AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.
pip install pipxLightweight, open-source AI agent for your tools, chats, and workflows.
git clone https://github.com/HKUDS/nanobot.gitSkills Catalog for Codex
Codex uses skills to help package capabilities that teams and individuals can use to complete specific tasks in a repeatable way. This repository catalogs skills for use and distribution with Codex.
Learn more:
- [Using skills in Codex](https://developers.openai.com/codex/skills)
- [Create custom skills in Codex](https://developers.openai.com/codex/skills/create-skill)
- [Agent Skills open standard](https://agentskills.io)
## Installing a skill
Skills in [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.
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:
An optional voice profile (casual / professional / technical / warm / blunt) sets how the prose should sound, independent of the audience context profile.
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.
A one-shot "make this sound human" prompt catches the obvious stuff. This skill is different:
SKILL.md; this count is enforced against it in CI.SKILL.md runs in Claude Code, Cowork (as a plugin), OpenClaw, and Cursor (as a ported rule). See the install paths below.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.
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.mdinto a folder connected to your Cowork session and tell the agent to follow./SKILL.md— works as a one-off, no auto-trigger.
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
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.
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
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
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 |
Once installed, ask your assistant to clean up AI writing:
In rewrite mode (default), the skill returns four sections:
In detect mode, the skill returns two sections:
Trigger detect mode with: "detect," "flag only," "audit only," "just flag," "scan," or similar.
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 44typecategories 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 indetector/CATEGORIES.md; don't "fix" one count to match the other.
| # | 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" |
| # | 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 |
| # | 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 |
| # | 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 |
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-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 |
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?").
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.
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.
Pattern research informed by:
Authored by Conor Bronsdon · LinkedIn · Chain of Thought podcast
Things the community has built around this skill:
SKILL-DE.md) and Romanian (SKILL-RO.md) adaptations, grounded in native-language research rather than translated from English. French and Spanish planned.$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.
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.
MIT
| 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. |