---
name: Avoid AI Writing
slug: avoid-ai-writing-3
category: Writing
description: Avoid AI Writing audits and rewrites content to remove AI writing patterns (AI-isms) such as em-dash overuse, bold overuse, and excessive bullet lists. Use it when asked to remove AI-isms, clean up AI writing, or make text sound less like AI.
github: "https://github.com/pchalasani/claude-code-tools/tree/main/plugins/writing/skills/remove-ai-patterns/upstream"
language: Python
stars: 1979
forks: 128
install: "npx degit https://github.com/pchalasani/claude-code-tools/tree/main/plugins/writing/skills/remove-ai-patterns/upstream ~/.claude/skills/upstream"
installs_to: ~/.claude/skills/upstream
source_path: plugins/writing/skills/remove-ai-patterns/upstream/SKILL.md
collection_size: 17
category_size: 1012
collection_url: "https://dirskills.com/collections/pchalasani/claude-code-tools"
added: 2026-08-18T06:58:53.688Z
last_synced: 2026-08-18T06:58:53.688Z
canonical_url: "https://dirskills.com/skills/avoid-ai-writing-3"
---

# Avoid AI Writing

Avoid AI Writing audits and rewrites content to remove AI writing patterns (AI-isms) such as em-dash overuse, bold overuse, and excessive bullet lists. Use it when asked to remove AI-isms, clean up AI writing, or make text sound less like AI.

**Install:**

```bash
npx degit https://github.com/pchalasani/claude-code-tools/tree/main/plugins/writing/skills/remove-ai-patterns/upstream ~/.claude/skills/upstream
```

## README

# Avoid AI Writing — Audit & Rewrite

You are editing content to remove AI writing patterns ("AI-isms") that make text sound machine-generated.

## What this skill is and isn't

This is a **writing-quality tool**, not a verdict. The patterns flagged here are statistically more common in LLM output, but humans on autopilot — especially writing under deadline pressure, in unfamiliar genres, or in a second language — produce the same shapes. Independent audits of commercial AI detectors have found false-positive rates above 60% on non-native English writers (Liang et al., Stanford, *Patterns* 2023) and overall misclassification rates above 70% on open-source detectors (Jabarian & Imas, BFI Working Paper 2025-116, 2025). Adversarial paraphrase reduces detection accuracy by ~88% across every method tested (arXiv:2506.07001, 2025).

The patterns are useful as a signal — both for cleaning up your own writing and for assessing whether a piece reads as AI-generated. Just don't make them the sole basis for a consequential decision (academic integrity, hiring, publication, attribution). Several rules here also fire on second-language writing, deadline-pressed humans, and technical genres that compress vocabulary by design. Pair the signal with context: who wrote it, what genre, what the writer's normal voice looks like, what other evidence you have.

In short: signals, not proof. Worth acting on; not worth ruining someone's day over.

## Modes

This skill operates in one of three modes:

**`rewrite`** (default) — Flag AI-isms and rewrite the text to fix them.

**`detect`** — Flag AI-isms only. No rewriting. Use this mode when:
- The writer wants to see what's flagged and decide what to fix themselves
- The flagged patterns might be intentional (AI patterns aren't always bad — they can be effective in small doses)
- You're auditing text you don't want altered (published content, someone else's writing, reference material)
- You want a quick scan without waiting for a full rewrite

**`edit`** — Edit a file in place rather than returning rewritten text. Use this when the writer points you at a file ("clean up `draft.md`", "fix the AI-isms in this file directly") and wants the file changed, not a copy to paste back. Make **minimal, targeted edits** with the Edit tool — change the flagged spans, not the whole document. **Preserve passages that are already human**: if a paragraph has no tells, leave it untouched. **Don't edit quoted material, code blocks, or text attributed to someone else** — flag those instead of rewriting them. For a large file, confirm which section to clean before changing anything. After editing, re-read the file and confirm the flagged patterns are resolved.

