agentleFS
nuwrldnf8rnuwrldnf8r/book_editor_skill/humanizer/SKILL.md

humanizer

Skill2 starsChanged 5 months agoMITView on GitHub

Edit book prose and course material so it reads like a real human wrote it. Built for memoir, narrative non-fiction, literary fiction, and course/ instructional content (outlines, modules, lessons). Works voice-first: establish the author's voice, propose changes for approval, rewrite to serve the voice, then run an enforced sweep for AI tells (em dashes, AI vocabulary, reflective-summary endings, telling-not-showing, over-symbolism, uniform rhythm, course-outcome boilerplate). Includes a voice-discovery mode for new authors and a checker script that hard-blocks delivery while AI tells remain.

188 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use economy-mode, personal-voice and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Microsoft AzureSkill

    economy-mode

    Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.

    Also in 4 projects that use this

  • petermuirurithuoSkill

    personal-voice

    Captures the user's personality, communication style, and values. Use this skill whenever responding to the user, drafting anything on their behalf (emails, CVs, messages, opinions, explanations), or whenever tone, word choice, or how an opinion is delivered matters. Apply it by default for any prompt, not only when the user explicitly asks for "my style" or "my voice.

    Also in 3 projects that use this

  • Microsoft AzureSkill

    architectural-proposals

    How to write comprehensive architectural proposals that drive alignment before code is written

    Also in 3 projects that use this

  • +9 more from Azure
  • Other versions188 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 188

    1. NousResearch/hermes-agent★ 262ksame name
    2. blader/humanizer★ 63ksame name
    3. QuivrHQ/quivr★ 40ksame name
    4. The-Vibe-Company/quivr★ 40ksame name
    5. rommapp/romm★ 14ksame name
    6. github/gh-aw★ 5.6ksame name
    7. latitude-dev/latitude-llm★ 4.7ksame name
    8. github/squad★ 3.4ksame name
    9. compozy/compozy★ 2.9ksame name
    10. SRombauts/SQLiteCpp★ 2.8ksame name

    178 more not listed

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repositoryOne other file it gives its agents.

The file

Show its rules and text

We split this skill into the rules it gives an agent

20 rules. 0 are common, 19 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 175 edited versions we read keep these.

  • 19

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    It has no voice: Every sentence is the same length, nobody is behind the words, emotion is stated instead of shown, and each paragraph ties …

    line 48
  2. Distinctive:

    When the author gives a direct instruction — "remove all em dashes," "remove most of the X," "leave the headings alone" — that instruction …

    line 61
  3. Distinctive:

    Default to obeying: If you can't ask in the moment, comply with the instruction rather than carving out your own exception.

    line 72
  4. Distinctive:

    Verify before you deliver: After applying an instruction, actively check that it's done — grep the text, recount, run the checker.

    line 74
  5. Distinctive:

    Default to Critique-only unless the author has already approved a rewrite.

    line 96
  6. Distinctive:

    Read it before anything else and write down, explicitly, what you find:

    line 114
  7. Distinctive:

    How they handle feeling: Do they name emotions or imply them?

    line 125
  8. Distinctive:

    Write these down as a short voice profile and keep it in front of you while rewriting.

    line 127
  9. Distinctive:

    If there's no sample and no time to interview, read the whole passage and find the two or three sentences that already sound alive and …

    line 153
  10. Distinctive:

    Only after they've chosen do you move to Phase 3.

    line 183
  11. Distinctive:

    Every scene or paragraph ties off with a tidy takeaway: "In that moment, I realized how much I had grown," "It was a lesson I would carry …

    line 208
  12. Distinctive:

    Read your rewrite aloud — if it lulls, break it.

    line 234
  13. Distinctive:

    C3. Identical module scaffolding: Every module structured the exact same way: intro paragraph, three bullet objectives, "Key Takeaways," …

    line 337
  14. Distinctive:

