zhtw-mcp: Skill for Claude Code

.claude/skills/zhtw-rules/SKILL.md

zhtw-rules is a skill for Claude Code from sysprog21/zhtw-mcp. It costs 119 tokens per session (1,593 once invoked), scanned A, original, MIT.

A guide for maintaining rules that flag incorrect or potentially misleading Traditional Chinese wording. It explains how the rule file, validation checks, text positions, and surrounding context work together.

In plain words
What is it for?
It helps add, edit, test, and format zh-TW vocabulary rules. It is for maintaining a language linter, which is a tool that checks text for unwanted wording.
Why use it?
It reduces false warnings where a term is correct in Traditional Chinese but has a different meaning in Simplified Chinese. It also gives a repeatable way to check rule changes before they are accepted.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions CLAUDE.md.

This is sysprog21/zhtw-mcp's own configuration. It tells Claude Code how to work on zhtw-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything zhtw-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to sysprog21/zhtw-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/sysprog21/zhtw-mcp/main/.claude/skills/zhtw-rules/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/sysprog21/zhtw-mcp

Made for: Claude Code.

Wrote 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.

agentmods badge for zhtw-rules

README.md
[![agentmods](https://agentmods.dev/badge/skills/sysprog21/zhtw-mcp/zhtw-rules/github.svg)](https://agentmods.dev/skills/sysprog21/zhtw-mcp/zhtw-rules)
Your own site
<a href="https://agentmods.dev/skills/sysprog21/zhtw-mcp/zhtw-rules"><img src="https://agentmods.dev/badge/skills/sysprog21/zhtw-mcp/zhtw-rules/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.

agentmods 80×15 button for zhtw-rules

Your own site · 80×15
<a href="https://agentmods.dev/skills/sysprog21/zhtw-mcp/zhtw-rules"><img src="https://agentmods.dev/badge/skills/sysprog21/zhtw-mcp/zhtw-rules.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,593 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00119 $0.01593
Opus 5.5 $0.00048 $0.00637
Sonnet 5.5 $0.00024 $0.00319
Haiku 4.5 $0.00012 $0.00159

Measured 1mo ago against content hash 80d0570451cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

zhtw-rules 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.

.claude/skills/zhtw-rules/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Changing what zhtw-mcp flags

assets/ruleset.json is the source of truth. build.rs serializes it into the binary with postcard, scripts/check-ruleset.py owns its dedup, sort and field order, and src/rules/schema.rs is the type the two agree on through the generated scripts/schema-facts.json. Hand formatting is rewritten and the indent gate fails on it, so the loop is: edit, python3 scripts/check-ruleset.py --lint, make indent.

The false friend is the whole problem

A from term that is also valid zh-TW with a different meaning is the failure mode this project has to defend against, because it turns the linter into something people switch off. 文件 is "file" in zh-CN and "document" in zh-TW. 字體 is a typeface here and a font file there. An ungated rule for either fires on correct prose.

Four answers, in order of how much they cost the reader:

  • disabled: true when the term cannot be judged from the sentence at all.
  • context_clues and negative_context_clues when a nearby word settles it.
  • exceptions when a fixed phrase is the only safe carve-out.
  • editorial_confidence for the milder case, and context_suggestions when the correction itself differs by domain.

The two terms above took different answers, which is the point of having four. 文件 to 檔案 is disabled, because a sentence holding it reads correctly under either meaning. 字體 to 字型 ships enabled behind context_clues, because the typeface sense travels with words the rule can look for.

A rule that needs none of these is a rule where the zh-CN form has no zh-TW reading, which is most of the vocabulary list and none of the hard cases.

The corpus is the argument, not the opinion

The assertions in tests/corpus-evaluation.rs are what settles whether a rule pays for itself. make corpus prints their table, but it does not gate: cargo autodiscovers that target, so make check has already run every one of them. There are ten, not the three the printed table draws the eye to:

Read the full file on GitHub · 116 lines

Changes

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.

  1. 1mo ago Changed · +2 lines 80d0570451cb
  2. 1mo ago Changed · +22 lines cc8b3e3c9f0d
  3. 1mo ago First seen · 92 lines · 119 tokens per session scan A b8761643e28b

Subscribe to this mod's changes

zhtw-rules is a skill published in the GitHub repository sysprog21/zhtw-mcp (495 stars, last pushed 17d ago), licensed MIT. It adds 119 tokens to every session and 1,593 once invoked, about $0.0005 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 2026-09-05.

Related

Other skills, from other repositories

intl-expansion

International market expansion strategy. Market selection, entry modes, localization, regulatory compliance, and go-to-market by region. Use when expanding to new countries, evaluating international markets, planning localization, or building regional teams.

alirezarezvani/claude-skills · 46 tokens

chinese-documentation

A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.

jnMetaCode/superpowers-zh · 62 tokens

azure-ai-translation-text-py

Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications. Triggers: "text translation", "translator", "translate text", "transliterate", "TextTranslationClient".

microsoft/skills · 60 tokens

new-app

Scaffold a new Atomic Agents project from scratch — create the directory, pyproject.toml, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs /atomic-agents:new-app.

Eigenwise/atomic-agents · 76 tokens

edit-site

Plans, coordinates and customizes PAC CLI-downloaded Standard or Enhanced data model non-SPA Power Pages sites created from Microsoft templates or manually in Design Studio. Use for requests involving pages, localized content, navigation, assets, snippets, site markers, Liquid/web templates, page layouts, or styling.…

microsoft/power-platform-skills · 104 tokens

troubleshoot

Diagnose and fix a failing or misbehaving Atomic Agents app — import errors, schema validation failures, empty or malformed LLM output, provider role/mode errors, history and context problems, MCP transport errors. Use when the user reports an error, traceback, or wrong behavior in atomic-agents code, asks "why is my…

Eigenwise/atomic-agents · 103 tokens