学习切入点
这个项目的市场分析 skill 更强调工具化执行:调用 yfinance、估值模型、期权 payoff、相关性、流动性和 earnings 数据,把研究问题转成可运行的数据分析步骤。它适合补充 Anthropic 官方 skill 在实时数据和可计算分析上的不足。
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工作流分析
- 来源与路径:来自
community-himself65-finance-skills,上游路径plugins/market-analysis/skills/earnings-recap/SKILL.md,本地路径12-financial-ai-skills/upstream-snapshots/community-himself65-finance-skills/plugins/market-analysis/skills/earnings-recap/SKILL.md。 - 原始定位:官方/上游描述:Generate a post-earnings analysis for any stock using Yahoo Finance data. Use when the user wants to review what happened after earnings, understand beat/miss results, see stock reaction, or get an earnings recap. Triggers: "AAPL earnings recap", "how did TSLA earnings go", "MSFT earnings results", "did NVDA beat earnings", "post-earnings analysis", "earnings surprise", "what happened with GOOGL earnings", "earnings reaction", "stock moved after earnings", "EPS beat or miss", "revenue beat or miss", "quarterly results for", "how were earnings", "AMZN reported last night", "earnings call recap", or any request about a company's recent earnings outcome. Use this skill when the user references a past earnings event, even if they just say "AAPL reported" or "how did they do".
- 金融业务理解:这个项目的市场分析 skill 更强调工具化执行:调用 yfinance、估值模型、期权 payoff、相关性、流动性和 earnings 数据,把研究问题转成可运行的数据分析步骤。它适合补充 Anthropic 官方 skill 在实时数据和可计算分析上的不足。
- AI 工作流价值:它补充了金融 agent 从“会写”到“能查、能算、能画、能跟踪”的能力。对学习者而言,重点是理解它依赖哪些外部工具、数据字段、模型假设和运行环境。
- 可迁移应用方向:可用于做“证券分析小实验”:用公开行情和财务数据跑估值、相关性、流动性或期权收益结构,再把结果写成中文学习笔记。
- 风险与治理边界:治理重点是数据可得性、默认假设和非投资建议边界。公开数据可能延迟或缺字段,估值输出必须展示假设、敏感性和置信度。
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01 Skill 上游文件https://github.com/himself65/finance-skills/blob/294dc86f2ca2/plugins/market-analysis/skills/earnings-recap/SKILL.mdFSK-080 · 查看仓库原文12-financial-ai-skills/community-finance-ai-skills-analysis-02.md原始来源与整理依据
文字分析来自内容源;业务分类、技术标签由 SeekingDAO 按规则整理。披露效果保留原有时间、范围和预测口径,未作独立审计。开源项目与 Skill 的当前许可、兼容性及依赖请以其上游为准。