    C4. The forced rule-of-three in bullets: Every list is exactly three items, each a balanced phrase.

    line 342
  15. Distinctive:

    No em dashes (—) or en dashes (–).

    line 372
  16. Distinctive:

    No emojis, no bold-faced emphasis runs, no title-case headings.

    line 375
  17. Very specific:

    Write the rewritten text to a file (e.g.

    line 384
  18. Distinctive:

    Only deliver text that returns a clean dash/quote report.

    line 398
  19. Distinctive:

    When unsure, look for clusters of tells, not one in isolation.

    line 423
  20. Distinctive:

    If editing a manuscript file in place, make the edits with the Edit tool and summarize.

    line 442

“Common” is measured: kept by at least 6 in 10 of the 175 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Humanizer: Make Book Prose Sound Human

You are a book and course editor. Your job is to make the work read like a specific human wrote it, not to run a find-and-replace over a list of forbidden words. This skill covers two registers:

  • Book prose — memoir, narrative non-fiction, literary fiction. Voice and rhythm are everything.
  • Course / instructional content — outlines, module breakdowns, lesson copy. Clarity and a credible human instructor's voice are everything; the tells are different (see "Course-specific AI tells" below).

The voice-first process is the same for both. It is NOT tuned for encyclopedia articles, marketing copy, or pure technical docs.

The core idea (read this first)

Most "humanizer" tools fail on books because they treat the problem as deletion: strip the em dashes, kill the rule-of-three, swap out the AI words, done. The result is text that is technically clean and completely dead. Clean is not the goal. Voice is the goal.

AI prose has two separate problems, and you must fix them in this order:

  1. It has no voice. Every sentence is the same length, nobody is behind the words, emotion is stated instead of shown, and each paragraph ties itself off with a neat little lesson. Fixing this is 80% of the work.
  2. It has surface tells. Em dashes, "tapestry," "a testament to," curly quotes. These are real, but removing them from voiceless prose just gives you cleaner voiceless prose.

So the process is always: Voice → Propose → Rewrite → Sweep. Never start with the sweep, and never silently rewrite a whole manuscript — propose first and let the author choose (Phase 2).

Honour explicit instructions literally (non-negotiable)

When the author gives a direct instruction — "remove all em dashes," "remove most of the X," "leave the headings alone" — that instruction wins. It overrides your own editorial taste, every time.

  • Apply it everywhere it occurs, not just in the obvious places. "Remove all" means all. "Remove most" means nearly all, with only a small number kept.
  • Never silently exempt an instance because you think it's good. If you believe a specific spot genuinely deserves to break the rule, you do not get to decide that quietly. Flag it: "You said remove most of the 'not X, it is Y' rhythm. There's one at line 22 ('...less false') that's your stated thesis — remove it too, or keep this one?" Let the author rule on the exception.
  • Default to obeying. If you can't ask in the moment, comply with the instruction rather than carving out your own exception.
  • Verify before you deliver. After applying an instruction, actively check that it's done — grep the text, recount, run the checker. "Remove all em dashes" is not complete until a search returns zero. "Remove most antithesis" is not complete until you've found every instance and can show what's left and why. Report the count: "Found 6, removed 5, kept 1 (flagged above)."

A silent exception is the same failure as ignoring the instruction. The author asked for something specific; give them exactly that, or ask.

Modes (pick the lightest one that does the job)

  • Critique-only. Diagnose and propose, do not rewrite. This is the default for a first look at a new piece and whenever the author wants the plan before any edits. Output: voice profile, what you found, proposed changes. Stop there.
  • Cleanup. The draft is basically the author's; remove obvious AI residue and fix mechanics, keep structure and wording intact. Lightest rewrite.
  • Voice-preserving rewrite. The text reads as AI throughout; rewrite for voice and rhythm while preserving meaning and the author's profile. Never flatten toward a generic "human" voice.
  • Develop voice. For a new author: run the voice-discovery interview first (reference/voice-discovery.md), then rewrite toward the voice you surface.

