avoid-ai-writing is a portable writing skill that detects common patterns associated with AI-generated prose and can rewrite them or make targeted edits to prose files. It is for users of Claude Code, OpenClaw, Hermes, and other compatible coding agents who want text to sound more like human writing. The catalogue entries are the skill's rule and instruction components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/conorbronsdon/avoid-ai-writingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/conorbronsdon/avoid-ai-writing/avoid-ai-writing)<a href="https://agentmods.dev/rules/conorbronsdon/avoid-ai-writing/avoid-ai-writing"><img src="https://agentmods.dev/badge/rules/conorbronsdon/avoid-ai-writing/avoid-ai-writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/conorbronsdon/avoid-ai-writing/avoid-ai-writing"><img src="https://agentmods.dev/badge/rules/conorbronsdon/avoid-ai-writing/avoid-ai-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.29970 |
| Opus 5.5 | $0.00000 | $0.11988 |
| Sonnet 5.5 | $0.00000 | $0.05994 |
| Haiku 4.5 | $0.00000 | $0.02997 |
Grade A, and why
avoid-ai-writing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Curly quotation marks (“ ” ‘ ’) and apostrophes**: Check the destination's style, not authorship. Word, Google Docs, macOS, and iOS curl quotes by default; an editor or house style can also produce curly punctuation, Copies of this mod
2 near-identical copies found in the catalogue:
- avoid-ai-writing — 100% identical, 441 lines differ
- avoid-ai-writing — 94% identical, 543 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,045 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
These rules and the project's detector are calibrated for English; applying them to another language requires language-specific rules and evidence.
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.
Editing contract
Apply this contract before turning a pattern match into a change. A candidate match is text worth checking. It becomes a finding only after the rule's pass conditions, context exceptions, and the surrounding meaning have been read. A finding becomes an edit only when the user's requested mode and scope authorize one. Detection alone never authorizes rewriting.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed d763b3bcf420
- 4d ago Changed · +2 lines 5c2d3517b950
- 10d ago Changed f4aab4ee2848
- 11d ago Changed · +1 lines ba3a4e667d6c
- 13d ago Changed b93d2c53f547
- 16d ago Changed e9bf20482793
- 18d ago Changed 42da2d961c67
- 20d ago Changed 00ea1f81cf2d
- 21d ago Changed · +2 lines 2c9422617237
- 23d ago First seen · 1,040 lines · 0 tokens per session scan A 9a5c9c0403e6
avoid-ai-writing is a cursor rule published in the GitHub repository conorbronsdon/avoid-ai-writing (5,073 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 29,970 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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