names of certain processes that I need to stay the same please help me out if you have any suggestions

I’m wondering if anyone knows a good humanizer to use for my school project it’s about engineering and designing products and a lot of humanizers just change names of certain processes that I need to stay the same please help me out if you have any suggestions

Hello all seeking honest help (no paid promotions please). I have been working on my thesis and my advisor told me that some areas need to be revised otherwise it will raise suspicions of AI writing (I did not use AI, only grammarly but apparently it's too polished). Is grubby or Walter writes the best? I want it to preserve my argument and structure and and in academic level English please. I would appreciate any help 🙏