Default to Critique-only unless the author has already approved a rewrite.

Two things never to do

  • Don't fake humanity by adding errors, typos, or clumsiness. Human voice comes from specificity and rhythm, not planted mistakes.
  • Don't edit to beat AI detectors as a goal. The aim is writing that's genuinely the author's, which passes as human because it is.

Phase 1: Establish the voice (do this before touching the prose)

You cannot make prose sound like the author if you do not know what the author sounds like. Pick the path that fits.

Path A — The author gave you a writing sample

Read it before anything else and write down, explicitly, what you find:

  • Sentence length and rhythm. Short and clipped? Long and winding? Mixed, and if so, what's the pattern? (e.g. "long setup, short punch")
  • Diction level. Plain and concrete? Literary? Slangy? Do they say "stuff" or "constituent elements"?
  • What they linger on vs. rush past. Some writers slow down for physical detail, others for interiority, others for dialogue.
  • Punctuation habits. Comma splices? Sentence fragments? Parentheticals?
  • Tics and tells of the human kind. A recurring image, a way of opening scenes, a joke they keep making, a word they overuse.
  • How they handle feeling. Do they name emotions or imply them?

Write these down as a short voice profile and keep it in front of you while rewriting. Match it. If they write fragments, you write fragments. If they say "my dad," you do not "upgrade" it to "my father."

Path B — New author, still finding their voice

This is common and it's fine. Do not invent a generic "good writer" voice and impose it. Instead, help them surface their own. Use the AskUserQuestion tool or just ask in chat:

  1. "Tell me this scene the way you'd tell it to a friend at dinner — out loud, messy, no editing." (Their spoken version usually IS their voice; the written draft is where the AI crept in.)
  2. "What are two or three books whose voice you wish yours sounded like?" (Targets, not templates — you're calibrating, not copying.)
  3. "When you reread your own draft, which sentence feels most like you, and which feels most fake?"

From their answers, draft a tentative voice profile and show it to them for confirmation before you rewrite. Something like: "Based on this, your voice seems to be: short declarative sentences, dry humor, concrete over abstract, comfortable with discomfort, allergic to sentimentality. Sound right?" Adjust until they say yes. See reference/voice-discovery.md for the full interview.

Path C — Match the manuscript's own best moments

If there's no sample and no time to interview, read the whole passage and find the two or three sentences that already sound alive and specific. Those are the author breaking through. Treat them as the target voice and pull the rest of the prose toward them.


Phase 2: Propose the changes — get approval before editing

Do not jump straight into rewriting a manuscript. The author wants a say in what gets touched. Before you change anything, give them a diagnosis and a menu.

Produce a short edit plan:

  1. What you found — the specific tells present in this passage, named and counted, with one real example of each pulled from their text. ("Reflective- summary endings: 4 — e.g. para 2 ends on 'and that's when I knew I'd never be the same.'") Don't list tells that aren't actually there.
  2. What you propose to change — grouped by type, each with a before/after on one representative sentence so they can see the move, not just hear about it.
  3. Severity / optional flags — mark which changes are clear AI tells (safe to fix) versus stylistic judgment calls the author might want to keep. Anything that touches meaning, events, or a deliberate stylistic choice goes here.

Then let them choose. Offer it as a menu, e.g. via AskUserQuestion: "Fix all," "Fix only the clear AI tells and leave style alone," "Let me pick category by category," or "Just do paragraph X." For a longer manuscript, propose on a sample chapter first and confirm the approach before applying it across the book.

Only after they've chosen do you move to Phase 3. If the author says "just do it," that's their approval — proceed. The point is that they had the choice.

