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.
npx agentmods add rules/practicalswan/agent-skills/avoid-ai-writinggit clone --depth 1 https://github.com/PracticalSwan/agent-skillsWhat 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 | $0.00000 | $0.22162 |
| Opus 5 | $0.00000 | $0.11081 |
| Sonnet 5 | $0.00000 | $0.04432 |
| Haiku 4.5 | $0.00000 | $0.02216 |
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 3d 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**: 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 This is a copy
100% identical to avoid-ai-writing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 838 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
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
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.
- 3d ago First seen · 838 lines · 0 tokens per session scan A 0f35afd2daf8
avoid-ai-writing is a cursor rule published in the GitHub repository PracticalSwan/agent-skills (11 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 22,162 tokens. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to avoid-ai-writing, differing in 0 lines, and is treated as a copy.
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