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 prose file in place (via the Edit tool) with minimal, targeted changes, preserving passages that are already human. Source code, configuration, and generated data are refused because prose rewrites can corrupt structured content. 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.
- 112-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 matches flag unless a listed exception applies, Tier 2 words flag when they cluster, Tier 3 words flag only at high density. Tier 1 itself splits into 1A frequency markers (
delve, tapestry) and 1B clarity edits (in order to, utilize) — same fix, but only 1A is evidence about how a passage was produced, and 1B is weighted lower so a wordiness fix cannot push a document toward an AI classification. 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.
- 74 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
references/patterns.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 — a directory-based skill runs in Claude Code, Cowork (as a plugin), OpenClaw, Cursor (as a ported rule), and other directory-aware agents. See the install paths below.
Installation & Usage
Install the complete skill directory
Use the plugin install below or clone the repository into your agent's skills directory. Keep SKILL.md with references/patterns.md: the entry file loads the catalog before auditing. The bundled scripts/, detector/, and examples/ provide optional mechanical verification.
For a single-file rules field, use dist/avoid-ai-writing.md. It includes every rule and profile, with manual fallbacks for commands unavailable outside the bundle. Do not copy the slim entry file alone. Older installers that fetch only root SKILL.md omit its required reference; use a directory install instead.
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 a self-contained file
Download dist/avoid-ai-writing.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.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 dist/avoid-ai-writing.md into a folder connected to your Cowork session and tell the agent to follow ./avoid-ai-writing.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:
git clone https://github.com/conorbronsdon/avoid-ai-writing ~/.hermes/skills/writing/avoid-ai-writing
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:
git clone https://github.com/conorbronsdon/avoid-ai-writing .agents/skills/avoid-ai-writing
Native ChatGPT and Codex plugin package
Published in the OpenAI Plugins Directory; install it there for ChatGPT or Codex.
The plugin package keeps the canonical SKILL.md as its editorial authority and provides seven focused Skills:
avoid-ai-writing — uses the canonical audit and rewrite instructionsavoid-ai-writing-router — routes mixed and multi-stage requestsai-writing-detector — runs the bundled deterministic detectorvoice-preserving-rewriter — rewrites returned text while retaining voicefile-edit-in-place — edits only explicitly named filespreservation-verifier — checks meaning and constraints before and after a rewritefalse-positive-reviewer — reviews detector findings that need context
Build and validate the package with:
python3 scripts/package-openai-plugin.py . /tmp/avoid-ai-writing.zip --json
python3 scripts/validate-openai-plugin.py . --json
Other agents
The generated dist/avoid-ai-writing.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 dist/avoid-ai-writing.md into the project's custom instructions |
| ChatGPT Custom GPTs |
paste dist/avoid-ai-writing.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:
- Issues found — every AI-ism identified, with the text quoted
- Rewritten version — clean version with all AI-isms removed
- What changed — summary of the major edits
- Second-pass audit — re-reads the rewrite and catches any surviving tells
In detect mode, the skill returns two sections:
- Issues found — every AI-ism identified, grouped by severity (P0/P1/P2)
- 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 54 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 |
| # |
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 |
| # |
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 modifier stacking |
"a high-quality, well-architected, future-proof solution" |
Cut to the modifier that matters; the individual hyphens may be correct |
| 48 |
Infomercial engagement hooks |
"T |
|
Переносимый навык для письма, который проверяет текст на «паттерны письма ИИ» (штампы вроде слов-паразитов, избегание связки «быть», рекламные формулировки) и переписывает его так, чтобы он звучал по-человечески, или только отмечает проблемы, ничего не меняя.