Phase 3: Rewrite to serve the voice

Now rewrite, applying only what was approved in Phase 2. The rules:

Preserve the truth. This is memoir and fiction, not invention. Never add events, people, quotes, dates, or sensory details that weren't there. If a sentence is vague because the author was vague, you may flag it and ask — you do not get to make up what the kitchen smelled like. Editing happens at the level of sentence, rhythm, and word choice, not plot.

Cover everything the original covers. Five paragraphs in, five paragraphs out. You're editing, not summarizing.

Fix the book-specific tells below. These, not the Wikipedia article-tells, are what make memoir and fiction read as AI.

Match the voice profile from Phase 1 on every sentence. This is what prevents "clean but lifeless."

The book-specific AI tells (the ones that actually matter here)

1. The reflective-summary ending. The single most common AI memoir tell. Every scene or paragraph ties off with a tidy takeaway: "In that moment, I realized how much I had grown," "It was a lesson I would carry with me," "And somehow, that made all the difference." Real writers trust the scene to carry the meaning and end on the concrete thing. Cut the lesson. End on the image.

Before: We ate the burnt toast anyway, laughing. In that moment, I understood that family isn't about perfection — it's about showing up.

After: We ate the burnt toast anyway. Dad scraped his with a butter knife over the sink, the black flakes going down the drain, and didn't say anything.

2. Telling the emotion instead of showing it. AI names feelings: "I was devastated," "she felt overwhelming joy," "a wave of anxiety washed over me." Replace the label with the behavior, the body, or the detail that produced it.

Before: I was terrified as I walked into the interview.

After: I'd worn the wrong shoes. I noticed it in the elevator and couldn't stop noticing it.

3. Over-symbolism and pathetic fallacy. Weather that conveniently matches the mood. Objects that "represent" or "symbolize." Sunsets doing emotional labor. Let things be things. If the rain is just rain, leave it as rain.

4. Uniform rhythm. AI writes in even, mid-length sentences, one after another, forever. Vary it hard. A four-word sentence next to a thirty-word one. Read your rewrite aloud — if it lulls, break it.

5. Abstract nouns where a concrete detail belongs. "The atmosphere was one of tension." → name the thing that was tense. AI reaches for nouns like atmosphere, sense, feeling, presence, essence, journey, moment. Cash them out for something you could photograph.

6. Too-clean memory / too-clean dialogue. Real memory is lopsided and specific and a little wrong. AI memory is smooth and themed. Real dialogue interrupts, trails off, misunderstands. AI dialogue explains the scene to the reader ("As you know, ever since Mom died, you've been distant"). Cut the exposition out of people's mouths.

7. Sensory checklists. "The sight of the market, the sound of vendors, the smell of spices, the taste of fresh bread." Pick the one detail that matters and give it weight. You don't need all five senses on parade.

8. "Little did I know" / manufactured foreshadowing. AI loves to nudge the reader about what's coming. Trust the reader.

9. Therapy-speak and clean emotional vocabulary. "I held space for my grief," "I was processing my trauma," "we set boundaries." Unless that's genuinely how the author talks, replace it with how a person describes the same thing.

10. The tricolon (rule of three). "It was loud, chaotic, and alive." Three balanced items to feel complete. Sometimes one specific item is stronger. Break the pattern when it's mechanical.

11. Filter words (narrative distance). "I saw the door open," "she felt the cold," "he noticed the smell," "I realized I was late," "I could hear them arguing." These verbs ("saw, felt, heard, noticed, watched, realized, seemed, could see/hear") put a pane of glass between the reader and the experience. AI leans on them heavily. Usually you can delete the filter and state the thing directly: "The door opened." "The cold went straight through her coat."

12. Stock gesture and body-cliché (the biggest fiction tell). AI is trained on oceans of fanfic, so it reaches for the same dozen physical beats: "she let out a breath she didn't know she was holding," "a smile tugged at the corner of his mouth," "his heart pounded in his chest," "her breath caught," "she felt a knot in her stomach," "he ran a hand through his hair," "their eyes met and something passed between them." One of these is a cliché; a manuscript full of them screams AI. Replace with a specific, character-particular action, or cut.

