Anti Ai Copy

Anti-AI Copy

Purpose

Edit writing so it sounds like a real person with a context, taste, and point of view wrote it. This skill is universal: use it for product pages, docs, READMEs, emails, social posts, bios, UI labels, articles, internal notes, and personal writing.

The goal is not to make text quirky. The goal is to remove model-polish: generic claims, soft transitions, smooth sameness, and words that make every topic sound like the same brochure.

Core rules

1. Preserve the writer’s voice. Do not sand the text into a new house style unless asked. 2. Do not synonym-swap banned words. Rewrite the sentence around concrete meaning. 3. Specific beats polished. Prefer nouns, examples, constraints, numbers, names, and lived details. 4. Do not over-humanize. Forced slang, fake jokes, and random sentence fragments are also AI tells now. 5. AI detection is not the goal. Detectors are unreliable. The goal is better writing.

Bad fix:

• “unlock your workflow” → “open up your workflow”

Good fix:

• “unlock your workflow” → “keep the issue, diff, terminal, and review notes in one place”

Scope notes

• The user asks for copywriting, editing, rewriting, wording, tone, tagline, messaging, or voice.
• The user says the text feels “AI”, “SaaS-y”, “generic”, “too polished”, “corporate”, “LinkedIn”, “marketing”, “soulless”, “not human”, “not me”, or “written by ChatGPT”.
• The user asks to make text more natural, dry, direct, blunt, warm, personal, specific, founder-written, less cringe, or less try-hard.
• You are reviewing public or identity-bearing text where trust depends on voice.

Boundaries

• The task is only strict grammar/spelling cleanup and the user did not ask for voice changes.
• The writing is legal, medical, regulatory, safety, or compliance text where convention matters more than voice.
• The user wants academic style, citations, or formal precision rather than naturalness.
• The user explicitly asks for conventional corporate polish.

Inputs expected

Required:

• the text to review, or the file/page containing it
• the intended audience/context, if not obvious

Optional:

• desired voice words, e.g. dry, kind, blunt, careful, playful
• phrases that must stay
• examples of the writer’s existing voice
• where the text will appear, e.g. landing page, email, README, tweet, issue, UI

Workflow

1. Identify the job of the text.

• What should the reader know, feel, or do?
• What relationship does the writer have with the reader?
• What level of polish is appropriate for the venue?

2. Detect the current voice. Preserve useful traits before editing:

• sentence length and rhythm
• formality level
• humour or restraint
• technical density
• warmth, irritation, uncertainty, confidence

3. Scan for AI tells. Use references/trope-checklist.md for a full pass. Look for:

• hype verbs: unlock, unleash, elevate, empower, transform
• abstraction fog: landscape, ecosystem, journey, solution, capabilities
• synthetic transitions: furthermore, additionally, it is important to note
• symmetrical slogans: not just X but Y, more than X, where X meets Y
• vague adjective stacks: seamless, powerful, robust, intuitive, innovative
• cadence sameness: every paragraph has the same smooth shape

4. Replace claims with evidence.

• Name the thing, not the vibe.
• Add one concrete example where the text currently makes a broad claim.
• Keep caveats if they make the text more trustworthy.

5. Break the rhythm deliberately. AI-ish writing is often too even. Vary sentence length. Let a short sentence stand. Cut throat-clearing. Keep one odd phrase if it belongs to the writer.

6. Tune attitude to the context.

• Product/README: concrete, useful, lightly opinionated.
• Email: direct, kind, not over-explained.
• Social post: one point, less setup, more human rhythm.
• Docs: clear first; personality only where it helps.
• Bio/about: specific details over self-description.

7. Return edits in the right format.

• Small text: provide a rewritten version directly.
• Review: use a table with Current, Issue, and Rewrite.
• File edit: edit the file, then summarize what changed.
• If the user asks “what’s wrong”, diagnose first before rewriting.

