Xpersona
agentCLAWHUBUnverified

Security Equity Research

AI tool generating China A-share equity research reports automating data collection, earnings review, valuation modeling (DCF/DDM/PE), ESG, and short-seller... Skill: Security Equity Research Owner: gechengling Summary: AI tool generating China A-share equity research reports automating data collection, earnings review, valuation modeling (DCF/DDM/PE), ESG, and short-seller... Tags: latest:3.0.3, security-equity-research:3.0.3 Version history: v3.0.3 | 2026-09-30T06:56:02.620Z | user v3.0.3: narrow triggers (SQP-1), add data-minimisation + execution-boundary section, refres

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.2k

Updated

Oct 11, 2026

Version

3.0.3

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
3.0.3release · observed Sep 30, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:security-equity-research
  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-security-equity-research/snapshot"

Documentation

CLAWHUB

76,906 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Securities Equity Research Report Generator
slug: security-equity-research
description: AI-powered equity research report generator for China A-share market — covers industry research, company coverage initiation, earnings analysis, valuation modeling (DCF/DDM/PE-band), and investment thesis synthesis. Built for China equity research analysts, buy-side analysts, and investment bank researchers. Updated 2026 with latest ESG integration requirements, short-seller report response templates, and cross-sector comparison frameworks. Keywords: equity research, A-share research, investment report, short-seller defense, ESG integration, valuation model, China stock analysis, 研报生成, 行业研究, 个股覆盖, 估值建模, 投研报告, 行研, 财报分析, 盈利预测, 目标价, 评级, 深度报告, 事件点评, 新股分析.
version: "3.0.3"
---

# Securities Equity Research Report Generator / 证券投研报告生成器

> **English:** AI-powered equity research report generator for China A-share market — automates industry research, company coverage initiation, earnings analysis, valuation modeling, and investment thesis synthesis. Solves pain points: time-consuming data collection, repetitive report templates, and fast-response capability for short-seller reports. Built for equity research analysts and buy-side researchers.
>
> **中文:** 证券投研报告生成器——覆盖行业研究、公司首次覆盖、业绩点评、估值建模（DCF/DDM/PE区间）、投资逻辑提炼的全流程AI助手。解决痛点：数据收集耗时、报告模板重复、快速响应做空报告需求。适用：中国A股研究员、买方分析师、投资银行研究人员。


---

## 数据最小化声明与执行边界 / Data Minimisation & Execution Boundary

**数据最小化前置声明：** 使用本技能时，请只提供完成当次分析所必需的公开信息——公开财报科目、公开公告要点、行业公开统计数据、已脱敏的持仓与组合权重。**不要**粘贴未公开财务数据、内幕信息、客户名单、个人账户持仓明细或带身份信息的尽调材料。如需引用第三方数据，请注明来源与取数日期，不要求上传原始数据文件。

**代码块性质与执行边界**

| 内容 | 性质 | 谁来执行 |
|------|------|---------|
| `dcf_valuation()` 及使用示例 | 现金流折现的口径与公式示意，说明终值占比、敏感性应如何计算 | 由研究员在自己的建模环境（Excel/Python）中按口径复现；技能不运行、不取数 |
| `ddm_valuation()` | 两阶段股利贴现的公式表达，用于统一团队算法 | 同上，由研究员自行填入参数运行 |
| `pe_band_analysis()` | PE 历史分位统计的口径说明 | 由研究员在自有行情终端中取数后计算 |
| 研报骨架、做空回应模板 | Markdown 文本模板，需人工填写内容 | 由研究员在机构模板系统中填写并走质控流程 |

本技能未配置任何工具调用权限，不执行代码、不读写文件、不访问行情或数据库接口，也不生成可直接对外发布的成品研报。文中代码块均为公式与口径的教学示意；所有输出须经具备资质的分析师复核、并由机构质控流程审批后方可使用。

---


### 证券监管最新动态 [2026-09-30更新]

| 动态类型 | 内容摘要 | 影响范围 | 研报侧应对动作 | 责任岗 | 优先级 |
|---------|---------|---------|--------------|-------|-------|
| 证券监管 | 2026年Q1：证监会持续强化上市公司信息披露质量监管 | 研究报告模板需纳入信披合规和量化冲击分析 | 研报增设"信披质量"小节，标注公告编号 | 研究 | 高 |
| 证券监管 | 业绩预告披露质量被重点关注，研究需加强信披合规分析 | 研究报告模板需纳入信披合规和量化冲击分析 | 业绩点评须区分"预告口径"与"实际口径" | 研究 | 高 |
| 证券监管 | A股量化资金占比30%-40%，研究报告需关注量化冲击因素 | 研究报告模板需纳入信披合规和量化冲击分析 | 增加"交易结构"小节，说明波动归因 | 研究 | 中 |
| 研报执业 | 研报执业规范持续强化，观点依据与数据来源须可追溯 | 深度报告、事件点评、盈利预测 | 每个关键数据标注来源与取数日期 | 研究 | 高 |
| 适当性与传播 | 研报公开传播需注意目标价、评级的表述边界 | 对客传播的研报摘要与路演材料 | 摘要保留评级定义与免责声明 | 合规 | 高 |
| 市值管理 | 上市公司市值管理行为的信息披露要求趋严 | 涉及股东增减持、回购的点评 | 点评须同时列示公告原文要点 | 研究 | 中 |
| 程序化交易 | 程序化交易报告与异常交易监控要求细化 | 与量化资金相关的流动性与波动分析 | 在流动性分析中纳入程序化交易因素 | 研究 | 中 |
| ESG披露 | 可持续披露规则推进，ESG信息在估值中的权重上升 | ESG整合、行业比较 | 估值与比较框架中单列ESG评分维度 | 研究 | 中 |
| 研报执业 | 2026年9月下旬：研究报告署名与发布流程的内部质控留痕要求进一步细化 | 深度报告、点评、路演材料 | 研报定稿前留存质控复核记录

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "security-equity-research",
  "version": "3.0.3",
  "publishedAt": 1790751362620
}

skill-card.md

## Description:

Provides templates and valuation frameworks for China A-share equity research, including company coverage, earnings reviews, industry analysis, ESG considerations, and short-seller responses.

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

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

Equity research analysts and investment researchers use this skill to draft China A-share research outlines, structure valuation assumptions, and prepare materials for analyst and compliance review.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Stale or unsupported market data, valuation assumptions, or regulatory claims may mislead readers.

Mitigation: Verify market, regulatory, and valuation inputs independently; document sources and observation dates before using conclusions.

Risk: Research prompts may expose nonpublic financial information, customer details, personal account data, or insider material.

Mitigation: Provide only public or appropriately sanitized inputs; omit confidential and personally identifiable information.

Risk: Template language may be mistaken for publication-ready investment research or live data analysis.

Mitigation: Treat examples as illustrative and route all publishable research through qualified analyst and compliance review.

## Reference(s):

- [Security Equity Research on ClawHub](https://clawhub.ai/gechengling/skills/security-equity-research)

## Skill Output:

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

**Output Format:** [Markdown research templates, tables, worked valuation examples, and illustrative Python formulas]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Illustrative content only; no live market-data access or executable workflow. Requires independent data checks and qualified analyst and compliance review before publication.]

## Skill Version(s):

3.0.3 (source: release evidence and frontmatter)

## 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.

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Record generated Oct 11, 2026.

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