One skill, two jobs. Part 1 defines a voice and how to write in it. Part 2 is how to edit any text so it does not read as machine-written. When writing in a voice, draft with Part 1, then run Part 2 as the edit pass and gate. When editing someone else’s text, use Part 2 alone, with a voice sample if one is supplied.

Part 2 combines the structural patterns from Wikipedia’s “Signs of AI writing” guide as packaged in blader/humanizer (MIT), the rules and scoring rubric from stop-slop by Hardik Pandya (MIT), and a set of plain-speech, documentation and tone rules. Credit to those projects; see Sources and licences at the end.

Make it yours: the voice profile

Part 1 ships with a default profile (below). To use your own voice, replace the “Voice profile” section with your traits and add 3 to 5 samples of your real writing under “Samples”. A supplied sample always wins over every rule in this file.


Part 1: Writing in a voice

Voice profile (default, edit freely)

A builder-and-operator voice. Concrete, curious, a narrator who is actually present.

Modes

Pick the register by format.

Mode A: Ideas essay (default for blog posts and long-form)

Enthusiast energy, conceptual scaffolding, technical vocabulary used casually, footnotes as humour. The piece needs a frame: a lens that makes the mundane interesting. Not “here are my thoughts on distribution” but “what if cracking a growth channel is less like building a product and more like finding a biological niche?”

Mode B: Reportage and cultural criticism (people, scenes, industries)

Mode C: Short form (emails, LinkedIn, social, cover letters)

Mode D: Company and site copy (a letter or page signed by an organisation)

Here the “we” is the organisation, not the author, so Mode A and B flourishes do not fit.

Voice DNA

Writing rules (all modes)

Formatting rules

Reference pieces (internalise the moves, do not copy)

Samples of the target register

These show rhythm and plainness. Replace them with your own.

On a lesson learned:

This is one of those lessons I have known about in the abstract, but recently gained a lot more definition on. […] what really happened was that we cracked a scalable distribution channel. We found channel-model fit.

On a product observation:

Unlike most voice agents, it doesn’t just display a pulsing orb, but actually shows bold white text in point form that summarises the reply. This seemingly simple touch, to me, demonstrates a deep understanding of the user context and psychology.

On a hiring mistake:

Almost immediately, problems cropped up. […] Eventually, they became increasingly disconnected from the company they were so excited to join in the first place, disillusioned that they were unable to make many meaningful contributions.

Format guidance

Self-check for Part 1

  1. Which mode? Did I pick the right register (A, B, C or D)?
  2. Any banned phrase or the “isn’t X, it’s Y” pattern (Part 2, A and I)?
  3. Dashes: Mode C and D have none. Mode A and B only genuine grammatical ones.
  4. Any slogan opener, smug kicker or cute label (Part 2, I)?
  5. Any vague verb where a physical one would work?
  6. Mode A and B: is there a frame, and does the piece arrive somewhere?
  7. Mode B: specific objects and a present narrator?
  8. Mode D: is every claim sourced and every human detail real?
  9. Does a number stand without an actual or estimate label, or mix bases?
  10. Then run Part 2.

Part 2: The edit pass

What a pattern pass cannot fix

Removing tells is half the job. Text can pass every check below and still read as machine-written, because the real tell is no specifics and no point of view: no named person, place, number, object or decision that only this writer could know. Never invent one. When a paragraph has none, mark it [NEEDS REAL DETAIL: what kind of detail] and ask the user. A short true detail beats three polished generalities.

Inputs

Process

  1. Mark. Read the whole text once. List every tell found, strongest first. Strong tells (staging, deep-sounding lines, forced triads, dramatic closers, inflated claims) justify an edit on one sighting. Weak tells (stacked hedges, hyphenated pairs, passive voice, curly quotes) need other tells nearby.
  2. Fix the facts first. Keep every supported claim. Do not add a fact, name, number, date, quote or citation that is not in the source or from the user. A lost claim is an error unless a pattern calls for cutting it. An unsupported addition is an error.
  3. Rewrite around the point. For each paragraph ask: what does this tell the reader to know or do? Write that. Use the plain word, a human subject doing something, and a physical verb. Do not patch flagged phrases one by one. Vary sentence length. If you cannot state the point concretely, cut the paragraph or mark it [NEEDS REAL DETAIL].
  4. Audit. Read it aloud. Ask “what still sounds AI-written?” Then search again for: contrasts, one-line closers, triads, repeated sentence openings, dashes, bold labels, quotable lines, headings that restate the next line.
  5. Score. Rate 1 to 10 on each: Directness (statements, not announcements), Rhythm (varied, not metronomic), Trust (respects the reader), Authenticity (sounds human), Density (nothing cuttable). Below 35 out of 50: revise and re-score. Stop after 2 loops and tell the user what is still weak and why, usually missing specifics.
  6. Deliver. Pasted text: the draft, remaining patterns, the final rewrite, the score. File mode: write only the final text, leave code, paths, commands, data and link targets unchanged, then a short summary. Embedded mode: the final text only.

Patterns

A. Staging instead of stating (strong)

B. Rhythm by rule

C. Inflation and borrowed authority

D. Vocabulary

E. Plain speech

F. Formatting

G. Artifacts of the chat or the draft

H. Wrong reader

I. Tone

Technical and docs register

For READMEs, PR descriptions, runbooks and code comments, the tells are different: feeling-words instead of mechanisms, and compression instead of padding.

Before and after

TellBeforeAfter
Not X but Y“This isn’t a pricing story. It’s a retention story.”“Retention rose while prices stayed flat.”
Forced triad“faster, simpler, and more reliable”“faster and more reliable” (if simpler is not true)
Quotable closer“Those 10 years are why this data exists.”End the paragraph on what the 10 years taught.
Staged candor“Where we could be wrong”“Limits”
Inflated“a pivotal moment for the industry”the number and the date
Vague source“Experts say signups are shifting.”“The Q2 report counted 127 signups, down from 153.”
-ing rider“…ensuring a seamless experience”delete, or say what happens
Fancy is“The platform serves as a hub”“The platform is a hub”
Slogan opener“Q3 was about widening the base.”“In Q3 we focused on growing the customer base.”

Sources and licences