Borrowing it
Nothing to install: this file belongs to belos-street/stock-analytics-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/belos-street/stock-analytics-skill/main/.agents/skills/comparable-company-analysis/SKILL.mdgit clone --depth 1 https://github.com/belos-street/stock-analytics-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/belos-street/stock-analytics-skill/comparable-company-analysis)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/comparable-company-analysis"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/comparable-company-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/comparable-company-analysis"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/comparable-company-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.02528 |
| Opus 5 | $0.00032 | $0.01264 |
| Sonnet 5 | $0.00013 | $0.00506 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
comparable-company-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
可比公司分析
技能核心定位
核心目标
帮你快速构建机构级可比公司分析表(Comps Analysis),自动筛选同行业可比公司,提取关键运营指标、估值倍数(PE、PB、EV/EBITDA等),并进行统计基准对比分析,输出专业的Excel分析表格。
目标用户
- 投资分析师:进行估值分析和股票推荐
- 并购从业者:进行并购估值
- 研究员:行业研究和公司分析
- 投资者:了解公司相对估值水平
技能边界
可提供服务:
- 可比公司筛选
- 估值倍数提取
- 统计基准对比
- 相对估值分析
- 投资建议输出
不可提供服务:
- 具体买卖指令
- 承诺收益
- 预测股价
- 内幕信息
可比公司分析框架
一、可比公司筛选
筛选标准
1. 行业相关性
- 同行业(证监会行业分类)
- 同细分行业(申万行业分类)
- 产品/服务相似
2. 业务相关性
- 相似的收入模式
- 相似的客户群体
- 相似的地理分布
3. 规模相关性
- 市值差异不超过10倍
- 营收差异不超过5倍
4. 可比性调整
- 剔除异常值
- 考虑成长性差异
- 考虑风险差异
筛选结果示例
目标公司:宁德时代
可比公司池:
1. 亿纬锂能 - 锂电池
2. 国轩高科 - 锂电池
3. 欣旺达 - 消费电池
4. 璞泰来 - 负极材料
5. 当升科技 - 正极材料
二、估值倍数选择
估值倍数适用场景
1. PE(市盈率)
- 适用:盈利稳定的成熟公司
- 适合:同行业对比
- 注意:亏损公司不适用
2. PB(市净率)
- 适用:金融行业、重资产公司
- 适合:净资产占比高的公司
- 注意:商誉较高时需调整
3. PS(市销率)
- 适用:尚未盈利的成长公司
- 适合:互联网、SaaS
- 注意:不同商业模式差异大
4. EV/EBITDA
- 适用:资本密集型、并购估值
- 适合:EBITDA为正的公司
- 注意:折旧摊销政策差异
5. EV/Revenue
- 适用:收入稳定公司
- 适合:对比不同规模公司
- 注意:毛利率差异
三、关键指标提取
运营指标
1. 盈利能力
- 毛利率
- 净利率
- ROE
- ROIC
2. 成长性
- 营收增速
- 净利润增速
- 三年CAGR
3. 财务健康
- 资产负债率
- 流动比率
- 经营现金流/净利润
4. 估值指标
- PE、PB、PS
- EV/EBITDA
- 股息率
- PEG
四、统计分析
计算统计量
1. 均值(Mean)
- 简单平均
- 加权平均(按市值)
2. 中位数(Median)
- 剔除极端值影响
3. 高/低四分位数
- 25分位数
- 75分位数
4. 离散度
- 标准差
- 变异系数
五、相对估值分析
估值偏离分析
目标公司估值 = 可比公司均值 × 调整系数
调整系数考虑:
- 成长性差异(高成长可享受溢价)
- 风险差异(高风险需要折价)
- 盈利能力差异(高盈利可享受溢价)
- 业务复杂度(多元化需折价)
报告输出格式
可比公司分析报告
# XXXX 可比公司分析报告
## 一、分析目标
- 目标公司:XXXX
- 分析目的:XXXX
- 估值方法:XXXX
## 二、可比公司筛选
### 筛选标准
1. 行业标准:
2. 业务标准:
3. 规模标准:
### 可比公司池
| 公司名称 | 代码 | 市值(亿) | 营收(亿) | 业务相似度 |
|---------|------|---------|---------|-----------|
| 公司A | | | | 高 |
| 公司B | | | | 中 |
| 公司C | | | | 中 |
## 三、估值倍数对比
### 主要估值指标
| 公司 | PE | PB | PS | EV/EBITDA |
|------|-----|-----|-----|-----------|
| 公司A | | | | |
| 公司B | | | | |
| 公司C | | | | |
| **均值** | | | | |
| **中位数** | | | | |
| 目标公司 | | | | |
### 估值分位数
| 指标 | 目标公司 | 可比公司区间 | 分位 |
|------|---------|------------|------|
| PE | | - | |
| PB | | - | |
| PS | | - | |
## 四、运营指标对比
### 盈利能力
| 公司 | 毛利率 | 净利率 | ROE | ROIC |
|------|--------|--------|-----|------|
| 公司A | | | | |
| 公司B | | | | |
| 行业平均 | | | | |
| 目标公司 | | | | |
### 成长性
| 公司 | 营收增速 | 净利润增速 | 3年CAGR |
|------|---------|-----------|---------|
| 公司A | | | |
| 公司B | | | |
| 行业平均 | | | |
| 目标公司 | | | |
## 五、财务健康
| 公司 | 资产负债率 | 流动比率 | 经营现金流/净利润 |
|------|-----------|---------|------------------|
| 公司A | | | |
| 公司B | | | |
| 行业平均 | | | |
| 目标公司 | | | |
## 六、相对估值结论
### 估值水平判断
PE估值:低估/合理/高估(相对行业均值±20%) PB估值:低估/合理/高估 综合判断:
### 估值调整因素
成长性调整:+XX%(因增速高于行业平均) 风险调整:-XX%(因风险高于行业平均) 盈利能力调整:+XX%(因ROE高于行业平均) 综合调整系数:X.XX
### 目标估值
基于可比公司均值:XX元 基于调整后估值:XX元 当前股价:XX元 潜在涨幅:XX%
## 七、投资建议
### 相对估值角度
- 估值水平:
- 投资逻辑:
- 风险提示:
### 注意事项
1. 可比公司选择可能不完美
2. 需要结合绝对估值验证
3. 估值只是决策因素之一
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 352 lines · 64 tokens per session scan A 22c277d8f9a3
comparable-company-analysis is a skill published in the GitHub repository belos-street/stock-analytics-skill (49 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 2,528 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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