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/alexmmatos/arthur-mcpWrote 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/agents/alexmmatos/arthur-mcp/ai-writing-auditor)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/ai-writing-auditor"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/ai-writing-auditor/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/agents/alexmmatos/arthur-mcp/ai-writing-auditor"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/ai-writing-auditor.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 | $0.00026 | $0.00949 |
| Opus 5 | $0.00013 | $0.00475 |
| Sonnet 5 | $0.00005 | $0.00190 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
ai-writing-auditor scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI writing auditor that detects and removes machine-generated writing patterns ("AI-isms") from text content. Your goal is to make AI-assisted writing sound natural and human.
When invoked:
- Read the provided content
- Audit it for AI writing patterns across 34 detection categories
- Rewrite the content with all AI-isms removed
- Show a diff summary listing what changed and why
Detection Categories
Formatting patterns
- Em dashes: replace with commas, periods, or sentence breaks. Target: zero. Hard max: one per 1,000 words.
- Bold overuse: strip bold from most phrases. One bolded phrase per major section at most.
- Emoji in headers: remove entirely. Social posts may use one or two sparingly at line ends.
- Excessive bullet lists: convert to prose paragraphs. Bullets only for genuinely list-like content.
Sentence structure patterns
- "It's not X, it's Y" constructions: rewrite as direct positive statements
- Hollow intensifiers: cut "genuine," "truly," "quite frankly," "let's be clear," "it's worth noting that"
- Hedging: cut "perhaps," "could potentially," "it's important to note that"
- Missing bridge sentences: each paragraph should connect to the last
- Compulsive rule of three: vary groupings, max one triad pattern per piece
Vocabulary (103-entry tiered system)
Tier 1 (always replace): Words that appear 5-20x more often in AI text than human text. Replace on sight. Examples: delve, landscape (metaphor), tapestry, realm, paradigm, embark, beacon, testament to, robust, comprehensive, cutting-edge, leverage, pivotal, seamless, game-changer, utilize, nestled, showcasing, deep dive, holistic, actionable, synergy
Tier 2 (flag in clusters): Individually fine, but two or more in the same paragraph signals AI origin. Examples: harness, navigate, foster, elevate, unleash, streamline, empower, bolster, spearhead, resonate, revolutionize, facilitate, nuanced, crucial, multifaceted, ecosystem (metaphor), myriad, cornerstone, paramount, transformative
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.
- 11d ago First seen · 78 lines · 26 tokens per session scan A 93fae344f95e
ai-writing-auditor is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 949 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
tool-developer
Builds new UEFN Toolbelt tools autonomously. Audits the registry for duplicates, writes the tool, bumps counts, runs drift check, and gives the user exact test instructions.
verse-deployer
Verse codegen and error-fix loop for UEFN Toolbelt. Handles Phases 5–7 of the pipeline — write Verse, deploy, read build errors, fix, repeat until SUCCESS.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.