--- name: fund-holding-companion-content-assistant description: | 当用户需要在基金场景中完成客户陪伴内容助手相关任务时使用本技能，适用于持仓陪伴及相关分析、生成、审阅请求。 用户提到「客户陪伴」「持仓陪伴」「陪伴内容」或需要输出结构化中文结论、模板和配套脚本时，应优先触发。 --- # 技能说明 本技能用于支持基金业务中的持仓陪伴工作，重点解决「围绕客户持有基金的市场表现、回撤波动和长期配置逻辑，生成中文陪伴内容草稿。」这类任务。输出默认使用中文，要求结构清楚、证据边界明确，并能被投研、产品、销售、投顾或合规团队直接复用。 # 本任务重点关注的问题 - 客户此时最焦虑或最想知道的是什么 - 当前波动应该怎样解释才不过度刺激情绪 - 哪些内容适合持续陪伴 - 长期逻辑该怎么讲才不越界 # 适用范围 - 周度或月度陪伴内容 - 回撤期间安抚与解释草稿 - 长期持有逻辑说明 - 客户触达内容结构设计 # 不适用范围 - 把陪伴内容写成收益保证 - 掩盖回撤和风险信息 - 未了解客户风险等级却给出配置建议 # 输入要求 优先输入包括： - 持有产品和市场表现 - 客户画像与风险等级 - 近期波动原因 - 希望触达的场景 可选增强输入包括： - 历史触达记录 - 客户问题反馈 - 渠道模板 当输入不完整时，必须先列出缺失材料，再基于现有信息输出阶段性判断，不得编造未确认事实。 # 核心分析框架 ## 一、先识别客户和沟通场景 - 明确渠道类型、客群层级、触达阶段和沟通目标。 - 区分首轮触达、异议处理、培训支持和持续陪伴。 - 让内容和场景匹配，而不是一套话术到处用。 ## 二、先搭证据，再写表达 - 卖点、风险、异议应答都要有事实支撑。 - 先明确什么能说，什么不能说。 - 对敏感问题要预留升级处理空间。 ## 三、让一线真正能用 - 输出要可复述、可拆分、可培训。 - 复杂投研语言要翻译，但不能失真。 - 风险揭示不能被卖点掩盖。 # 推荐工作流 1. 先识别目标渠道、目标客群、沟通场景和常见异议。 2. 整理产品事实、持仓解释、市场背景和渠道规则，明确哪些内容可说、哪些不能说。 3. 围绕销售支持、FAQ、路演或陪伴内容搭建结构化输出。 4. 把复杂投研信息翻译成可沟通、可复述、可审阅的中文材料。 5. 输出时保留合规提醒、敏感表述边界和升级处理建议。 # 关键判断视角 - 客群画像、触达场景与沟通目标 - 产品卖点、证据链与高频异议 - 渠道问答、陪伴内容和后续转化动作 - 合规敏感词、披露要求和升级节点 # 输出要求 建议输出至少覆盖以下模块： - 陪伴内容摘要 - 事实解释 - 情绪安抚表述 - 后续关注点 - 敏感边界提示 若任务涉及对客材料、话术或解释文案，必须同时输出「不能直接对外使用的前提说明」或「需正式复核」的提醒。 # 输出约束 - 不得写收益承诺、保本承诺或确定性修复表达。 - 不得弱化回撤、波动和流动性风险。 - 任何对客版本都应保留复核边界。 # 质量检查清单 - 是否先界定了任务边界、时间窗口、样本范围和使用场景。 - 是否把已确认事实、解释性判断和待核验事项分开表达。 - 是否给出了证据链，而不是只写结论标签。 - 是否保留了风险提示、限制说明和后续动作。 - 若内容可能进入对客或外部场景，是否保留了复核边界。 - 当前 skill 特别要盯住的判断视角： - 客群画像、触达场景与沟通目标 - 产品卖点、证据链与高频异议 - 渠道问答、陪伴内容和后续转化动作 - 合规敏感词、披露要求和升级节点 # 可读取资源 - references/input-schema.md：输入字段、资料分层和补数要求。 - references/output-schema.md：输出结构、段落安排和表达边界。 - references/review-checklist.md：交付前核对要点。 - assets/templates/companion_content_template.md：默认输出模板。 - assets/templates/data_request_template.md：缺失材料补充模板。 # 可调用脚本 - scripts/generate_companion_content.py - scripts/render_companion_content.py --- Source attribution: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/fund-holding-companion-content-assistant/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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