Stock Fundamental Analysis

对上市公司进行深度基本面分析，包括财务健康度评估、估值分析、成长性分析、行业对标、财报解读，输出结构化投研报告

Security Scan

The skill's description (deep stock fundamental analysis) mostly matches the included valuation and fetcher code, but the runtime instructions reference many additional tools that are missing and rely on an external 'toc-trading' adapter of unknown origin — these inconsistencies warrant caution before installing.

Purpose & Capability

The skill claims to perform multi‑dimensional fundamental analysis and includes a valuation engine (valuation_engine.py) and a data fetcher (financial_fetcher.py), which is coherent with the stated purpose. However, SKILL.md expects many other helper scripts (health_checker.py, growth_analyzer.py, industry_comparator.py, report_parser.py, shareholder_tracker.py) that are not present in the bundle. The fetcher also inserts a path to a local 'toc-trading/src' module (stock_data_adapter) which is not declared in requirements or documented — relying on a local/sidecar package is unexpected and unexplained.

Instruction Scope

The SKILL.md runtime commands call a set of tools that do not exist in the package (e.g., tools/health_checker.py, tools/growth_analyzer.py). That means following the provided instructions will fail or cause the integrator/agent to search for or attempt to import missing modules. The instructions also reference {baseDir} and expect scripts under tools/ but the manifest only includes two files. No instructions ask for unrelated system files, but the mismatch gives the agent broad discretion to locate/require other code (toc-trading), which is a scope creep and operational risk.

Install Mechanism

There is no install spec (instruction-only), which lowers installation risk. However, financial_fetcher.py dynamically adds a relative path to 'toc-trading/src' into sys.path at runtime; this implicitly depends on an external/local repository to be present. Because that dependency is not declared or bundled, it is unexpected and could cause the agent to import code from an unknown location on disk or fail at runtime.

Credentials

The skill requests a single API credential (STOCK_DATA_API_KEY) as its primary credential, which is proportionate for fetching market/financial data. The included files do not read other environment variables. Note: the supplied code does not explicitly show how STOCK_DATA_API_KEY is consumed — likely the stock_data_adapter (external) would use it, so the key's usage should be confirmed before granting.

Persistence & Privilege

The skill does not request always: true and does not declare any system config paths. It is user-invocable and allows normal autonomous invocation. There is no evidence the skill modifies other skills or system-wide settings.

What to consider before installing

Do not install or enable this skill yet. Key concerns: (1) SKILL.md lists several helper scripts (health_checker.py, growth_analyzer.py, industry_comparator.py, report_parser.py, shareholder_tracker.py) that are missing from the package — ask the author to supply those files or update the instructions. (2) financial_fetcher.py appends a relative 'toc-trading/src' path and imports StockDataAdapter from there; confirm where 'toc-trading' comes from, whether it will be present at runtime, and inspect that adapter's code for how it handles your STOCK_DATA_API_KEY and remote endpoints. (3) Verify the provenance of the package (source/homepage unknown) and ensure the data provider is trusted and licensed. If you must test this skill, do so in an isolated environment, monitor network calls, and avoid providing high‑privilege or long‑lived credentials until you confirm how the key is used.

股票基本面分析技能

触发条件

• "XX公司基本面怎么样"
• "帮我分析一下XX的财报"
• "XX估值高不高"
• "XX和YY哪个更值得投资"
• "这家公司财务健康吗"

分析维度与工具调用

维度一：财务健康度评估

python3 {baseDir}/tools/health_checker.py --symbol <代码> --years 5

检查项:

检查项	健康标准	权重
资产负债率	<60% (非金融)	15%
流动比率	>1.5	10%
速动比率	>1.0	10%
经营性现金流/净利润	>0.8	15%
有息负债率	<40%	10%
应收账款周转天数	行业中位数以下	10%
商誉/净资产	<20%	10%
连续3年净利润为正	是	10%
审计意见	标准无保留	10%

输出: 0-100 财务健康评分 + 各项明细 + 风险提示

维度二：估值分析

python3 {baseDir}/tools/valuation_engine.py --symbol <代码> --method all

估值方法:

1. PE 估值法: 当前PE vs 历史PE分位数 vs 行业平均PE
2. PB 估值法: 当前PB vs 历史PB分位数 vs 行业平均PB
3. PEG 估值法: PE/净利润增速，<1低估，1-1.5合理，>2高估
4. DCF 折现模型: 自由现金流折现 → 内在价值 → 安全边际
5. PS 估值法: 适用于高增长未盈利公司

输出: 综合估值判断（低估/合理/高估）+ 合理价格区间

维度三：成长性分析

python3 {baseDir}/tools/growth_analyzer.py --symbol <代码> --years 5

分析:

• 营收增速（近1/3/5年 CAGR）
• 净利润增速
• ROE 趋势（杜邦分解：净利率×周转率×杠杆）
• 毛利率/净利率变化趋势
• 研发投入占比趋势
• 市场份额变化

维度四：行业对标

python3 {baseDir}/tools/industry_comparator.py --symbol <代码> --peers auto

对标维度:

• PE/PB/PS 对比
• ROE/毛利率/净利率对比
• 营收/净利润增速对比
• 市值/市占率对比 输出: 雷达图数据 + 行业排名

维度五：财报解读

python3 {baseDir}/tools/report_parser.py --symbol <代码> --quarter latest

解读重点:

• 营收/净利润 同比/环比变化 → 超预期/低于预期
• 毛利率变化 → 产品竞争力
• 费用率变化 → 管理效率
• 现金流变化 → 经营质量
• 关键风险：大额减值、关联交易、非经常性损益占比

维度六：股东/机构分析

python3 {baseDir}/tools/shareholder_tracker.py --symbol <代码>

追踪:

• 十大股东变化（增/减持）
• 北向资金持仓变化
• 公募基金重仓变化
• 高管增减持记录
• 股权质押比例

综合评分体系

维度	权重	评分范围
财务健康度	25%	0-100
估值合理性	25%	0-100
成长性	25%	0-100
行业竞争力	15%	0-100
机构认可度	10%	0-100

综合评分 = Σ(维度评分 × 权重)

输出规范

• Markdown 结构化报告，包含数据表格
• 明确标注数据来源和截止日期
• 必须包含⚠️免责声明：基本面分析仅供参考，不构成投资建议
• 对不确定的结论标注置信度（高/中/低）