SKILLEMALL.ai

BD insurance-substandard

带病投保顾问。当用户咨询有既往病史或健康异常(如高血压、糖尿病、肿瘤、 结节、肝病等)时能否投保、适合买哪些保险产品时触发。也在用户涉及非标体核保、 健康告知填写、指标异常投保等专业问题时触发。支持众民保系列及主流保司带病体 可投产品的查询与类比,覆盖百万医疗、重疾险、复发险等险种。

ClawHub Agent Skills author: jiazebei v1.0.5 MIT-0 6 files body ≈ 685 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
94
Quality 40%
79
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Secrets in code secret-labelled-token scripts/query_product_knowledge.py:14
    Labelled token / key literal (vendor format unknown — verify it is not a live credential)
    API_KEY = "bfa9…c15"
  • low Secrets in code secret-password-literal scripts/query_product_knowledge.py:14
    Hard-coded password / key literal (may be an example)
    API_KEY = "bfa9…c15"

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (insurance-substandard) differs from the folder (za-healthguide)
  • 100Tools and files. No external tools needed
  • 100Steps. 19 steps
  • 100Execution cost. Instruction body is 685 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 142: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
This insurance guidance skill appears purpose-aligned, but it handles sensitive health-insurance questions and sends them to external services without clear user notice or consent.
LLM: suspicious (high) · VirusTotal: · 29 May 2026