Trigger detect mode when the user says "detect," "flag only," "audit only," "just flag," "scan," "what AI patterns are in this," or similar. Trigger edit mode when the user names a file and asks you to fix or clean it in place. Default to rewrite mode if not specified.

**Invocation.** Natural language is enough ("rewrite this in a blunt voice for LinkedIn," "edit `post.md` in place," "scan this, don't rewrite"). Power users can also pass explicit options, which map to the sections below: `[--mode rewrite|detect|edit]`, `[--voice casual|professional|technical|warm|blunt]`, `[--context linkedin|blog|technical-blog|investor-email|docs|casual]`, `[--file PATH]`, `[--iterate N]` (max 2).

**Iterate to convergence (optional).** Rewrite mode already runs one corrective second pass (see Output format) — that built-in pass *is* pass 2, so `--iterate` does not stack on top of it. When the writer asks to "iterate," "keep going until it's clean," or passes `--iterate N`, repeat the audit→rewrite cycle until no patterns remain or **N passes** are reached. Cap **N at 2**: a rewrite plus one corrective pass clears the flagged patterns, and a third pass costs a full regeneration while rarely finding more. Report how many passes it took ("converged in 2 passes").

---

In **rewrite** mode, your job is to:

1. **Audit it**: identify every AI-ism present, citing the specific text
2. **Rewrite it**: return a clean version with all AI-isms removed
3. **Show a diff summary**: briefly list what you changed and why

In **detect** mode, your job is to:

1. **Audit it**: identify every AI-ism present, citing the specific text
2. **Assess it**: note which flags are clear problems vs. patterns that may be intentional or effective in context

In **edit** mode, your job is to:

1. **Read** the file the writer named
2. **Edit in place**: apply minimal, targeted fixes to the flagged spans with the Edit tool, leaving already-human passages untouched
3. **Verify**: re-read the file and confirm the flagged patterns are resolved; report what you changed

---

## What to remove or fix

### Formatting
- **Em dashes (— and --)**: Replace with commas, periods, parentheses, or rewrite as two sentences. Target: zero. Hard max: one per 1,000 words. This applies to headings and section titles too, not just body prose. Catch both the Unicode em dash (—) and the double-hyphen substitute (--). Carve-out: an em dash acting as the separator in a bulleted or numbered list item that opens with a bolded lead term or a markdown link (`- **Term** — description`, `- [label](url) — description`) is typography, not a prose splice — don't count it toward the rate. Only the list-item form qualifies: a mid-sentence splice still counts, as does a line-initial `**Bold lead** — full sentence` outside a list (itself an AI tell), and the double-hyphen substitute is never carved out.
- **Bold overuse**: Strip bold from most phrases. One bolded phrase per major section at most, or none. If something's important enough to bold, restructure the sentence to lead with it instead.
- **Emoji in headers**: Remove entirely. No `## 🚀 What This Means`. Exception: social posts may use one or two emoji sparingly — at the end of a line, never mid-sentence.
- **Excessive bullet lists**: Convert bullet-heavy sections into prose paragraphs. Bullets only for genuinely list-like content (feature comparisons, step-by-step instructions, API parameters).
- **Curly quotation marks (“ ” ‘ ’) and apostrophes**: Curly quotes and apostrophes (U+201C/U+201D, U+2018/U+2019) are a *weak* paste-from-chat signal — meaningful mainly in plain-text contexts like code comments, commit messages, or plaintext drafts, where nothing auto-curls. Treat as corroborating, never conclusive: Word, Google Docs, macOS, and iOS curl quotes by default, so most human prose contains them too. Don't flag curly apostrophes (U+2019) on their own. Replace with straight quotes in plain-text/code; leave them in finished publications and locale-correct punctuation (French « », German „ “).
- **Immaculate typography in casual registers**: Same tier as curly quotes — a *weak*, register-scoped signal, never conclusive alone. Perfect spacing, punctuation, and capitalization in a context where humans type fast (issue/PR comments, chat, DMs) is corroborating evidence, not proof: a careful human can type a flawless comment, and a rushed one can type a sloppy one. Judge it alongside other signals. Inverse case worth flagging the other direction: when editing a human's casual text (a Slack message, a quick reply), preserve their typos, contractions, and idiosyncratic capitalization rather than correcting them — smoothing away the rough edges erases the fingerprint that marks the text as theirs.

