Xpersona
agentCLAWHUBUnverified

Finance Data Analysis

Provides AI-driven financial data analysis including KPI tracking, financial statement evaluation, data visualization, and automated reporting for decision s... Skill: Finance Data Analysis Owner: gechengling Summary: Provides AI-driven financial data analysis including KPI tracking, financial statement evaluation, data visualization, and automated reporting for decision s... Tags: banking:5.0.0, dianjin:5.0.0, finance:5.0.0, finance-data-analysis:5.0.3, insurance:5.0.0, latest:5.0.3 Version history: v5.0.3 | 2026-09-15T14:22:39.395Z | user 内容增强与修正（4235→8761字符）：修复 frontmatte

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

5.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

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 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.3K downloadsadoption · observed Oct 10, 2026
Latest release
5.0.3release · observed Sep 15, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:finance-data-analysis
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. 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-data-analysis/snapshot"

Documentation

CLAWHUB

47,699 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Financial Industry Data Analysis Expert
slug: finance-data-analysis
description: AI-powered financial data analysis expert — covers financial statement analysis, KPI tracking, trend analysis, data visualization, and automated reporting. Built for financial analysts, CFO offices, and data-driven decision making. Keywords: financial data analysis, KPI dashboard, data visualization, financial reporting, Python analysis, SQL queries, 金融数据分析, 财务分析, KPI追踪, 数据可视化, Python分析, 数据看板, 经营分析, 业务分析, Excel分析, Pandas分析.
version: "5.0.3"
allowed-tools: []
capabilities:
  - educational-reference
  - advisory-only
  - requires-human-review
  - code-examples-reference
---

# Financial Industry Data Analysis Expert / 金融数据分析专家

> **⚠️ SECURITY NOTICE**
> - **Type:** Educational reference / analytical framework ONLY
> - **技能本身不包含可执行代码**，文中 Python / SQL 片段均为**教学示例**，不会被本技能自动运行
> - **No persistent storage and no background execution** — 本技能不创建、不写入、不读取任何文件
> - **No credential collection, no PII processing, no system access** — 不接触数据库连接串、账号口令或生产数据
> - **All outputs require human review before real-world application**
> - **NOT financial, legal, or insurance advice**
>
> **⚠️ 数据安全警告**
> - 本技能仅提供金融数据分析的方法论参考框架，**不执行任何代码或脚本**
> - 文中提到的市场数据查询、资金流向分析为**教学方法论展示**，不涉及实际的 API 调用或数据采集
> - 使用者如将示例代码落地，须自行完成**数据脱敏、权限审批与留痕**，不得将客户身份信息、账户信息带入分析环境
> - 所有分析结果仅供参考，不构成投资建议或审计意见

> **English:** AI-powered financial data analysis — covers financial statements, KPIs, visualization, and automated reporting.
>
> **中文:** 金融数据分析——覆盖财务报表、KPI、可视化、自动化报告。

---

## 金融监管与行业动态（截至 2026-09-15）

| 动态类型 | 内容摘要 | 对分析工作的影响 |
|---------|---------|----------------|
| 监管合规 | 数据安全与个人信息保护要求在金融业持续压实，"最小必要"原则成为分析取数的默认前提 | 取数环节须可溯源、可解释；分析底稿需记录数据来源与使用范围 |
| 监管合规 | 金融"五篇大文章"（科技、绿色、普惠、养老、数字金融）统计口径逐步细化 | KPI 体系需与监管口径对齐，避免同一指标多口径并存 |
| 监管合规 | 反洗钱与可疑交易监控的数据要求持续加强 | 客户维度分析需区分"分析用"与"报送用"两套口径与授权 |
| 监管合规 | 理财与保险产品信息披露透明度要求提高 | 产品类经营分析需同时满足内部分析与对外披露两类口径 |
| 行业趋势 | 经营分析从"报表解读"转向"指标归因 + 前瞻预测" | 分析交付物需包含归因链条与情景假设，而非仅同比环比 |
| 行业趋势 | 数据中台与指标体系治理成为金融机构标准动作 | 指标定义、口径、责任人需成体系管理，避免"同名不同义" |
| 行业趋势 | 大模型辅助取数、解读与报告生成进入实用阶段 | 生成结果必须人工复核，数字与结论不得直接对外使用 |
| 技术演进 | 湖仓一体与流批一体降低"日终批量"分析的时延 | 日频分析可向准实时演进，但对账与口径一致性要求同步提高 |

