avoid-ai-writing

用具体检查项发现并修改空泛、套路化和过度修饰的 AI 文风。

主要能力

Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an edit-in-place mode for files, an optional voice profile (casual / professional / technical / warm / blunt), and an iterate-to-convergence pass.

使用方式

仓库级安装会保留完整目录；本次核验的入口是 SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

它适合编辑已有文本，不应被当作事实核验或原创性证明工具。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

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avoid-ai-writing

Audit & rewrite content to remove AI writing patterns. A practical skill for any AI agent. Supports detect-only and edit-in-place modes, plus voice profiles.

<img src="docs/demo.gif" alt="The bundled detector engine flagging 13 AI-writing patterns by category in a sample paragraph, then scoring the clean rewrite 0/100" width="800"> </div>

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A portable writing skill for Claude Code, OpenClaw, Hermes, and any other agentskills.io-compatible agent. Audits and rewrites content to remove AI writing patterns ("AI-isms").

Three modes:

• Rewrite (default) — flags AI patterns and rewrites the text to fix them. A built-in second pass catches patterns that survived the first edit.
• Detect — flags AI patterns without rewriting. Shows which flags are real problems vs. judgment calls. Useful when patterns might be intentional, when auditing content you don't want altered, or when you just want a quick scan.
• Edit — edits a file in place (via the Edit tool) with minimal, targeted changes, preserving passages that are already human. Returns an edits-made + verification report, not the full file.

An optional voice profile (casual / professional / technical / warm / blunt) sets how the prose should sound, independent of the audience context profile.

Quick demo

Input:

Certainly! Acme Analytics, a vibrant startup nestled in the heart of Boulder's thriving tech ecosystem, has secured $40M in Series B funding — marking a watershed moment for the observability landscape. The platform serves as a unified hub, featuring real-time dashboards, boasting sub-second queries, and presenting a seamless integration layer. Moreover, experts believe Acme is poised to disrupt the market. In conclusion, the future looks bright!

Output:

Acme Analytics raised a $40M Series B led by Sequoia. The Boulder-based startup makes an observability platform that runs queries in under a second and plugs into existing monitoring stacks without custom integration work.

What it caught: chatbot opener ("Certainly!"), promotional language ("vibrant," "nestled," "thriving"), significance inflation ("watershed moment"), copula avoidance ("serves as," "featuring," "boasting"), 4 word replacements, vague attribution ("experts believe"), filler ("Moreover"), generic conclusion ("the future looks bright"), over-polished uniformity. 15+ AI tells in one paragraph.

Why a skill, not just a prompt

A one-shot "make this sound human" prompt catches the obvious stuff. This skill is different:

• Structured audit — returns identified issues with quoted text, the rewrite, a change summary, and a second-pass audit in four discrete sections. You see exactly what changed and why.
• Two-pass detection — the second pass re-reads the rewrite and catches patterns that survive the first edit: recycled transitions, lingering inflation, copula swaps that snuck through.
• 109-entry word replacement table across 3 tiers + 10 Tier 3 phrases — not vibes-based. Every flagged word has a specific, plainer alternative. "Leverage" → "use." "Commence" → "start." Tier 1 words always flag, Tier 2 words flag when they cluster, Tier 3 words flag only at high density. Tier 3 phrases (multi-word boilerplate like "the integration of," "decentralized compute") flag on per-phrase repetition or when 3+ distinct phrases stack in one piece — the LLM-self-varies-boilerplate shape.
• 53 pattern categories — representative examples below, each with before/after. Includes structural detection (hashtag stuffing, bare-NP bullet lists, hedge-stacked predictions), AI-tool fingerprints (placeholders, citation markup, UTM params), rhythm/uniformity checks, conversational-register tells, and writer-side tests. The full catalog lives in SKILL.md; this count is enforced against it in CI.
• Detect mode — flag patterns without rewriting. See which flags are real problems vs. judgment calls. Useful when patterns might be intentional or you're auditing content you don't want altered.
• Works across platforms — one SKILL.md runs in Claude Code, Cowork (as a plugin), OpenClaw, and Cursor (as a ported rule). See the install paths below.