Validation

Before finalising, check:

• No “unlock”, “unleash”, “seamless”, “revolutionary”, “game-changing”, “in today’s landscape”, or similar phrases unless quoted, mocked, or genuinely required.
• No “not just X, but Y” structure unless it is intentionally preserved.
• No abstract value claim is left unsupported by concrete detail.
• Sentence rhythm is not uniformly smooth.
• The rewrite still sounds like the same writer, unless the user asked for a new voice.
• The text still tells the reader what to do next, when action is needed.
• The attitude does not obscure the message.

Error handling

Error: no text provided

Ask for the text, file, or page to review.

Error: audience/context unclear

Make the smallest useful edit and state the assumption. Ask only if the rewrite would change drastically based on context.

Error: requested voice conflicts with venue

Explain the tradeoff briefly. Example: “I can make this drier, but for a customer apology I’d keep the jokes out.”

Error: a high-risk phrase is actually precise

Keep it. This is not a blind blacklist. Explain only if useful.

Error: user wants “humanized” text to evade detection

Do not frame the work as evasion. Offer to improve clarity, specificity, rhythm, and voice.

Output contract

When reviewing text, return:

• the main issues
• specific phrases to cut or rewrite
• replacement copy
• any remaining risks, e.g. too cute, too vague, too negative, too formal

When editing files, return:

• changed file paths
• a short before/after summary
• validation performed

Examples

Example 1: product fog

User says: “Make this less AI: Unlock seamless collaboration for modern developer teams.”

Expected behaviour: 1. Identify “unlock”, “seamless”, “modern”, and generic audience fog. 2. Ask what the product actually does if needed. 3. Rewrite concretely.

Possible rewrite:

Keep review comments, terminal state, and project sessions in one place. Less tab archaeology.

Example 2: email polish

User says: “Make this email sound less ChatGPT.”

Input:

I hope this email finds you well. I wanted to reach out to discuss the possibility of collaborating on an exciting opportunity.

Possible rewrite:

Hey Sam — I’ve got an idea for a small collaboration. Worth a quick look?

Example 3: social post

User says: “This post feels LinkedIn.”

Bad:

In today’s rapidly evolving AI landscape, adaptability is no longer optional.

Better:

The weird part of AI work is how fast yesterday’s clever trick becomes plumbing.

Example 4: too much sass

User says: “Make this sassy but not annoying.”

Bad:

Your IDE could never. Ship code like a caffeinated raccoon.

Better:

Not an IDE. Not a dashboard. A shell for the parts of agent work that keep getting lost.

Example 5: personal bio

User says: “Make this bio less generic.”

Bad:

Passionate technologist building innovative solutions at the intersection of design and engineering.

Better:

I build small, opinionated tools for people who would rather ship than babysit dashboards.

Supporting files

• references/trope-checklist.md: common AI/corporate copy tells, safer rewrite moves, and source notes.

AI Writing Trope Checklist

Use this for full copy reviews across product pages, docs, READMEs, emails, posts, bios, and essays. Do not treat it as a blind word blacklist. The test is whether the phrase creates generic polish instead of meaning.

Hard-cut phrases

Usually remove or rewrite:

• in today’s fast-paced world
• in today’s rapidly evolving landscape
• in the ever-evolving world of
• it is important to note
• it is worth noting
• in conclusion / in summary, when used as boilerplate
• not just X, but Y
• more than just X
• where X meets Y
• the future of X
• game-changing / revolutionary / transformative
• a testament to
• plays a crucial / pivotal role
• when it comes to
• at the end of the day, unless it fits the writer’s real speech

High-risk verbs

Usually replace the sentence with concrete behaviour:

• unlock
• unleash
• elevate
• empower
• streamline
• optimize, when not technical
• transform
• enhance
• leverage, when “use” works
• delve
• navigate, when used with “complexities”
• harness

High-risk adjectives

Use only if backed by evidence:

• seamless
• robust
• powerful
• intuitive
• innovative
• cutting-edge
• dynamic
• comprehensive
• effortless
• scalable
• meticulous
• pivotal
• crucial
• vibrant