> **数据截止**: 2026-09-15 | 来源：国家金融监督管理总局、中国人民银行、中国证监会、中国证券业协会、行业公开研究
> **声明**: 以上动态供参考，政策与口径以官方最新发布为准

---

## Industry Pain Points / 行业痛点

| Pain Point / 痛点 | Impact / 影响 | Solution / 本Skill解决方案 |
|------------------|-------------|------------------------|
| **数据分散** | 数据源多、系统异构，整合耗时且口径不一 | 统一数据模型与指标字典 |
| **手工报表多** | 月报/季报重复劳动，易出错、难追溯 | 报告模板化 + 生成流程标准化 |
| **分析浅** | 只看表面数字，缺归因与前瞻 | 三层归因分析框架 |
| **可视化差** | 图表不直观，管理层读不出结论 | 图表选型对照表 |
| **口径打架** | 同一指标各部门定义不同 | 指标口径与责任人矩阵 |
| **合规风险** | 取数越界、留痕缺失 | 数据使用纪律与留痕清单 |

---

## Trigger Keywords / 触发关键词

**English Triggers:** financial data analysis, KPI dashboard, data visualization, financial reporting, Python analysis

**中文触发词：** 数据分析 / 财务分析 / KPI追踪 / 数据可视化 / 自动化报告 / Python分析 / SQL查询 / 数据看板 / 经营分析 / 业绩分析 / 同比环比 / 指标归因 / 报表搭建 / 口径对齐

---

## Core Capabilities / 核心能力

### 1. Fi

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "finance-data-analysis",
  "version": "5.0.3",
  "publishedAt": 1789482159395
}

skill-card.md

## Description:

Provides financial analysts and finance teams with reference frameworks for financial statement analysis, KPI tracking, trend analysis, data visualization, and reporting.

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

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

Financial analysts, CFO office teams, and data-driven finance users use this skill to structure KPI dashboards, financial statement reviews, trend analysis, visualization choices, and reporting workflows. The skill is advisory and its outputs should be reviewed before business, investment, audit, legal, or regulatory use.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Financial analysis outputs may be incorrect, incomplete, or mistaken for financial, legal, insurance, audit, or investment advice.

Mitigation: Require qualified human review before using outputs for decisions, reporting, disclosure, audit, or regulatory work.

Risk: Users may include customer, account, credential, or production financial data in uncontrolled prompts or downstream examples.

Mitigation: Keep real customer and account data out of uncontrolled prompts; use approved, minimized, aggregated, or desensitized data only.

Risk: Broad finance and Chinese trigger terms may activate the skill in conversations where the reference framework is not intended.

Mitigation: Narrow trigger terms during deployment if unintended activation would disrupt agent behavior.

## Reference(s):


## Skill Output:

**Output Type(s):** [Analysis, Code, Guidance]

**Output Format:** [Markdown with Python and SQL example code blocks]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Advisory reference output requiring human review; no tool execution or data access.]

## Skill Version(s):

5.0.3 (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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Machine-readable data

The same record, as JSON, for agents and crawlers.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/gechengling/skills/finance-data-analysis",
      "sourceUrl": "https://clawhub.ai/gechengling/skills/finance-data-analysis",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:03:00.579Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
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      "href": "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-finance-data-analysis/contract",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-finance-data-analysis/contract",
      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:03:00.579Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "1.3K downloads",
      "href": "https://clawhub.ai/gechengling/finance-data-analysis",
      "sourceUrl": "https://clawhub.ai/gechengling/finance-data-analysis",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:03:00.579Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "5.0.3",
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      "sourceUrl": "https://clawhub.ai/gechengling/finance-data-analysis",
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      "confidence": "medium",
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    {
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      "sourceType": "trust",
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  "events": [
    {
      "eventType": "release",
      "title": "Release 5.0.3",
      "description": "内容增强与修正（4235→8761字符）：修复 frontmatter slug 与实际 slug 不一致（finance-data-analytics→finance-data-analysis）；修复示例代码逻辑错误（ratio_analysis 方法体使用未定义的 data，参数名为 financial_data）；消除安全声明中的重复条目；监管与行业动态更新至 2026-09-15 并扩充至8项；新增指标体系与口径矩阵、数据质量校验六维度表、归因分析三层下钻框架与量价拆解口径、可视化选型对照表、三条参考工作流、常见误用与纠偏表、数据使用纪律（合规底线）；版本号 5.0.2→5.0.3",
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      "sourceUrl": "https://clawhub.ai/gechengling/finance-data-analysis",
      "sourceType": "release",
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      "observedAt": "2026-09-15T14:22:39.395Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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