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Financial Statement Analyzer

Community
Geeksfino
financial-statement-analyzer

对单个A股上市公司的财务报表进行深度分析,评估盈利质量、财务健康状况、财务造假风险和运营效率。当用户要求深入分析某家公司的财务报表、杜邦分析、盈利质量检查、资产负债表分析、现金流分析、Z值评分、M值评分、营运资本分析,或任何详细的单公司财务审视时使用此技能。

Overview

PublisherGeeksfino
Repositoryfinskills
Skill namefinancial-statement-analyzer
Stars
285
Forks
45
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Geeksfino on GitHub. Read the source before you install it.

Installation

Install the Financial Statement Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Geeksfino/finskills/tree/main/China-market/financial-statement-analyzer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Geeksfino/finskills.git /tmp/finskills
mkdir -p .claude/skills
cp -r /tmp/finskills/China-market/financial-statement-analyzer .claude/skills/financial-statement-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Financial Statement Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Financial Statement Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Financial Statement Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

财务报表深度分析

扮演法证财务分析师。对单个A股上市公司的财务报表进行全面分析,评估盈利质量、财务健康状况、财务造假风险指标和运营效率。

工作流程

第一步:确定分析对象

与用户确认:

  1. 公司 — 股票代码或名称
  2. 时间跨度 — 默认:最近5年(20个季度)
  3. 重点领域 — 全面分析(默认)或特定聚焦(盈利质量、造假风险、营运资本等)
  4. 对比公司 — 用于基准比较的主要竞争对手(可选)
  5. 数据来源 — 以年报、半年报、季报为主要数据源

第二步:盈利能力分解

进行5因子杜邦分析。详细公式参见 references/analysis-methodology.md。

ROE = 税负系数 × 利息负担系数 × 营业利润率 × 资产周转率 × 权益乘数
组成部分公式揭示内容
税负系数净利润 / 税前利润税收效率
利息负担系数税前利润 / EBIT债务成本影响
营业利润率EBIT / 营业收入运营效率
资产周转率营业收入 / 总资产资产利用效率
权益乘数总资产 / 股东权益财务杠杆

追踪各组成部分5年变化趋势,识别ROE变化的驱动因素。

第三步:盈利质量评估

评估报告盈利是否反映真实经济价值:

测试衡量内容红灯阈值
应计比率非现金盈利占比> 总资产的10%
现金转化率经营现金流 / 净利润持续 < 0.8
收入确认收入增长 vs 应收账款增长应收增速超过收入
合同负债/预收合同负债趋势下降(提前确认收入)
非经常性项目一次性收益/费用频率"非经常性"项目每年出现
政府补助依赖政府补助占净利润比例> 30%(利润可持续性存疑)

第四步:财务健康评分

计算综合财务健康指标:

模型用途组成部分
Altman Z值破产预测营运资本、留存收益、EBIT、市值、营收——均相对于总资产
Piotroski F值财务实力9个二元信号,涵盖盈利能力、杠杆和效率
Beneish M值盈利操纵检测8个变量,衡量财务数据中的异常

详细公式和解读参见 references/analysis-methodology.md。

第五步:营运资本分析

通过现金转化周期考察运营效率:

指标公式揭示内容
应收账款周转天数(DSO)(应收账款 / 营业收入) × 365收款效率
存货周转天数(DIO)(存货 / 营业成本) × 365存货管理
应付账款周转天数(DPO)(应付账款 / 营业成本) × 365付款实践
现金转化周期DSO + DIO − DPO营运资本效率

追踪5年趋势并与同行比较。

第六步:资产负债表风险评估

识别表外和隐性风险:

风险领域检查内容
商誉/无形资产相对净资产的规模;减值风险(A股并购商誉减值是常见雷区)
关联交易与控股股东及关联方的交易规模和定价公允性
应收票据及应收账款商业承兑汇票的信用风险
在建工程长期不结转固定资产的项目(可能的费用资本化)
其他应收款对关联方的资金占用
股份支付稀释影响
有息负债到期结构短期偿债压力 vs 现金/再融资能力
股权质押大股东股权质押比例

第七步:业务分部分析

按业务分部拆解业绩:

  • 各分部营收及增长
  • 各分部利润率
  • 各分部资本密集度
  • 识别哪些分部创造价值、哪些毁灭价值
  • 分部间的交叉补贴

第八步:同行基准比较

与2–3家直接竞争对手比较关键指标:

指标类别具体指标
盈利能力毛利率、营业利润率、净利率、ROE、ROIC
效率资产周转率、存货周转率、应收周转率
杠杆资产负债率、利息保障倍数、净负债/EBITDA
估值PE、EV/EBITDA、P/FCF、PEG
成长性营收CAGR、EPS CAGR、FCF CAGR

第九步:综合与呈现

按 references/output-template.md 编制报告:

  1. 公司概览 — 业务概述和关键指标
  2. 杜邦分解 — 5年ROE驱动分析
  3. 盈利质量报告 — 应计、现金转化、红灯项
  4. 财务健康评分 — Z值、F值、M值及解读
  5. 营运资本分析 — CCC趋势和同行比较
  6. 资产负债表风险图 — 隐性风险和表外项目
  7. 分部分析 — 价值驱动和拖累
  8. 同行比较 — 基准比较表
  9. 综合评估 — 从财务视角的多空分析
  10. 免责声明

数据增强

如需实时市场数据支撑分析,请使用金融数据工具包技能(findata-toolkit-cn)。该工具包提供A股实时行情、财务指标、董监高增减持、北向资金、宏观数据等功能,所有数据源免费,无需API密钥。

重要注意事项

  • 读附注:财务报表中最重要的信息往往在附注中。标记任何异常的会计政策、关联交易或重大估计。
  • 趋势比绝对值重要:下降的毛利率比低但稳定的毛利率更令人担忧。始终强调方向性变化。
  • A股特有陷阱:关联交易转移利润、政府补助撑利润、在建工程长期不转固、商业承兑汇票坏账风险——这些是A股财务分析的特有关注点。
  • 中国会计准则:A股使用企业会计准则(CAS),与US GAAP有差异。主要差异体现在收入确认、金融工具分类、政府补助处理等方面。
  • 补充而非替代估值:本技能评估财务质量,而非判断股票买卖。估值相关问题请导向相应的筛选技能。
  • GAAP vs 扣非:关注扣除非经常性损益后的净利润,这是评估A股公司持续经营能力的更可靠指标。
  • 非审计:这是分析性审阅,不是专业审计。无法检测高级造假或验证数据准确性。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Financial Statement Analyzer AI skill do?

对单个A股上市公司的财务报表进行深度分析,评估盈利质量、财务健康状况、财务造假风险和运营效率。当用户要求深入分析某家公司的财务报表、杜邦分析、盈利质量检查、资产负债表分析、现金流分析、Z值评分、M值评分、营运资本分析,或任何详细的单公司财务审视时使用此技能。

Why use Financial Statement Analyzer on TypingMind?

Because you install it once and use it with any model. Financial Statement Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Financial Statement Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Geeksfino/finskills/tree/main/China-market/financial-statement-analyzer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Financial Statement Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Financial Statement Analyzer?

As many as you like. As long as a model supports skills, you can use Financial Statement Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Financial Statement Analyzer AI skill free?

Yes. It is published on GitHub by Geeksfino under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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