### Sentence structure
- **"It's not X — it's Y" / "This isn't about X, it's about Y"**: Rewrite as a direct positive statement. Max one per piece, and only if it serves the argument. This includes the **split-sentence form**, where the negation and the correction fall in two separate sentences rather than pivoting on a single dash or comma: "The headline isn't the speed. The real story is Y." Read on its own, each sentence looks like an innocent declarative, which is exactly why the split version slips past a check tuned to the joined phrasing — flag it the same way. AI also stacks the negation across several options before the reveal ("It's not the price. It's not the features. It's the trust."). The multi-negation countdown is the same move inflated; flag it and cut straight to the positive claim. The **tailing negation** is the clipped cousin: a bare negation fragment tacked onto the end of a sentence — "The options come from the selected item, no guessing." Write the constraint as a real clause ("without forcing the user to guess") or cut it. Carve-out: negations enumerating spec constraints in a list ("no dependencies, no telemetry") are list content, not a reveal. Adapted from `blader/humanizer` P9.
- **Hollow intensifiers**: Cut `genuine` / `genuinely`, `real` (as in "a real improvement"), `truly`, `quite frankly`, `to be honest`, `let's be clear`, `it's worth noting that`. Just state the fact.
- **Vague endorsement ("worth [verb]ing")**: Cut or replace `worth reading`, `worth paying attention to`, `worth a look`, `worth exploring`, `worth checking out`, `worth your time`. These substitute a generic thumbs-up for a specific reason. Say *why* something matters instead.
- **Hedging**: Cut `perhaps`, `could potentially`, `it's important to note that`, `to be clear`. Make the point directly.
- **Missing bridge sentences**: Each paragraph should connect to the last. If paragraphs could be rearranged without the reader noticing, add connective tissue.
- **Compulsive rule of three**: Vary groupings. Use two items, four items, or a full sentence instead of triads. Max one "adjective, adjective, and adjective" pattern per piece.

### Words and phrases to replace

Words are organized into three tiers based on how reliably they signal AI-generated text. This tiered approach — adapted from [brandonwise/humanizer](https://github.com/brandonwise/humanizer)'s vocabulary research — reduces false positives on words that are fine in isolation but suspicious in clusters.

- **Tier 1 — Always flag.** These words appear 5–20x more often in AI text than human text. Replace on sight.
- **Tier 2 — Flag in clusters.** Individually fine, but two or more in the same paragraph is a strong AI signal. Flag when they appear together.
- **Tier 3 — Flag by density.** Common words that AI simply overuses. Only flag when they make up a noticeable fraction of the text (roughly 3%+ of total words).

**Match inflected forms.** Each entry below covers the listed word *and its morphological variants* — adverb (`-ly`), gerund/participle (`-ing`), plural, comparative/superlative, and verb conjugations — unless a variant carries a distinct, legitimate meaning. So `genuine` also flags `genuinely`, `leverage` also flags `leveraging` / `leveraged`, `delve` covers `delving`, and `meticulous` covers `meticulously`. When a variant has a separate honest sense (e.g. `real` meaning factual, not the intensifier in "a real improvement"), judge by context rather than matching blindly.

#### Tier 1 — Always replace

Tier 1 splits into two bands. **Both are always replaced**; the edit is the same. What differs is what a flag *means*.

**1A — AI frequency markers.** Words claimed to appear far more often in machine text than in human writing. A cluster of these is evidence about how a passage was produced.

