No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by rollingSirius · Agent Tool · ★ 245
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is equity-research-skill safe to install? View the security audit →
Equity Research Skill 个股投研报告技能 一个可用于 Claude Code / Claude Desktop (Cowork) / Codex 及其他 AI Agent 工具的 Agent Skill：把"研究一下某只股票"这类请求，转化为一份事实准确、来源可溯、结论明确的机构级九章投研报告（中文输出）。 An Agent Skill that turns "research this stock for me" into an institutional-grade, nine-chapter equity research report (written in Chinese) — with sourced data, timestamps, and multi-method valuation cross-checks. 它能做什么 What it does 多市场覆盖：美股 / 港股 / A股，A/H 双重上市标的默认做溢价对比并分市场给结论。US, HK and China A-shares, with an A/H premium module for dual-listed names. 五步工作流：确认标的 → 探测可用数据源并并行采集（行情 / Morningstar / 财报与卖方观点，工具缺失自动降级并标注）→ 数据对账与时间戳 → 按九章模板撰写 → 估值交叉验证并交付文件。 九章报告结构：一页速览、业务详情、竞争与护城河、管理层与治理、财务分析、多方法估值、分析师观点汇总、新闻与催化剂、投资结论。 估值纪律：至少三种方法交叉验证——反向 DCF（证伪现价）+ 三情景概率加权 DCF + 相对估值/SOTP；所有 DCF 计算由 执行，禁止心算，假设以 JSON 留档。 结论可复现：估值标签与买卖动作按预注册标定规则映射产出（含动作矩阵与治理否决项），同一组数字不会两次给出不同结论。 研究纪律：事实与判断分离、每个关键数据标注来源与时间、冲突数据显式对账、缺失数据如实标注"未获取到"。 文件结构 Structure equity-research-skill/ ├── SKILL.md # 技能主文件（触发条件 + 工作流程） ├── references/ │ ├── report-template.md # 九章报告模板与表格骨架 │ ├── data-sources.md # 取数手册：工具探测与降级表、行情 / Morningstar / SEC / 分析师数据与对账规则 │ ├── valuation-methods.md # 估值方法：相对估值、正向/反向 DCF、情景加权、SOTP + 结论标定规则 │ └── markets-cn-hk.md # A股/港股/A+H...
| Stars | 245 |
| Forks | 39 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.8886445208738/100 |
| Last Updated | 2026-08-12 |
| Created | 2026-07-14 |
| Platforms | python |
| Est. Tokens | ~14k |
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equity-research-skill is Possibly the deepest AI equity-research skill: nine-chapter single-stock deep dives and earnings deep-dives, with scripted DCF/EPV/EVA and reproducible valuation. Covers US, HK and A-shares. Docs in E. It is categorized as a Agent Tool with 245 GitHub stars.
equity-research-skill is primarily written in Python. It covers topics such as agent-skills, ai, dcf.
You can find installation instructions and usage details in the equity-research-skill GitHub repository at github.com/rollingSirius/equity-research-skill. The project has 245 stars and 39 forks, indicating an active community.
equity-research-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to equity-research-skill on Agent Skills Hub include agentii-investment-intelligence, book2skills, excel-analyst-pro-skill-md. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: