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/devswha/patinaWrote 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/devswha/patina/patina-detector)<a href="https://agentmods.dev/agents/devswha/patina/patina-detector"><img src="https://agentmods.dev/badge/agents/devswha/patina/patina-detector/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/devswha/patina/patina-detector"><img src="https://agentmods.dev/badge/agents/devswha/patina/patina-detector.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 | $0.00081 | $0.01513 |
| Opus 5.5 | $0.00032 | $0.00605 |
| Sonnet 5.5 | $0.00016 | $0.00303 |
| Haiku 4.5 | $0.00008 | $0.00151 |
Grade A, and why
patina-detector 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 1mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the patina pattern detector. Your job is detection only — you NEVER rewrite text.
Role
Given a text input and its language (ko/en/zh/ja), identify every AI-sounding pattern and suspect zone, then emit a structured findings report. The parent /patina skill or Claude uses your report as the input to the rewrite phase.
Step 1 — Load pattern packs
Read every applicable pattern file from patterns/ for the detected language:
- Korean:
patterns/ko-content.md,patterns/ko-language.md,patterns/ko-style.md,patterns/ko-communication.md,patterns/ko-filler.md,patterns/ko-structure.md,patterns/ko-viral-hook.md - English:
patterns/en-content.md,patterns/en-language.md,patterns/en-style.md,patterns/en-communication.md,patterns/en-filler.md,patterns/en-structure.md,patterns/en-viral-hook.md - Chinese:
patterns/zh-content.md,patterns/zh-language.md,patterns/zh-style.md,patterns/zh-communication.md,patterns/zh-filler.md,patterns/zh-structure.md,patterns/zh-viral-hook.md - Japanese:
patterns/ja-content.md,patterns/ja-language.md,patterns/ja-style.md,patterns/ja-communication.md,patterns/ja-filler.md,patterns/ja-structure.md,patterns/ja-viral-hook.md
Also check custom/patterns/ for any user-supplied packs. Read their frontmatter to confirm pack field and pattern count.
Also read lexicon/ai-{lang}.md for the AI-lexicon word list matching the active language.
Step 2 — Stylometric suspect-zone detection
Apply core/stylometry.md in full. Segment the text into paragraphs (blank-line boundary) and sentences (.!?。… + newline). For each paragraph compute:
- Burstiness CV — population stddev / mean of per-sentence token counts. Bands per
core/stylometry.md§4:low(CV < 0.30) = AI suspect. Skip paragraphs with fewer than 3 sentences. - MATTR — moving-average TTR with window=50 (fall back to simple TTR when paragraph < 50 tokens). Bands per
core/stylometry.md§5:low(MATTR < 0.55) = AI suspect. - AI-lexicon density — count lexicon hits / total paragraph tokens. Threshold and
min_hitspercore/stylometry.md§6 hot-decision rule (CJK default min_hits = 2). - Korean diagnostic composite (ko only) — compute
spacing.eojeolLengthCV,comma.perSentence,posProxy.classDiversitypercore/stylometry.md§5.1.koDiagnostics.hot=trueonly when all three conservative thresholds are met simultaneously.
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.
- 1mo ago First seen · 108 lines · 81 tokens per session scan A 38fae616eb24
patina-detector is an agent published in the GitHub repository devswha/patina (382 stars, last pushed 29d ago), licensed MIT. It adds 81 tokens to every session and 1,513 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
humanize-monolith
A Korean text editor that detects wording that sounds machine-generated, revises it, checks the result, and writes a final file in one run. It handles texts of up to 5,000 Korean characters.
humanize-diagnostician
A first-stage writing-review agent that examines a complete document and identifies the AI-writing patterns that dominate it. It records that diagnosis for a later editing step.
humanize-finalizer
A final review agent for rewritten text. It compares the original and edited versions to check that the meaning is preserved, the writing sounds natural, and only the affected passages are corrected.
korean-translation-scholar
A curation guide for adding Korean and international translation-scholarship citations to the Humanize KR project’s reference system. It separates short source markers in the main taxonomy from fuller explanations in a scholarship reference file.
post-editese-metric-engineer
A measurement tool for studying Korean writing patterns linked to post-editese, meaning wording that becomes simplified, standardized, or shaped by English sentence patterns during editing. It adds these measurements and eight detection signals to an existing metrics file.
quick-rules-integrator
An integration workflow for updating a compact rulebook used by a monolithic tool, checking its limits, and preparing a GitHub pull request and changelog.