**1B — Clarity edits.** Wordiness and inflated formality. Replacing them is good writing regardless of who wrote the sentence, and a 1B hit is **not** evidence of machine authorship. Measured against 257 paragraphs of verified pre-2023 human prose, 1B entries fire on ordinary professional and formal writing at a meaningful rate — `in order to`, `utilize`, `commence`, `ascertain`, and `endeavor` are simply the words some people reach for. The detector emits these as `tier1-clarity`, weights them like Tier 2, and excludes them from the dense-AI-vocabulary signal so a wordiness fix can never push a document toward an AI classification.

In `detect` mode, report the two bands separately. Presenting a wordiness fix as authorship evidence is the error this split exists to prevent.

Caveat worth keeping visible: the "appears far more often in AI text" claim behind 1A is **inherited, not measured here**. It traces to [brandonwise/humanizer](https://github.com/brandonwise/humanizer), which states a 5–20x ratio without publishing a method or dataset. Treat 1A as a well-supported convention rather than a verified statistic until this repo measures the ratios itself against a machine-written corpus.

##### Tier 1A — AI frequency markers

| Replace | With |
|---|---|
| delve / delve into | explore, dig into, look at |
| landscape (metaphor) | field, space, industry, world |
| tapestry | (describe the actual complexity) |
| realm | area, field, domain |
| paradigm | model, approach, framework |
| embark | start, begin |
| beacon | (rewrite entirely) |
| testament to | shows, proves, demonstrates |
| robust | strong, reliable, solid |
| comprehensive | thorough, complete, full |
| cutting-edge | latest, newest, advanced |
| leverage (verb) | use |
| pivotal | important, key, critical |
| underscores | highlights, shows |
| meticulous / meticulously | careful, detailed, precise |
| seamless / seamlessly | smooth, easy, without friction |
| game-changer / game-changing | describe what specifically changed and why it matters |
| hit differently / hits different | (say what specifically changed, or cut) |
| watershed moment | turning point, shift (or describe what changed) |
| marking a pivotal moment | (state what happened) |
| the future looks bright | (cut — say something specific or nothing) |
| only time will tell | (cut — say something specific or nothing) |
| nestled | is located, sits, is in |
| vibrant | (describe what makes it active, or cut) |
| thriving | growing, active (or cite a number) |
| despite challenges… continues to thrive | (name the challenge and the response, or cut) |
| showcasing | showing, demonstrating (or cut the clause) |
| deep dive / dive into | look at, examine, explore |
| unpack / unpacking | explain, break down, walk through |
| bustling | busy, active (or cite what makes it busy) |
| intricate / intricacies | complex, detailed (or name the specific complexity) |
| complexities | (name the actual complexities, or use "problems" / "details") |
| ever-evolving | changing, growing (or describe how) |
| enduring | lasting, long-running (or cite how long) |
| daunting | hard, difficult, challenging |
| holistic / holistically | complete, full, whole (or describe what's included) |
| actionable | practical, useful, concrete |
| impactful | effective, significant (or describe the impact) |
| learnings | lessons, findings, takeaways |
| thought leader / thought leadership | expert, authority (or describe their actual contribution) |
| best practices | what works, proven methods, standard approach |
| at its core | (cut — just state the thing) |
| synergy / synergies | (describe the actual combined effect) |
| interplay | relationship, connection, interaction |
| keen (as intensifier) | interested, eager, enthusiastic (or cut — just state the interest) |
| genuinely / genuine (as intensifier) | (cut — just state the fact) |
| symphony (metaphor) | (describe the actual coordination or combination) |
| embrace (metaphor) | adopt, accept, use, switch to |
| load-bearing *(metaphor)* | essential, critical, necessary — or say what breaks if you remove it |

**Hyphen required:** unhyphenated "load bearing" is ordinary English ("the load bearing down on the bridge") — only the hyphenated compound is the tell.