Abstraction fog

These often hide the actual meaning:

• landscape
• ecosystem
• journey
• solution
• experience
• platform
• capabilities
• potential
• productivity
• workflow, if repeated without naming the work
• realm
• tapestry
• intersection, when used as “at the intersection of X and Y”

Email tells

Often cut:

• I hope this email finds you well
• I wanted to reach out
• I am writing to inform you
• please do not hesitate to
• thank you for your understanding
• I would like to take a moment to

Better moves:

• lead with the reason for the email
• ask the actual question
• keep warmth where it is real, not ceremonial

Social-post tells

Often cut:

• “Here’s the thing:” when it only delays the point
• “The real kicker?” when nothing kicks
• numbered lessons that all have the same cadence
• “This changed everything” without a concrete before/after
• faux vulnerability followed by a pitch

Better moves:

• start with the specific observation
• keep one sharp line, not five
• include a concrete detail or caveat

Product/marketing tells

Often cut:

• “built for modern teams”
• “all-in-one solution”
• “supercharge your productivity”
• “move faster than ever”
• “designed to help you”
• “without compromising on quality”

Better moves:

• name the actual parts
• show the tradeoff
• admit what it does not do
• make CTAs plain

AI-ish structure tells

• Balanced paragraphs where every sentence is medium length.
• Three-item adjective stacks: “powerful, flexible, intuitive”.
• Setup-transition-payoff rhythm in every paragraph.
• Over-explaining obvious implications.
• Generic “why it matters” closers.
• One paragraph politely summarizing the previous paragraph.
• Too many em dashes used for sophistication rather than meaning.
• Bullets where every item starts with the same polished verb.

Better replacement moves

Name the product nouns

Instead of:

streamline your development workflow

Use:

keep composer, terminal, git, and sessions together

Admit the edge

Instead of:

seamless cross-platform experience

Use:

Windows installer, Linux AppImage, Mac hopefully

Use dry contrast

Instead of:

not just a coding app, but a new way to build

Use:

Not an IDE. Not a dashboard. A shell for agent work.

Cut the throat-clearing

Instead of:

It is important to note that local dictation runs on CPU.

Use:

Local dictation runs on CPU.

Replace promise with mechanism

Instead of:

empowers developers to collaborate with agents

Use:

gives agents terminal access, git context, and a composer to work from

Keep the writer’s odd phrase

If the source has one slightly weird but clear phrase, preserve it. AI-ish editing often removes the only human part.

Universal house posture

Aim for:

• concrete
• contextual
• voice-preserving
• useful before clever
• comfortable with caveats
• varied rhythm

Avoid:

• VC deck words
• founder guru voice
• launch-thread hype
• fake rebellion
• random slang injected as “humanity”
• flattening every writer into the same dry persona

Source notes

Patterns collected from current research and writing commentary:

• TechRadar reports leakage of words like “delve”, “realm”, “pivotal”, “meticulous”, phrases like “the real kicker”, and structures like “it’s not X, it’s Y”; it recommends simpler wording and preserving personal quirks.
• Tom’s Guide notes vague claims such as “experts agree” without sources, warns against treating em dashes as proof, and recommends adding caveats and pitfalls.
• How-To Geek describes AI writing as too uniform and unlike how people normally communicate.
• Microsoft Copilot’s writing guidance identifies redundant phrasing, neutral tone, and vague language as common AI-text traits, and recommends bringing key points forward and adding real examples.
• Louis Bouchard’s AI-editing guide points to phrases like “delve into”, “underscore”, “ever-evolving landscape”, “seamless, robust solutions”, and stock email openings as common model-polish.
• Tropes.fyi catalogs recurring AI patterns across word choice, sentence structure, tone, formatting, and composition.
• Winston AI and Pangram publish common AI phrase/category lists; use them as heuristics, not proof.
• Reddit writing communities repeatedly flag smooth sameness, “not just X, it’s Y” structures, “in today’s world”, “moreover”, and over-polished vocabulary, while warning that no single word proves AI authorship.