SKILLEMALL.ai

AF cn-client-investigation

China mainland client investigation and banker-grade analysis with strict guards for Chinese text accuracy and data provenance. Use when the target is an A-share / STAR / ChiNext / HK-H or private-unicorn Chinese company and the deliverable must not contain Chinese character-level typos or fabricated numbers. Triggers: 中国 / A股 / 港股 / 中概股 / STAR Market / 创业板 / 北交所 / CNINFO / 巨潮 / 天眼查 / Tushare. This skill supersedes the generic customer-investigation + datapack-builder flow for China targets.

ClawHub Agent Skills author: jackdark v0.9.6 MIT-0 22 files body ≈ 4 978 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process F 53/100 · Will not run — References files that are not bundled: references/data-provenance.md

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: references/data-provenance.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 22. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/data-provenance.md

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: references/data-provenance.md
  • 0Tools and files. 1 referenced file(s) missing: references/data-provenance.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 4978 tokens
  • 100Steps. 67 steps
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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
  • +4Description does not say when NOT to use the skill (false activations)
  • -36 of 16 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 496: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 67 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: suspicious
The skill is a coherent China-company research workflow, but it needs Review because it embeds and transmits API credentials insecurely and runs external build/helper code during deliverable generation.
LLM: suspicious (high) · VirusTotal: · 29 May 2026