SKILLEMALL.ai

BB Chinese

Writes and edits Mandarin Chinese that reads as if a native wrote it — casual or formal, mainland or Taiwan. Use when composing anything in Chinese (WeChat message, work email, 小红书 or 公众号 post, resume, 请假条, speech, toast, 通知, contract clause), or when Chinese text sounds stiff, textbook, translated, or AI-generated; when choosing 你 versus 您, 口语 versus 书面语, simplified versus traditional, or mainland versus Taiwan vocabulary; when 的/地/得, 了, 把, 被 or measure words come out wrong; when full-width punctuation, Han-Latin spacing, or 万/亿 number grouping is off; when internet slang, 成语, emoji or 表情包 need calibrating to an audience; when a refusal, apology, compliment or request has to land politely; or when naming a product or a person in Chinese. Not for translating an existing source text (`translate`), Traditional-only writing (`traditional-chinese`), or travelling in China (`china`).

ClawHub Agent Skills author: Iván v1.0.2 MIT-0 20 files body ≈ 6 902 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 77/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

GeneratorWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
B
77/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 20. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning body-long SKILL.md body ≈ 6902 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "changelog"

Process rating: all ten parameters 77/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 60Result and completion. Output format stated, no completion criterion
  • 70Failures and branches. 7 branches
  • 70Execution cost. Instruction body is 6902 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Description length 891: 120–800 characters recommended
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 44 items
  • +3Output format is stated explicitly

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
The skill is a coherent Chinese-writing assistant, but it automatically builds long-lived local records about people, documents, business communications, outcomes, and mistakes without requiring explicit user consent before writes.
LLM: suspicious (high) · VirusTotal: · 27 Jul 2026