What makes A股个股诊断 (A-Share Stock Diagnosis) notable?

1.2K downloads. Trust evidence available

How should A股个股诊断 (A-Share Stock Diagnosis) be evaluated before use?

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

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

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A股个股诊断 (A-Share Stock Diagnosis)

A股个股自上而下综合诊断 — 先调用 china-macro-climate 获取宏观分析，再识别所属申万一级行业并调用 industry-diagnosis 获取行业分析，然后从基本面/技术面/资金面/消息面四维度进行个股分析，最后综合宏观+行业+个股三层面给出投资星级和操作建议。触发场景：(1) 个股... Skill: A股个股诊断 (A-Share Stock Diagnosis) Owner: stevenge791 Summary: A股个股自上而下综合诊断 — 先调用 china-macro-climate 获取宏观分析，再识别所属申万一级行业并调用 industry-diagnosis 获取行业分析，然后从基本面/技术面/资金面/消息面四维度进行个股分析，最后综合宏观+行业+个股三层面给出投资星级和操作建议。触发场景：(1) 个股... Tags: analysis:1.0.0, china-stocks:1.0.0, finance:1.0.0, latest:1.0.0, stock:1.0.0 Version history: v1.0.0 | 2026-05-27T08:14:17.856Z | user 首版发布：自上而下三层分析框架——宏观(china-macro-climate)→行业(indust

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

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 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed May 27, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1772hk5cbz8s8pz2z248atkps87heg8:a-share-stock-diagnosis

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-stevenge791-a-share-stock-diagnosis/snapshot"

Documentation

CLAWHUB

9,130 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: stock-diagnosis
description: >
  A股个股自上而下综合诊断 — 先调用 china-macro-climate 获取宏观分析，
  再识别所属申万一级行业并调用 industry-diagnosis 获取行业分析，
  然后从基本面/技术面/资金面/消息面四维度进行个股分析，
  最后综合宏观+行业+个股三层面给出投资星级和操作建议。
  触发场景：(1) 个股诊断/个股分析/股票分析 (2) XXX股票怎么样/能不能买
  (3) 自上而下选股/投资分析 (4) 某某股票基本面/技术面分析
  (5) 某某股票的综合诊断。
---

# A股个股诊断 (Stock Diagnosis)

## 概述

采用**自上而下(Top-Down)** 三层分析法：

```
宏观层 → 行业层 → 个股层 → 综合诊断
```

---

## 工作流程

### Step 1: 宏观分析

1. 调用 `china-macro-climate` 技能进行中国宏观景气分析
2. 获取完整的宏观分析结果
3. **将完整的宏观分析报告原样输出到最终报告的第二部分**

### Step 2: 行业分析

1. 确定该股票所属的申万一级行业
   - 可通过 mx-finance-data 查询该股票的行业归属
2. 调用 `industry-diagnosis` 技能进行行业诊断
3. 获取完整的行业分析报告
4. **将完整的行业分析报告原样输出到最终报告的第三部分**

### Step 3: 个股分析（四维度）

从以下四个维度对个股进行全面分析，每个维度必须逐项展开：

| 维度 | 权重 | 核心问题 |
|------|------|----------|
| **基本面** | 35% | 公司质地如何？估值合理吗？ |
| **技术面** | 25% | 当前价格趋势和位置如何？ |
| **资金面** | 20% | 聪明钱在买还是卖？ |
| **消息面** | 20% | 近期有什么催化或风险？ |

#### 3.1 基本面分析

| 子项 | 指标 | 数据源 | 分析要点 |
|------|------|--------|----------|
| **盈利能力** | ROE(TTM)、毛利率、净利率、营收/利润增速 | mx-finance-data 财报 | 是否持续改善？与行业对比如何？ |
| **估值水位** | PE(TTM)、PB、PS、PE/PB历史分位(近5年) | mx-finance-data 估值 | 处于历史什么位置？贵还是便宜？ |
| **成长性** | 营收CAGR(3年)、归母净利润CAGR(3年) | mx-finance-data | 增速是否在加速/减速？ |
| **财务健康** | 资产负债率、经营现金流/营收比、应收账款周转 | mx-finance-data | 是否有暴雷风险？ |
| **股东回报** | 股息率、分红率、回购计划 | mx-finance-data + 公告 | 是否重视股东回报？ |
| **机构覆盖** | 卖方覆盖家数、盈利预测调整方向 | mx-finance-data | 市场共识如何？ |

