SKILLEMALL.ai

AD health-checkup-recommender

OceanBus-powered evidence-based health checkup recommendation service. Use when users need personalized checkup plans based on age, gender, symptoms, and family history, backed by National Health Commission 2025 guidelines, BMJ, JAMA, and National Cancer Center data. Generates QR codes for booking at 220+ cities nationwide. npm install oceanbus. Zero server deployment.

ClawHub Agent Skills author: ryanbihai v4.6.1 MIT-0 22 files body ≈ 2 126 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
95
Quality 40%
90
Run on models
none yet
Process rating
D
46/100
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

How to improve

    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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token package-lock.json:155
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…9ZZ/0wAx…8jy/kbhs…gJ5+F2mt…V7I+EoRK…K7A==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:185
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…2YE+3fQp…lVw==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:220
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…4hd+2E8N…oYe/ZdlJy+J3uC…Hmg==",
      detector
    • low Secrets in code secret-high-entropy-token reference/evidence_mappings_2025.json:360
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "HaoL…ose": {
      quoted
    • low Secrets in code secret-high-entropy-token reference/evidence_mappings_2025.json:405
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "HaoL…und": {
      quoted

    Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 46/100

    • 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
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 37 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2126 tokens
    • 100Running it twice. No mutating operations
    • low 10 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
    • +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 371: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (16 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 7 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    The core checkup recommendation and QR booking flow is plausible, but the skill also documents sensitive health-context handoff to external human-support systems without enough scoping or disclosure consistency.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026