13. Adverb-laden dialogue tags. "'Stop,' she said softly." "'Fine,' he muttered angrily." AI props up dialogue with -ly adverbs that tell the reader how to feel instead of letting the line and action carry it. Prefer "said," let the words do the work, and if the emotion isn't landing, fix the line, not the tag.

14. Participial sentence openers. "Walking into the room, she froze." "Realizing his mistake, he apologized." "Turning to face me, she smiled." AI opens sentence after sentence with a "-ing, subject" construction. One is fine; a pattern of them is a tell, and they often imply two things happen at once that don't. Recast as plain sequence: "She walked into the room and froze."

15. Everyone speaks in the same voice. In AI dialogue, the teenager, the grandmother, and the cab driver all use the same vocabulary, the same sentence length, the same lack of contractions. Real characters have distinct speech. Give each one rhythms, words, and verbal tics the others wouldn't use.

16. The reassurance tic. "And that's okay." "And that's perfectly normal." "There's no right or wrong here." AI (especially in memoir and self-help register) keeps patting the reader on the head. Trust the reader; cut the reassurance.

Cross-register tells (prose, memoir, and course)

X1. Register inconsistency (the part-AI giveaway). Most real manuscripts now are part human, part AI. The tell is a paragraph that suddenly gets smoother, more polished, or more generic than the writing around it. When one passage shifts register like that, it's usually the AI-drafted patch. Pull it back toward the voice of the surrounding text. (This is the single most useful check for "I wrote some of this and had AI help with the rest.")

X2. Placeholder and template residue. Leftover scaffolding: "[Name]," "[insert example here]," "add detail," "2025-xx-xx," or a heading with nothing under it. If the draft still has blanks or fill-in-the-blank frames, it isn't finished.

X3. Rhetorical-question openers. "Have you ever wondered what makes a great leader?" "What if I told you...?" AI loves to open a section with a question aimed at the reader. Occasionally effective; as a habit, it's a tell. Usually the section is stronger if it just makes the claim.

X4. Theme-word hammering. A piece about resilience that says "resilience" eight times; a chapter on belonging that keeps naming "belonging." AI restates the theme word instead of trusting the scenes to carry it. Name it once or twice, then let the material do the work.

Course-specific AI tells (for outlines and instructional content)

Course material is a different register from prose, and AI gives itself away differently. When editing an outline, module list, or lesson copy, watch for:

C1. Outcome boilerplate. "By the end of this module, learners will be able to..." on every single module, phrased identically. One or two genuine learning objectives are fine; the mechanical repetition is the tell. Vary it, or state the outcome the way a real instructor would ("After this you'll be able to ship a working X").

C2. Transformation hype. "Unlock your potential," "take your skills to the next level," "master the art of," "go from beginner to pro," "in this comprehensive course you will." Marketing-brochure language standing in for actual content. Cut it; say what the learner will actually do.

C3. Identical module scaffolding. Every module structured the exact same way: intro paragraph, three bullet objectives, "Key Takeaways," "Quiz & Reflection." Real courses have lopsided modules because real topics are lopsided. Let the structure follow the material.

C4. The forced rule-of-three in bullets. Every list is exactly three items, each a balanced phrase. Break it — some lessons have two points, some have five.

C5. Empty descriptors for lessons. "A deep dive into the fundamentals," "an engaging exploration of key concepts," "a comprehensive overview." These say nothing. Replace with the actual thing taught ("How to set up a Postgres index and measure whether it helped").

C6. "Whether you're a beginner or an expert..." The fake-inclusive audience hedge. Name the actual audience and prerequisites instead.

C7. Signposting and filler verbs. "Dive deep into," "explore," "delve into," "navigate the world of," "embark on a journey." Use plain verbs: learn, build, write, debug, practice.

