What makes Finance Training Coach notable?

960 downloads. Trust evidence available

How should Finance Training Coach be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Finance Training Coach

AI-powered financial industry training system offering skill assessment, personalized learning paths, role-play exercises, quizzes, and performance tracking... Skill: Finance Training Coach Owner: gechengling Summary: AI-powered financial industry training system offering skill assessment, personalized learning paths, role-play exercises, quizzes, and performance tracking... Tags: banking:5.0.0, dianjin:5.0.0, finance:5.0.0, finance-training-coach:5.0.1, insurance:5.0.0, latest:5.0.1 Version history: v5.0.1 | 2026-09-14T06:00:35.756Z | user v5.0.1: 新增数据最小化声明；监管动态更新至 2026-09

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

960

Updated

Oct 12, 2026

Version

5.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 960 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 960 downloadsadoption · observed Oct 12, 2026
Latest release: 5.0.1release · observed Sep 14, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 5.0.1release
v5.0.1: 新增数据最小化声明；监管动态更新至 2026-09-14（AI安全开发、销售行为合规、培训留痕）；修复评估引擎代码缺陷并补调用示例；新增两个完整角色扮演对话示例与合规课程示例；四表新增维度
Observed Sep 14, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:finance-training-coach

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-finance-training-coach/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

26,663 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Financial Industry Training & Coaching System
slug: finance-training-coach
description: AI-powered financial industry training and coaching system — covers skill assessment, personalized learning paths, role-play training, knowledge quizzes, and performance tracking. Built for financial institutions' training departments. Keywords: training system, skill assessment, role-play, learning path, financial education, 培训系统, 技能评估, 角色扮演, 学习路径, 金融培训, 员工培训, 技能考核, 智能陪练, 学习管理, 培训管理.
version: "5.0.1"
---

# Financial Industry Training & Coaching System / 金融行业培训陪练系统

> **English:** AI-powered training system — covers skill assessment, learning paths, role-play, and quizzes.
>
> **中文:** 培训陪练系统——覆盖技能评估、学习路径、角色扮演、考核测验。

---


### 金融监管最新动态 [2026-09-14更新]

| 动态类型 | 内容摘要 | 影响范围 | 对应培训动作 |
|---------|---------|---------|------------|
| 金融监管 | 2026年Q1：金融行业培训需覆盖最新合规要求 | 培训课程库需增加2026年合规和AI工具课程 | 每年至少一次全员合规必修 + 留痕 |
| 金融监管 | 保险销售人员分级制度、银行合规管理、反洗钱培训 | 培训课程库需增加2026年合规和AI工具课程 | 按岗位分级设计必修课与考核线 |
| 金融监管 | AI工具使用培训需求上升（MCP/RAG/Agent等） | 培训课程库需增加2026年合规和AI工具课程 | 新增"AI 工具合规使用"通识模块 |
| 新增·AI安全开发应用 | 银行业保险业人工智能应用的安全开发要求正下沉为机构内部操作规程 | 技术与业务双条线均需培训 | 新增"AI 应用六环节"专题：立项评估/数据合规/模型验证/上线审批/运行监测/退出 |
| 新增·销售行为合规 | 线上销售可回溯、适当性管理、禁止合同外利益等要求细化 | 一线销售与直播岗位必修 | 角色扮演题库需补充线上销售合规场景 |
| 新增·数据安全 | 员工培训数据与客户案例数据的合规使用受关注 | 培训组织者必修 | 培训案例脱敏规范纳入课程 |

**新动态解读（截至 2026-09-14）：**
- **AI 素养从"选修"变"必修"**：金融机构对员工的 AI 能力要求不再局限于技术部门，业务条线也需要理解模型能力边界、数据合规与人工复核要求。培训体系应区分"AI 使用者""AI 应用设计者""AI 治理者"三类角色分别设课。
- **培训留痕成为检查项**：监管检查与内部审计 increasingly 关注"培训是否真实发生、考核是否有效"，因此签到、考试、成绩、补训记录需要形成闭环，而不能只有课程清单。
- **角色扮演场景更新**：线上直播、数字人客服、智能投顾推荐等新场景带来新的合规风险，陪练题库应同步更新，避免只练传统面对面销售场景。
- 以上为公开信息综述，**具体口径以国家金融监督管理总局、中国证监会、中国证券业协会及所在机构合规部门官方最新发布为准**。

