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Write like me

Skill DeepakGanapathi97/write-like-me/skills/write-like-me

Use this whenever the user asks you to write, draft, or reply to something in their own voice — a text to a friend, an email (to a landlord, recruiter, coworker), a cover letter, a speaker bio, a README intro, a LinkedIn/social post, a blog post, an essay, a comment. Trigger on plain requests like "write a bio for my page," "reply to this email," "draft a text canceling plans" even with zero mention of AI or sounding human — any first-person prose going out under the user's name qualifies, whether it's a new draft or a polish of existing text. Also trigger on explicit asks: "write like me," "in my voice," "my writing style," "make this sound human/less robotic/less like ChatGPT," or /write-like-me. The skill strips AI-tell vocabulary and formatting (delve, crucial, "stands as a testament," em-dash chains, "not just X but Y," bold-everything lists) and matches the user's real voice from saved samples when available. Skip for code, terminal output, commit messages, and short factual answers.From its SKILL.md

Install
npx -y skills add DeepakGanapathi97/write-like-me --skill write-like-me

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

6.7 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

write-like-me

The job is one thing: make the words that follow read like a person wrote them, not a model. That happens two ways — avoiding the patterns below by default, and, when there's real material to work from, actually sounding like this person specifically.

Why this list exists

Wikipedia's editors catalogued "Signs of AI Writing" after noticing the same fingerprints showing up across AI-generated encyclopedia edits — not typos or wrong grammar, just words and structures that models reach for by default because they're statistically safe. None of these are incorrect English. The tell is clustering: one "crucial" or one em dash means nothing, several stacked in a paragraph reads as generated. Treat this as a habit to break, not a list of forbidden words.

What to strip out, every time

  • Reach-for-it vocabulary: delve, boasts, crucial, pivotal, intricate/intricacies, interplay, landscape, meticulous, underscore, tapestry, testament, vibrant, garner, bolstered, robust, dynamic, comprehensive, myriad, realm, seamless, leverage, elevate, align with, showcasing, fostering.
  • Copula avoidance: "serves as / stands as / marks / functions as / represents" instead of the plain "is." Plain "is" is fine. Use it.
  • Negative parallelisms: "not just X, but Y," "it's not X, it's Y," "X rather than Y" as a rhetorical crutch instead of a real contrast.
  • Rule-of-three padding: reflexive triplets ("adjective, adjective, and adjective") used to simulate depth rather than because three distinct things actually exist.
  • Promotional / undue-significance filler: "is a testament to," "plays a crucial role," "underscores the importance of," "nestled in the heart of," "leaves an indelible mark," and canned "faces challenges, but the future looks promising"-style wrap-ups.
  • Superficial "-ing" analysis: tacking on "...highlighting the importance of X" or "...underscoring Y" instead of actually asserting something.
  • Formatting tics: em-dash chains, bolding every key term, emoji as bullets, bold-label-then-colon list items where a sentence would read better, tables for things that don't need a table, horizontal rules before headings.
  • Vague attribution: "industry reports suggest," "experts argue," "observers have cited" — with nobody named. If there's no real source, don't fake the shape of one.
  • Fourth-wall breaks: "I hope this helps," "please let me know if you'd like adjustments," disclaimers about being an AI. Stay inside the piece of writing; don't narrate around it.

The full distilled list with more examples per category lives in references/ai-writing-signs.md — read it if a category above isn't specific enough for the case at hand.

Sound like the actual person, when there's material for it

Check for a samples file at ~/.write-like-me/voice-samples.md. That path is deliberately outside any one tool's own config directory, so whichever AI tool is running this skill reads and writes the same file — the voice carries over whether it's Claude Code, Cursor, or anything else.

  • If the file exists, read it and let it inform sentence length, punctuation habits, contractions, typical openers/closers, and vocabulary register for what you're about to write.
  • If it doesn't exist yet, ask once, the first time this skill activates in a session — briefly and low-pressure: "Want to share 3-5 sentences you've written, so I can match your voice? Totally optional, and I'll still write naturally either way." If they give samples, save them verbatim into that file (create ~/.write-like-me/ if needed) so future sessions and other tools pick it up automatically. If they decline or don't respond, don't ask again this session — proceed on the checklist above, which does most of the work on its own.

Samples are a refinement, not a requirement. Never block on them, and never make the absence of samples an excuse for AI-sounding prose.

Don't flatten dialect while de-AI-ifying

The checklist above targets AI padding — vocabulary and structures a model reaches for by default. It is not a mandate to standardize everything into generic American English. If the user's voice samples (or their visible writing style) include Indian English constructions — "revert" for reply, "prepone," "do the needful," "isn't it/no?" tags, "only"/"itself" for emphasis, progressive tense on stative verbs ("I am understanding it now") — keep them. Left unchecked, AI models default toward "standard" English and quietly erase dialect; that's the same flattening instinct this skill exists to fight, just aimed at a different target. See references/indian-english-patterns.md for the fuller picture and why this matters, not just stylistically.

Leave this alone

Code, shell commands and their output, error messages, logs, and anything the user pasted in expecting it back verbatim (direct quotes, exact text to insert elsewhere). This skill is about prose in the user's own words, not about rewriting things that need to stay exact.

Don't narrate it

No "here's a more human version," no "I've matched your voice here" sign-offs, no announcing that this skill is active. Just write the text the way it was asked for.

Example

Before (AI-flavored):

Our Q3 results serve as a testament to the team's dedication. Not only did we exceed targets, but we also fostered deeper client relationships, underscoring our commitment to sustainable growth. Moving forward, we remain committed to delivering value.

After:

We beat our Q3 targets and the client relationships got stronger too — not a coincidence. Next quarter we're keeping the same approach.

What ships with it: 2 files

7.4 KB alongside SKILL.md

Gives 0 of the 12 instructions most hr recruiting skills give in ~1.3k tokens

Counted across 356 of the 357 authors here whose files we hold, read 2026-08-07

  • Quantify achievements with specific metricsin 14 of 356, across 6 files
  • Keep the resume under two pagesin 14 of 356, across 6 files
  • Request the full job description if not providedin 12 of 356, across 4 files
  • Extract keywords and prioritize job requirementsin 12 of 356, across 4 files
  • Stop and ask for clarification if required inputs are missingin 12 of 356, across 5 files
  • Map candidate experience to job requirementsin 11 of 356, across 3 files
  • Ask if the user wants adjustmentsin 11 of 356, across 3 files
  • Provide strengths and gap analysis after the resumein 10 of 356, across 2 files
  • Request candidate background details if not providedin 10 of 356, across 2 files
  • Format experience bullets as action verb plus resultin 10 of 356, across 2 files
  • Ask for missing inputs before startingin 10 of 356, across 9 files
  • Use exact job description terminologyin 9 of 356, across 1 file

Said here and by no other author read

  • strip AI-tell vocabulary and formatting
  • use plain verbs instead of evasive substitutes
  • avoid negative parallelisms as rhetorical crutches
  • remove rule-of-three padding
  • delete promotional or undue-significance filler
  • check for a user voice samples file

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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