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AD finance-consumer-guard

金融消保合规护栏 (finance-consumer-guard) v1.0.0。 在银行理财 / 保险 / 基金 / 资管等金融产品的营销文案、销售话术、广告投放、官网介绍、 对外宣传发布前,实时检测"保本保收益(刚兑)、收益承诺夸大、风险弱化误导、 过往业绩误导、无资质代客理财、广告极限词"相关的高频违规 / 高风险表述, 按风险分级输出命中与整改建议,供 Agent 主动调用。区别于事后深度审计,这是事前拦截。 Use when: 需要在金融产品营销文案、销售话术、广告、官网介绍、对外宣传发布前, 实时拦截"保本保收益 / 稳赚不赔 / 低风险高收益 / 代客理财 / 最佳理财"等高频危险表述; 防止触碰资管新规(打破刚兑)、《商业银行理财业务监督管理办法》、 《理财公司理财产品销售管理暂行办法》、《保险销售行为管理办法》、 《广告法》第二十五条的金融消保红线;为金融机构 / 理财经理 / 合规 / 运营加装一道轻量实时护栏。 核心能力: - 🛡️ 实时检测 6 类高频危险表述:保本保收益(刚兑) / 收益承诺夸大 / 风险弱化误导 / 过往业绩误导 / 无资质代客理财 / 广告极限词 - 📊 风险分级(high / medium / low)与逐条整改建议 - 🔍 重叠命中智能去重(保留高 severity / 更长匹配),降低误报 - 📋 结构化 JSON 输出,便于 Agent 程序化调用与批量扫描 - 🧱 内核与规则分离:规则集中在 scripts/rules/terms.py,追加词即可扩展 触发关键词:保本保收益、稳赚不赔、低风险高收益、代客理财、最佳理财、金融消保、 理财合规、finance-consumer-guard、资管新规 适用范围:银行理财 / 保险 / 基金 / 资管等金融产品营销文案、销售话术、广告、官网介绍、对外宣传的发布前实时检测 运行模式:纯本地,零网络请求,零动态执行 外部依赖:Python 标准库(无需额外安装) 本产品为免费护栏,纯本地运行,零网络请求,文本输入即可评估。

ClawHub Agent Skills author: Wei Wu v1.0.0 MIT-0 9 files body ≈ 839 tokens Open the sourceclawhub.ai analyzed 2 d ago

金融消保合规护栏 (finance-consumer-guard) v1.0.0。 在银行理财 / 保险 / 基金 / 资管等金融产品的营销文案、销售话术、广告投放、官网介绍、 对外宣传发布前,实时检测"保本保收益(刚兑)、收益承诺夸大、风险弱化误导、 过往业绩误导、无资质代客理财、广告极限词"相关的高频违规 /…

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

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
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 · 0

    ✓ No critical or high findings

    Files scanned: 9. 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 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 (python) that frontmatter does not declare
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 839 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)
    • +3Description length 884: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This skill is a local financial-marketing compliance checker that scans supplied text and does not request network, credential, or persistent access.
    LLM: benign (high) · VirusTotal: · 9 Aug 2026