> **数据截止**: 2026-09-14 | 来源：证监会、NFRA、中证协、安永Q1分析
> **声明**: 以上动态供参考，具体以官方最新发布为准

## ⚠️ 使用前必读：数据最小化声明

> 培训与陪练过程会涉及**员工个人信息**（姓名、工号、考核成绩、能力短板、客户沟通记录）。这类数据属于个人信息，部分场景下属于敏感个人信息。
>
> **请遵循数据最小化原则：**
> 1. 用于评估与陪练时优先使用**工号或化名**，非必要不录入真实姓名、身份证号、联系方式
> 2. 角色扮演中涉及的客户案例，应使用改写后的虚拟案例，不得直接粘贴真实客户信息或完整通话录音文本
> 3. 考核结果与能力雷达图属于人事管理数据，导出、分享与留存须符合所在机构的人事数据管理制度
> 4. 本 Skill 输出的学习建议仅供参考，不直接作为晋升、绩效或淘汰依据，最终人事决策须由具备权限的人员按制度作出

---

## Industry Pain Points / 行业痛点

| Pain Point / 痛点 | Impact / 影响 | Solution / 本Skill解决方案 | 典型表现 | 可量化指标 |
|------------------|-------------|------------------------|---------|-----------|
| **培训效率低** | 统一培训不适应个体差异 | 个性化学习路径 | 资深员工陪听基础课、新人听不懂进阶课 | 完课率、人均有效学时 |
| **实战不足** | 理论多、实操少 | AI角色扮演陪练 | 考试高分但面对客户说不出话 | 陪练轮次、情景通过率 |
| **效果难评估** | 培训效果无法量化 | 技能雷达图 | 只能统计"培训了多少人" | 前后测分差、行为改变率 |
| **知识遗忘快** | 培训后知识快速遗忘 | 间隔重复机制 | 一个月后合规要点回忆率不足一半 | 30/60/90 天留存率 |
| **学与用脱节** | 培训内容落后于业务变化 | 场景题库持续更新 | 新业务上线但题库仍是旧场景 | 题库更新频率、新场景覆盖率 |

---

## Trigger Keywords / 触发关键词

**English Triggers:** training system, skill assessment, role-play, learning path, financial education

**中文触发词（优先）：** 培训 / 陪练 / 技能评估 / 角色扮演 / 学习路径 / 考核 / 测验 / 金融培训

**使用示例（完整提问）：**
- "帮我给 30 名新入职保险代理人设计 2 周培训计划，含每日课程与考核点" → 走能力 1 + G.3 学习路径
- "生成 10 道反洗钱合规测验题，含单选、多选和案例分析" → 走 G.4 测评题目类型
- "我要和客户演练异议处理，扮演一个嫌贵的客户" → 走能力 2 角色扮演
- "某员

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "finance-training-coach",
  "version": "5.0.1",
  "publishedAt": 1789365635756
}

skill-card.md

## Description:

AI-powered financial industry training and coaching system for financial institution training departments, covering skill assessment, personalized learning paths, role-play training, knowledge quizzes, and performance tracking.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

Training teams and managers at financial institutions use this skill to design employee learning paths, role-play exercises, quizzes, skill assessments, and coaching feedback. Outputs should remain educational and should not be used for personalized financial recommendations, suitability judgments, or employment decisions without authorized human review.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill may generate content that reads like personalized investment advice, buy/sell/hold conclusions, or suitability judgments.

Mitigation: Keep outputs limited to training scenarios, fictional or synthetic examples, and general education; require qualified review before any customer-facing or advisory use.

Risk: Training workflows can involve real employee performance data or customer case information.

Mitigation: Use pseudonyms, employee IDs, and synthetic or redacted cases where possible; obtain internal approval before processing real employee, customer, or personnel data.

## Reference(s):


## Skill Output:

**Output Type(s):** [text, markdown, code, configuration, guidance]

**Output Format:** [Markdown with occasional code examples, tables, and structured training plans]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces educational training content, assessment rubrics, role-play prompts, quizzes, learning paths, and coaching recommendations.]

## Skill Version(s):

5.0.1 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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