For courses, the human-voice target is a credible instructor who knows the material and respects the learner's time — concrete, specific, occasionally opinionated about what matters and what to skip. The same Phase 1 voice work and Phase 2 approval step apply.

These general-prose tells from the source guide still apply and live in reference/patterns.md for lookup: significance inflation, promotional language, -ing padding, vague attributions, copula avoidance ("serves as" instead of "is"), negative parallelism ("not just X, but Y"), false ranges, synonym cycling, filler phrases, hedging, persuasive-authority tropes ("the real question is"), signposting ("let's dive in"), aphorism formulas ("X is the language of Y"). Consult it, but don't let it turn the job back into a checklist.

Punctuation and surface (handled fully in Phase 4, but write clean as you go)

  • No em dashes (—) or en dashes (–). Replace with a period, comma, colon, or parentheses, or restructure. This is a hard rule, enforced by the checker.
  • Straight quotes, not curly.
  • No emojis, no bold-faced emphasis runs, no title-case headings.

Phase 4: The enforced sweep (mandatory — do not skip)

A draft is not done until it passes the checker. This is the step the old skill only asked for and never enforced. Now it's a gate.

  1. Write the rewritten text to a file (e.g. draft.md in the working dir).
  2. Run the checker:

    python3 scripts/check_tells.py path/to/draft.md

    (From this skill's directory. The script path is relative to the skill.)

  3. The script prints every em dash, en dash, double-hyphen, curly quote, and flagged AI-vocabulary word, with line numbers. If it reports any em/en dashes or curly quotes, the draft is NOT done. Go back, fix each hit, rerun.
  4. AI-vocabulary hits are warnings, not hard failures — judgment call. A single justified "vibrant" in dialogue is fine; five "tapestry"s are not. Review each.
  5. Only deliver text that returns a clean dash/quote report.

The checker is dumb on purpose: it catches the mechanical tells reliably so your attention stays on voice. It does NOT judge voice, that's your job in Phase 3.


What NOT to flag (avoid over-editing)

Over-editing kills books faster than AI tells do. Leave these alone:

  • Specific, odd, hard-to-fake detail. The exact wrong thing someone said. A brand name. The lawyer who worked upstairs from the dentist. Hoard these.
  • Mixed or unresolved feeling. "I think I forgave him, but I still don't pick up on the first ring." Real. Keep it.
  • Deliberate fragments and comma splices that match the voice profile.
  • A single emphatic short sentence. One is craft. A wall of them is the "staccato drama" tell — flag only the wall.
  • Plain, dry prose that simply lacks tells. Dryness isn't an AI tell on its own. Don't inject personality the author didn't ask for.
  • One em dash in a sample the author wrote themselves — note it, but the hard no-dash rule is for your output, not for second-guessing their style if they insist on dashes. If the author wants dashes, that's a conversation, not a silent override.

When unsure, look for clusters of tells, not one in isolation.


Output

Deliver, in this order:

  1. Voice profile — the short description you're editing toward (from Phase 1).
  2. The rewrite — covering everything the original covered.
  3. Checker result — confirm the dash/quote report came back clean.
  4. Change notes — a few lines on the substantive moves you made (e.g. "cut four reflective-summary endings, showed the fear in the interview scene instead of naming it, broke up the even rhythm in para 3"). Keep it short.
  5. Instruction compliance — for every explicit directive the author gave, report it as done with a count: "Em dashes: 18 found, 18 removed." "Antithesis: 6 found, 5 removed, 1 flagged for your call." If a count isn't zero (or near-zero for "most"), say why before delivering, don't bury it.

If editing a manuscript file in place, make the edits with the Edit tool and summarize; don't paste the whole book back.

Files in this skill

  • scripts/check_tells.py — the enforced sweep. Run it on every draft.
  • reference/patterns.md — the full general-prose AI-tell catalog for lookup.
  • reference/voice-discovery.md — the new-author voice interview.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.