Installation & Usage

Claude Code

Option 1: Clone into skills directory

git clone https://github.com/conorbronsdon/avoid-ai-writing ~/.claude/skills/avoid-ai-writing

Option 2: Copy the file directly

Download SKILL.md and place it in any directory that Claude Code can read. Reference it in your CLAUDE.md:

- Editing for AI patterns → read `path/to/avoid-ai-writing/SKILL.md`

Option 3: Use as a slash command

Create a command file (e.g., ~/.claude/commands/clean-ai-writing.md):

---
description: Audit and rewrite content to remove AI writing patterns
---

$ARGUMENTS

Read and follow the instructions in ~/.claude/skills/avoid-ai-writing/SKILL.md

Then use /clean-ai-writing <your text> in Claude Code.

Claude Cowork — install as a plugin

Cowork loads skills only from installed plugins — it doesn't scan ~/.claude/skills/, so a bare clone (the Claude Code steps above) won't be discovered there. This repo doubles as a single-plugin marketplace, so install it as a plugin instead:

/plugin marketplace add conorbronsdon/avoid-ai-writing
/plugin install avoid-ai-writing@conorbronsdon-skills
/reload-plugins   # or restart the session, to activate the skill

In the Cowork desktop app, do the same from Customize → Plugins → Add marketplace from GitHub (conorbronsdon/avoid-ai-writing), then install avoid-ai-writing. The skill auto-triggers from phrases like "remove AI-isms." New releases arrive when the plugin's version is bumped — run /plugin marketplace update to pull them.

The same plugin install works in Claude Code if you'd rather have a versioned, updatable plugin than the file clone above.

Prefer not to install a plugin? Copy SKILL.md into a folder connected to your Cowork session and tell the agent to follow ./SKILL.md — works as a one-off, no auto-trigger.

OpenClaw

Option 1: Install from ClawHub

clawhub install avoid-ai-writing

Option 2: Clone into skills directory

git clone https://github.com/conorbronsdon/avoid-ai-writing ~/.openclaw/skills/avoid-ai-writing

Cursor

Drop the ported rule into your project's .cursor/rules/:

mkdir -p .cursor/rules
curl -o .cursor/rules/avoid-ai-writing.mdc \
  https://raw.githubusercontent.com/conorbronsdon/avoid-ai-writing/main/cursor-rules/avoid-ai-writing.mdc

See cursor-rules/README.md for activation globs and trigger phrases. Functionally identical to the Claude Code skill — same tier vocabulary, same context profiles, same modes.

Hermes

Drop the skill into Hermes's skills directory — it then appears automatically as /avoid-ai-writing, no registration needed:

mkdir -p ~/.hermes/skills/writing/avoid-ai-writing
curl -o ~/.hermes/skills/writing/avoid-ai-writing/SKILL.md \
  https://raw.githubusercontent.com/conorbronsdon/avoid-ai-writing/main/SKILL.md

OpenAI Codex

Codex reads Agent Skills in the same SKILL.md format. Put it in .agents/skills/ at the repo root, or ~/.agents/skills/ to use it across all your projects:

mkdir -p .agents/skills/avoid-ai-writing
curl -o .agents/skills/avoid-ai-writing/SKILL.md \
  https://raw.githubusercontent.com/conorbronsdon/avoid-ai-writing/main/SKILL.md

Other agents

The same SKILL.md (or the Cursor .mdc port) drops into most tools' rules/skills location:

Tool	Where to put it
Windsurf	.windsurf/rules/avoid-ai-writing.md
Cline	.clinerules/avoid-ai-writing.md
GitHub Copilot (VS Code)	paste into .github/copilot-instructions.md
Claude.ai Projects	paste SKILL.md into the project's custom instructions
ChatGPT Custom GPTs	paste SKILL.md into the GPT's Instructions field

Triggering the skill

Once installed, ask your assistant to clean up AI writing:

• "Remove AI-isms from this post"
• "Audit this draft for AI tells"
• "Make this sound less like AI"
• "Clean up AI writing in this paragraph"

In rewrite mode (default), the skill returns four sections:

1. Issues found — every AI-ism identified, with the text quoted
2. Rewritten version — clean version with all AI-isms removed
3. What changed — summary of the major edits
4. Second-pass audit — re-reads the rewrite and catches any surviving tells

In detect mode, the skill returns two sections:

1. Issues found — every AI-ism identified, grouped by severity (P0/P1/P2)
2. Assessment — which flags are clear problems vs. patterns that may be intentional or effective in context

Trigger detect mode with: "detect," "flag only," "audit only," "just flag," "scan," or similar.

Pattern reference

Representative examples from the catalog — not the exhaustive list (that's SKILL.md). The skill's human-facing prose catalog and the detector engine use different counts on purpose: the engine implements 44 type categories because it splits the vocabulary tiers and adds stylometric/fingerprint signals (punctuation distribution, function-word entropy, bypass-trick detection) that work as math over a document rather than as a rule you'd look up. The two are mapped in detector/CATEGORIES.md; don't "fix" one count to match the other.

