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.
curl -O https://raw.githubusercontent.com/sysprog21/zhtw-mcp/main/.claude/skills/zhtw-rules/SKILL.mdgit clone --depth 1 https://github.com/sysprog21/zhtw-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/skills/sysprog21/zhtw-mcp/zhtw-rules)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00119 | $0.01593 |
| Opus 5.5 | $0.00048 | $0.00637 |
| Sonnet 5.5 | $0.00024 | $0.00319 |
| Haiku 4.5 | $0.00012 | $0.00159 |
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.
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: truewhen the term cannot be judged from the sentence at all.context_cluesandnegative_context_clueswhen a nearby word settles it.exceptionswhen a fixed phrase is the only safe carve-out.editorial_confidencefor the milder case, andcontext_suggestionswhen 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:
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 Changed · +2 lines 80d0570451cb
- 1mo ago Changed · +22 lines cc8b3e3c9f0d
- 1mo ago First seen · 92 lines · 119 tokens per session scan A b8761643e28b
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.
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