#### 3.2 技术面分析

| 子项 | 指标 | 数据源 | 分析要点 |
|------|------|--------|----------|
| **趋势** | MA5/10/20/60/120排列 | mx-finance-data K线 | 多头/空头/盘整？ |
| **动量** | RSI(14)、KDJ、MACD | mx-finance-data 技术指标 | 超买还是超卖？金叉死叉？ |
| **量价** | 成交量同比变化、量价配合 | mx-finance-data 行情 | 放量还是缩量？健康吗？ |
| **支撑/压力** | 前高/前低、筹码密集区 | mx-finance-data + K线 | 关键价位在哪？ |
| **形态** | K线组合、通道形态 | K线图形 | 头肩顶/W底/旗形？ |
| **相对强弱** | vs 所属行业指数、vs 沪深300 | 计算 | 跑赢板块还是跑输？ |

#### 3.3 资金面分析

| 子项 | 指标 | 数据源 | 分析要点 |
|------|------|--------|----------|
| **北向资金** | 近5日/20日/60日北向净流入 | mx-finance-data 北向数据 | 外资在买还是卖？ |
| **主力资金** | 超大单/大单净流入(近5日) | mx-finance-data 资金流向 | 机构在吸筹还是出货？ |
| **融资余额** | 融资余额变化 | mx-finance-data | 散户杠杆方向？ |
| **大宗交易** | 大宗交易折溢价、成交量 | 交易所公开数据 | 有无机构大宗接盘？ |
| **股东增减持** | 近3月董监高/大股东增减持 | 公司公告 | 内部人怎么看？ |

#### 3.4 消息面分析

| 子项 | 指标 | 数据源 | 分析要点 |
|------|------|--------|----------|
| **近期公告** | 业绩预告/快报/正式财报 | 公司公告/mx-finance-data | 超预期还是低于预期？ |
| **新闻舆情** | 正面/负面新闻热度 | web_search | 舆论环境如何？ |
| **政策影响** | 行业政策对公司影响 | web_search | 政策利好还是利空？ |
| **机构研报** | 近期研报观点变化 | mx-finance-data 研报 | 券商上调/下调评级？ |
| **业绩催化剂** | 未来3个月业绩催化事件 | 公司日历/行业日历 | 有什么值得期待的事件？ |

### Step 4: 综合诊断

将三层分析结果汇总，给出最终判断：

```
个股综合评分 = 宏观得分(10%) + 行业景气度得分(25%) + 个股四维得分(65%)

个股四维得分 = 基本面×35% + 技术面×25% + 资金面×20% + 消息面×20%
```

---

## 输出格式（严格模板 — 不可省略任何子项）

**标题行：**
```
# 📈 个股诊断：XXX（XXXXXX.SZ/SH/BJ）
**YYYY-MM-DD**
```

---

**一、综合诊断结果**

| 项目 | 内容 |
|------|------|
| **投资星级** | ★★★★★ / ★★★★☆ / ★★★☆☆ / ★★☆☆☆ / ★☆☆☆☆ |
| **投资操作建议** | **买入/增持/持有/减持/卖出** + 一句话核心逻辑 |
| **简要综合诊断结论** | 综合宏观+行业+个股三层面分析结论（3-5行） |

---

**二、宏观分析**

此处原样输出调用 `china-macro-

_meta.json

{
  "ownerId": "kn72npcamwgmtwbc1knw44e39h87g2tx",
  "slug": "a-share-stock-diagnosis",
  "version": "1.0.0",
  "publishedAt": 1779869657856
}

skill-card.md

## Description:

Provides top-down China A-share stock diagnosis by combining macro climate, industry diagnosis, and single-stock fundamental, technical, capital-flow, and news analysis into a structured investment report.

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

## Publisher:

[stevenge791](https://clawhub.ai/user/stevenge791)

### License/Terms of Use:

MIT-0

## Use Case:

Investors, analysts, and agent workflows use this skill to create informational A-share stock research reports that combine macro, industry, and company-level signals. It is suited for structured stock diagnosis, not for unreviewed personalized financial advice.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The report can include buy, hold, or sell suggestions that may be mistaken for personalized investment advice.

Mitigation: Treat the output as informational stock research and review assumptions, evidence, and risk notes before acting.

Risk: Market, macro, industry, and company data may be stale, incomplete, or inconsistent across upstream sources.

Mitigation: Verify current market data independently with trusted data sources before relying on the diagnosis.

Risk: The final report depends on upstream macro and industry skill outputs.

Mitigation: Use trusted upstream macro and industry skills and inspect their reports before incorporating them into decisions.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/stevenge791/skills/a-share-stock-diagnosis)

## Skill Output:

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

**Output Format:** [Markdown stock research report with tables, scores, star rating, operation suggestion, and risk notes]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [The report includes macro analysis, industry analysis, fundamentals, technicals, capital flows, news signals, weighted scoring, and key risks.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## 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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