Content Patterns

#	Pattern	Before	After
1	Significance inflation	"marking a pivotal moment in the evolution of..."	"was founded in 2019 to solve X"
2	Notability name-dropping	"cited in NYT, BBC, and Wired"	"In a 2024 NYT interview, she argued..."
3	Superficial -ing analyses	"symbolizing... reflecting... showcasing..."	Replace with specific facts or cut
4	Promotional language	"nestled within the breathtaking region"	"is a town in the Gonder region"
5	Vague attributions	"Experts believe it plays a crucial role"	"according to a 2019 survey by Gartner"
6	Formulaic challenges	"Despite challenges... continues to thrive"	Name the challenge and the response
7	Novelty inflation	"He introduced a term I hadn't heard before"	"He walked through how X works in practice"

Language Patterns

#	Pattern	Before	After
8	Word/phrase replacements (3 tiers)	"leverage... robust... seamless... utilize"	"use... reliable... smooth... use"
9	Copula avoidance	"serves as... features... boasts"	"is... has"
10	Synonym cycling	"developers... engineers... practitioners... builders"	"developers" (repeat the clear word)
11	Template phrases	"a [adj] step towards [adj] infrastructure"	Describe the specific outcome
12	Filler phrases	"In order to," "Due to the fact that"	"To," "Because"
13	False ranges	"from the Big Bang to dark matter"	List the actual topics
14	Parenthetical hedging	"tools (like X and Y)"	Name them directly or cut

Structure Patterns

#	Pattern	Before	After
15	Formatting	Em dashes (— and --), bold overuse, emoji headers, bullet-heavy	Commas/periods, prose paragraphs
16	Sentence structure	"It's not X, it's Y" + hollow intensifiers + hedging	Direct positive statements
17	Structural issues	Uniform paragraphs, formulaic openings, too-clean grammar	Varied length, lead with the point
18	Transition phrases	"Moreover," "Furthermore," "In today's [X]"	"and," "also," or restructure
19	Inline-header lists	"Speed: Speed improved by..."	Write the point directly
20	Title case headings	"Strategic Negotiations And Partnerships"	"Strategic negotiations and partnerships"
21	Numbered list inflation	"Here are 7 reasons why..."	Cut to the 2-3 that matter
22	False concession	"While X has limitations, it's still remarkable"	State the real tradeoff
23	Rhetorical question openers	"What if there were a better way to...?"	Lead with the claim

Communication Patterns

#	Pattern	Before	After
24	Chatbot artifacts	"I hope this helps! Let me know if..."	Remove entirely
25	"Let's" constructions	"Let's explore," "Let's break this down"	Just start with the point
26	Cutoff disclaimers	"While details are limited in available sources..."	Find sources or remove
27	Generic conclusions	"The future looks bright," "Only time will tell"	Specific closing thought or cut
28	Emotional flatline	"What surprised me most," "I was fascinated to discover"	Earn the emotion or cut the claim
29	Reasoning chain artifacts	"Let me think step by step," "Breaking this down"	State conclusion, then evidence
30	Sycophantic tone	"Great question!", "You're absolutely right!"	Remove entirely
31	Acknowledgment loops	"You're asking about," "To answer your question"	Just answer directly
32	Confidence calibration	"It's worth noting," "Interestingly," "Surprisingly"	Let the fact speak for itself

Meta Patterns

#	Pattern	Before	After
33	Excessive structure	5 headers in 200 words, "Overview:", "Key Points:"	Merge sections, use specific headers
34	Rhythm and uniformity	All sentences 15–25 words, all paragraphs same length	Mix short/long, fragments, questions
35	Over-polishing	Every irregularity sanded away, perfectly uniform prose	Keep natural disfluency, varied rhythm
36	Rewrite-vs-patch threshold	5+ vocabulary flags + 3+ pattern categories + uniform rhythm	Advise full rewrite, not patching

Structural Detection (v3.4)

Added in v3.4 to catch LLM output that sidesteps the vocabulary tables by substituting synonyms but still leans on structural shapes detectors can identify. Crypto/web3/AI-infra content is where these patterns concentrate most heavily, but the rules generalize to any social-length post.

