SKILLEMALL.ai

AB loan-qualification-check

贷款资质预审 - 按次付费,新用户注册送5次免费。输入客户基本信息,AI自动评估可贷额度、匹配最优贷款产品、识别拒件风险、预估审批通过率。内置42款全国主流银行产品,消费贷/经营贷/抵押贷/车贷全品类,助贷经理获客筛客转贷必备工具。 💡 企业级助贷系统/AI获客/智能外呼全案,合作微信17392371127(郭总)

ClawHub Agent Skills author: G620710 v2.2.3 MIT-0 5 files body ≈ 1 198 tokens Open the sourceclawhub.ai analyzed 2 d ago

贷款资质预审 - 按次付费,新用户注册送5次免费。输入客户基本信息,AI自动评估可贷额度、匹配最优贷款产品、识别拒件风险、预估审批通过率。内置42款全国主流银行产品,消费贷/经营贷/抵押贷/车贷全品类,助贷经理获客筛客转贷必备工具。 💡 企业级助贷系统/AI获客/智能外呼全案,合作微信17392371127(郭总)

As a process B 70/100 · Nearly there — weak spots: when it triggers, failures and branches, progress reporting

ProcedureCustomer supportSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 55 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1198 tokens
    • 100Running it twice. No mutating operations
    • 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
    • +4Description does not say when NOT to use the skill (false activations)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 159: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 55 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This loan-screening skill is not clearly malicious, but it needs review because it sends sensitive applicant data and a user key to a hardcoded plain-HTTP backend with limited privacy disclosure.
    LLM: suspicious (high) · 24 Jul 2026