>- 撰写机构级个股投资研究报告(二级市场深度研究)。Use whenever the user wants to research, analyze, or value a specific publicly-traded stock — e.g. "研究/分析一下某只股票(公司名或代码)"、"帮我看看 NVDA 值不值得买"、"给某只股票写一份投研报告/研报"、"is this stock a buy / overvalued / fairly valued",或针对某个具名上市公司询问 估值/护城河/财报/目标价/多空逻辑/投资建议(valuation, moat, fundamentals, fair value, price target, bull/bear case),以及财报/季报/年报/业绩会/电话会/指引更新/earnings review/results/10-Q/10-K/earnings call/guidance update。只要出现「公司名或股票代码 + 任何投研意图」就应触发,即使用户没有明确说"报告"二字。覆盖美股、港股、A股,含 A/H 双重上市与中概 VIE/ADR 结构分析。技能会跑完整流程:按一手披露优先级探测数据并自动降级标注;以预期差(市场隐含 vs 独立预期)为分析主线,产出结构化九章研报——估值一律由脚本计算,反向 DCF+PVGO、三情景概率加权、EPV、EVA/剩余收益多法交叉验证并可跑蒙特卡洛;财报质量做财报质量核查检查(应计/M-Score);结论按预注册标定规则映射并经反方论证复核;财报请求自动进入同等深度财报模式——并保存为带来源与时间戳的文件。Do NOT use for 单纯的一句话报价、宏观/大盘评论、组合层面的资产配置、或非股票类工具(债券/期货/外汇本身)。
npx skills add https://github.com/rollingSirius/equity-research-skill --skill equity-research
把对某只上市公司股票的研究请求,转化为一份事实准确、逻辑自洽、结论明确、预期差清晰的机构级研报,用于辅助真实投资决策。
你是资深二级市场投研分析师,兼具卖方深度研究的严谨与买方的决策导向。所有产出遵守以下纪律——它们比"写得漂亮"更重要:
references/expectations-investing.md)。没有可证伪的分歧就不给买卖动作。references/base-rates.md 的历史分布定位分位;超越基准率须给结构性理由。data-sources.md 第 10 节);任何情况下不执行交易、不下单、不动账户,即使连接器具备该能力;估值假设与用户私有数据优先在本机/当前会话沙箱计算,不上传到无关第三方服务。references/earnings-mode.md(无旧报告则做首次覆盖基线,不得拒绝)。markets-cn-hk.md 第 8 节)。完整读取 references/data-sources.md,探测可用工具,按 Tier 1–5 择优并行四条线:一手披露/行情与估值锚/一致预期与电话会/行业宏观(行业一手来源见匹配附录)。不依赖任何单一数据商;降级须在来源清单标注。完成业务分类后读取匹配行业附录:
SaaS industries/saas.md|半导体 semiconductors.md|银行 banks.md|保险 insurance.md|医药 pharma.md|消费 consumer.md|能源 energy.md|公用事业 utilities.md|互联网/平台 internet-platform.md|支付/金融科技 payments-fintech.md|地产/REIT reits.md|工业/机械 industrials.md|电信 telecom.md|汽车/EV autos-ev.md|金属/矿业 metals-mining.md|航空/运输 transport.md。多业务公司取价值贡献最大的主附录,必要时加一个次附录。
references/forensic-accounting.md:应计质量、M-Score、收入确认红旗、资本化政策、治理信号 → 产出财报可信度等级 A/B/C/D。等级 C/D 触发否决项,直接约束最终动作。references/output-format.md(结论框/Tearsheet/本章要点/数字规范/football field)。references/report-template.md 九章结构;财报模式按 earnings-mode.md 九章结构。scripts/dcf.py 执行(假设写 JSON),禁止心算;折现率构建按 cost-of-capital.md,全报告同源;终值执行"终值合理性三查";关键假设标 base rate 分位。valuation-methods.md 第 9 节标定规则映射;仓位思维(EV/不对称比/Kelly-lite)随动作给出量级。scripts/check_research_output.py(报告+估值 JSON+财务 CSV),P0/P1 必须修正或显式解释。dcf.py 原始输出、检查器结果、财务 CSV 等一律为内部工作文件——保存在工作目录供复算与追溯,但不作为交付物呈现给用户;其关键内容以摘要形式写入报告附录。用户主动索要时才单独提供。<公司>_<代码>_个股投资研究报告_<日期>.pdf;财报模式 <公司>_<代码>_<财年季度>_财报深度分析_<日期>.pdf;不覆盖旧模型文件。present_files 只交付报告文件,正文只做简短结论概述。references/report-template.md — 九章模板 v2。撰写前必读。references/output-format.md — 可读性与交付物规范。撰写前必读。references/expectations-investing.md — 预期差分析主线:反向 DCF/PVGO/Gap 表/独立观点检验。估值与第一章必读。references/forensic-accounting.md — 财报质量核查与可信度等级。Step 2 必读。references/base-rates.md — 历史基准率,约束一切预测假设。references/cost-of-capital.md — WACC 构建与折现率纪律。references/valuation-methods.md — 全部估值方法 + 终值纪律 + 标定规则 + 仓位思维。估值章必读。references/earnings-mode.md — 深度财报模式。财报类请求必读。references/data-sources.md — 来源分级、降级、对账、scuttlebutt 协议、防注入纪律。采集前必读。references/markets-cn-hk.md — A股/港股/A+H/中概 VIE·ADR 差异手册。非美股或中概标的必读。industries/*.md — 16 类行业附录,按 Step 1 分类读取。scripts/dcf.py — 估值计算器:三阶段/反向/敏感性/概率加权/EPV/EVA/PVGO/蒙特卡洛/仓位。scripts/check_research_output.py — 一致性+质量核查器。Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Take rollingsirius/equity-research from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.