#	Pattern	Before	After
37	Tier 3 phrases (multi-word boilerplate)	"the integration of," "decentralized compute," "community-driven," "long-term sustainability" stacked across a piece	Replace the repeated phrase with a specific claim, or vary genuinely. Flagged per-phrase at ≥2 hits, or as a cluster when ≥3 distinct phrases appear
38	Future-narrative closers	"may become one of the most important narratives of the next market cycle"	Pick the falsifiable version. "X may exceed Y by 2027" is a prediction; the template form is not
39	Hedge-stacked predictions	"could potentially create," "may eventually unlock"	Pick one. Each hedge cancels the next
40	"Real/actual" adjective inflation	"real on-chain tokenomics," "actual reward sustainability"	Drop the empty intensifier and add the specific claim. Carve-out: "real on-chain settlement, not bridged IOUs" is honest contrastive writing — the AI tell is the unsaid contrast
41	Hashtag stuffing	15-tag trailing block: #AI #Crypto #Web3 #Innovation #FutureTech…	2-3 specific tags max, or none. Empirical threshold: 6+ tags is near-universal in LLM social output, rare in thoughtful human posts
42	Bullet lists of bare noun phrases	* Stable mining efficiency / Reliable pool connectivity / Optimized RandomX performance / Low failed share rates / Effective hardware utilization / Consistent thermal stability	Convert to prose, or rewrite each item as a full claim with a verb and a number. Carve-out: genuine list content (changelogs, parameter docs, ingredient lists) where bare NPs are correct

AI-tool fingerprints & later additions (v3.5–3.8)

#	Pattern	Before	After
43	Unfilled placeholders	[Your Name], [INSERT SOURCE], 2025-XX-XX	Fill in with real content or delete — shipped placeholders are a near-definitive tell
44	Chatbot citation markup	citeturn0search0, oai_citation, contentReference[oaicite:0]	Strip the markup token entirely
45	AI-tool URL parameters	utm_source=chatgpt.com, utm_source=copilot.com	Strip the tracking parameter; keep the URL if the link matters
46	Speculative gap-filling	"maintains a low profile," "likely began his career"	Cut the guess, or replace with a sourced fact
47	Hyphenated-pair overuse	"a high-quality, well-architected, future-proof solution"	Cut to the modifier that matters; no hyphen in predicate ("the report is high quality")
48	Infomercial engagement hooks	"The catch?", "The kicker?", "Here's the thing."	Delete the hook, state the thing
49	Vocabulary diversity (low TTR)	Narrow, repetitive word range across 200+ words	Broaden the what — name specific things, cite specific cases
50	Self-labeling significance	"That last move is the contrarian one," "This is the interesting part"	Cut the label; let the explanation carry the weight, or reposition the item so it stands out on its own
51	List-label periods	- **Intros.** Years of conferences and operator network. (also unbolded: - Intros. Years of...)	Use a colon, not a period, on a list label: - **Intros:** years of conferences and operator network.

Conversational-register patterns (v3.15)

Added after a real-world exchange in which a maintainer called out an assisted-sounding GitHub issue reply with "I prefer to talk human to human." Both are judgment calls rather than regex-detectable (an ordinary human paragraph and an AI-generated one can look structurally identical; the tell is the register and the redundant context, not a fixed shape) — see the detector/CATEGORIES.md §C note for why a first attempt at a wall-of-text detector was reverted.

#	Pattern	Before	After
52	Wall-of-text replies	A 4+ sentence, sub-150-word reply delivered as one unbroken paragraph with no line breaks — the shape LLMs default to in issue/PR comments, chat, and DMs	Break at thought boundaries. One idea per line-group, the way a person actually types a reply
53	Recap-flattery opener	"Thanks for all the legwork here — the migration script and the rollback plan you worked through are what made this possible."	Substance first. If thanks is warranted, one plain clause without the recap: "Thanks for the legwork — this looks right to me"

Two writer-side tests round out the catalog (judgment checks, not auto-detected): paragraph-reshuffle immunity (can you swap two body paragraphs without breaking the piece?) and the treadmill effect ("what's actually new in this paragraph?").

Full Example

Before (AI-generated):

Certainly! Here's a comprehensive overview of Acme's Series B. Acme Analytics, a vibrant startup nestled in the heart of Boulder's thriving tech ecosystem, has secured $40M in Series B funding — marking a watershed moment for the company and the observability landscape at large. The round was led by Sequoia, with participation from Andreessen Horowitz, Y Combinator, and Index Ventures, underscoring the robust investor confidence in Acme's vision. The platform serves as a unified hub for engineering teams, featuring real-time dashboards, boasting sub-second query performance, and presenting a seamless integration layer. It's not just another monitoring tool — it's a paradigm shift in how organizations navigate complex distributed systems. Experts believe Acme is poised to disrupt the $15B observability market. Studies show that companies utilizing comprehensive monitoring solutions can ascertain issues 40% faster. - 🚀 Performance: The platform streamlines incident response, empowering engineers to resolve issues faster. - 💡 Scale: Acme fosters collaboration across teams, from frontend developers to SRE practitioners to platform builders to infrastructure engineers. - ✅ Adoption: Customer adoption continues to accelerate, reflecting broader industry trends. Moreover, Acme's approach showcases the potential of modern observability — symbolizing a shift toward proactive monitoring, reflecting years of R&D investment, and highlighting the intricate interplay between data collection and actionable intelligence. Despite challenges common in the crowded monitoring space, Acme continues to thrive. In order to fully capitalize on this momentum, the company plans to expand its go-to-market endeavors. In conclusion, the future looks bright for Acme Analytics. Only time will tell how this game-changer will reshape the observability paradigm. Feel free to reach out if you'd like more details!

After (cleaned up):

Acme Analytics raised a $40M Series B led by Sequoia. The Boulder-based startup makes an observability platform that runs queries in under a second and plugs into existing monitoring stacks without custom integration work. The pitch is speed. Their median time-to-resolution is 12 minutes, compared to the 45-minute industry average that Datadog reported in 2024. That gap is why 380 companies are paying for it. They'll use the money to hire sales reps in EMEA and add log management. The monitoring market is crowded, but Acme's bet is that most teams still stitch together three or four tools and lose time switching between them.

What the skill caught: chatbot artifacts (Certainly!, Feel free to reach out), 3 em dashes, promotional language (vibrant, nestled, thriving), significance inflation (watershed moment), copula avoidance (serves as, featuring, boasting, presenting), 10 word replacements (landscape, robust, seamless, paradigm, streamline, empower, foster, utilize, ascertain, endeavor), synonym cycling (developers/practitioners/builders/engineers), negative parallelism (It's not just X, it's Y), notability name-dropping (Sequoia, a16z, YC, Index stacked for credibility), vague attributions (Experts believe, Studies show), filler phrases (In order to, Moreover), inline-header list with emoji, superficial -ing analysis (symbolizing... reflecting... highlighting...), formulaic challenges (Despite challenges... continues to thrive), generic conclusion (the future looks bright, only time will tell), false range implied in the adoption bullet.

That's 35+ AI tells.

Run the detector

The skill ships a deterministic, zero-dependency detection engine in detector/ — the same 45-category engine the rules above describe, as runnable code. It works in Node (>=18) and the browser with no build step.

It's also the single source of the numeric score: the skill itself (and detect mode) report which patterns are present and how severe (P0/P1/P2), and the engine is what turns those into one computed 0–100 score. There's deliberately no second, prose-estimated score in SKILL.md — one scorer, not two.

npm test          # run the detector's fixtures (no deps to install)

const AIDetector = require("./detector/patterns.js");
const { score, label, issues } = AIDetector.analyzeText("Your text here…");

See detector/README.md for the full analyzeText API and detector/CATEGORIES.md for the rule ↔ category map that keeps SKILL.md and the engine in sync.

Credits

Pattern research informed by:

• Pangram Labs AI detection research — structural regularity insights, vocabulary flags from a decoder-only classifier trained on 28M human documents
• Wikipedia's Signs of AI-generated text documentation — the canonical reference for AI writing tells, maintained by Wikipedia editors
• blader/humanizer Claude Code skill
• brandonwise/humanizer — tiered vocabulary system, statistical analysis research (burstiness, sentence length variation, trigram repetition), and rewrite philosophy
• OpenClaw humanizer skill ecosystem — community patterns and vocabulary research

Authored by Conor Bronsdon · LinkedIn · Chain of Thought podcast

Community / Multilingual

Things the community has built around this skill:

• avoid-ai-writing-multilingual by Jürgen Kraus — German (SKILL-DE.md) and Romanian (SKILL-RO.md) adaptations, grounded in native-language research rather than translated from English. French and Spanish planned.
• $avoid token + burn web app — a community-built Solana token ($avoid) and token-burn web app around this project (2026), now in maintenance mode.

Built something on top of this skill? Open an issue — happy to link it here.

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Disclaimer

All views, opinions, and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of Modular. This account is not affiliated with, authorized by, or endorsed by Modular in any way.

License

MIT