**Construction carve-out:** `load-bearing` before a literal structural noun (`wall`, `beam`, `column`, `joist`, `truss`, `member`, `footing`, `slab`, `stud`, `partition`, `masonry`, `lintel`, `pier`, `rafter`, `girder`, `capacity`), optionally with one material or position adjective in between (`load-bearing structural wall`), is standard building terminology — don't flag. Abstract-capable nouns (`structure`, `element`, `frame`, `foundation`) are excluded on purpose, so "the load-bearing structure of his argument" still flags. Known gap: predicative use ("the wall is load-bearing") still flags — see issue #56.

##### Tier 1B — Clarity edits

Wordiness and formality, not authorship evidence. Same fix, weaker claim.

| Replace | With |
|---|---|
| utilize | use |
| in order to | to |
| due to the fact that | because |
| serves as | is |
| features (verb) | has, includes |
| boasts | has |
| presents (inflated) | is, shows, gives |
| commence | start, begin |
| ascertain | find out, determine, learn |
| endeavor | effort, attempt, try |

#### Tier 2 — Flag when 2+ appear in the same paragraph

These words are legitimate on their own. When two or more show up together, the paragraph likely needs a rewrite.

| Replace | With |
|---|---|
| harness | use, take advantage of |
| navigate / navigating | work through, handle, deal with |
| foster | encourage, support, build |
| elevate | improve, raise, strengthen |
| unleash | release, enable, unlock |
| streamline | simplify, speed up |
| empower | enable, let, allow |
| bolster | support, strengthen, back up |
| spearhead | lead, drive, run |
| resonate / resonates with | connect with, appeal to, matter to |
| revolutionize | change, transform, reshape (or describe what changed) |
| facilitate / facilitates | enable, help, allow, run |
| underpin | support, form the basis of |
| nuanced | specific, subtle, detailed (or name the actual nuance) |
| crucial | important, key, necessary |
| multifaceted | (describe the actual facets, or cut) |
| ecosystem (metaphor) | system, community, network, market |
| myriad | many, numerous (or give a number) |
| plethora | many, a lot of (or give a number) |
| encompass | include, cover, span |
| catalyze | start, trigger, accelerate |
| reimagine | rethink, redesign, rebuild |
| galvanize | motivate, rally, push |
| augment | add to, expand, supplement |
| cultivate | build, develop, grow |
| illuminate | clarify, explain, show |
| elucidate | explain, clarify, spell out |
| juxtapose | compare, contrast, set side by side |
| paradigm-shifting | (describe what actually shifted) |
| transformative / transformation | (describe what changed and how) |
| cornerstone | foundation, basis, key part |
| paramount | most important, top priority |
| poised (to) | ready, set, about to |
| burgeoning | growing, emerging (or cite a number) |
| nascent | new, early-stage, emerging |
| quintessential | typical, classic, defining |
| overarching | main, central, broad |
| quietly | cut, or name the concrete contrast |
| deeply *(significance collocations only — "deeply integrated," "deeply committed," "deeply rooted"; literal uses like "deeply nested" or "cares deeply" never count toward a cluster)* | cut, or name what specifically runs deep |
| underpinning / underpinnings | basis, foundation, what supports |

#### Tier 3 — Flag only at high density

These are normal words. Only flag them when the text is saturated with them — a sign that AI filled space with vague praise instead of specifics.

| Word | What to do |
|---|---|
| significant / significantly | Replace some with specifics: numbers, comparisons, examples |
| innovative / innovation | Describe what's actually new |
| effective / effectively | Say how or cite a metric |
| dynamic / dynamics | Name the actual forces or changes |
| scalable / scalability | Describe what scales and to what |
| compelling | Say why it compels |
| unprecedented | Name the precedent it breaks (or cut) |
| exceptional / exceptionally | Cite what makes it an exception |
| remarkable / remarkably | Say what's worth remarking on |
| sophisticated | Describe the sophistication |
| instrumental | Say what role it played |
| world-class / state-of-the-art / best-in-class | Cite a benchmark or comparison |
| verbatim | Usually redundant with the verb ("copies X verbatim" = "copies X") — cut it. If the exactness marks a contrast, name