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面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-17T11:18:27.865Z | user\n\nPublish 1.0.0\n\nArchive index:\n\nArchive v1.0.0: 4 files, 7286 bytes\n\nFiles: scripts/get_data.py (10994b), skill-card.md (2444b), SKILL.md (4989b), _meta.json (133b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: fund-diagnosis\ndescription: 面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。\nmetadata:\n  {\n    \"openclaw\": {\n      \"requires\": {\n        \"env\":[\"EM_API_KEY\"]\n      }\n    }\n  }\n---\n\n# 基金综合诊断\n\n通过**自然语言问句**对单只基金进行综合分析，返回 Markdown 诊断报告，适用场景包括：\n- **基金整体判断（收益表现 + 风险特征 + 持仓结构）**\n- **持有决策参考（继续持有/观望/调整仓位）**\n- **波动市场中的基金风险排查**\n- **用户泛化问法的一站式诊断回答**\n\n## 密钥来源与安全说明\n\n- 本技能仅使用一个环境变量：`EM_API_KEY`。\n- `EM_API_KEY` 由东方财富妙想服务（`https://ai.eastmoney.com/mxClaw`）签发，用于接口鉴权。\n- 在提供密钥前，请先确认密钥来源、可用范围、有效期及是否支持重置/撤销。\n- 禁止在代码、提示词、日志或输出文件中硬编码/明文暴露密钥。\n\n## 功能范围\n\n### 基础诊断能力\n- 输入自然语言问句，调用诊基接口生成结构化结论\n- 每次仅处理**一只**基金\n- 返回可读 Markdown 报告（优先提取 `data.displayData`）\n- 支持将结果保存为本地 `.md` 文件，便于复盘追踪\n\n### 触发规则（何时使用本技能）\n- 用户问题是**笼统/概括性**诊断：如“这只基金怎么样”“值得继续持有吗”“风险大吗”\n- 问句中未明确要求具体指标计算、回测建模或程序化导出\n- 若上下文已明确基金实体，用户后续使用“它/这只基金”等代词继续提问，也应触发\n\n### 不触发规则（何时不要使用本技能）\n- 用户要求 Python 回测、组合优化、量化建模等高级计算任务\n- 用户明确要求导出净值明细、构建CSV或做数据工程处理\n- 用户要求多只基金横向对比（应走多标的分析类能力）\n\n### 触发示例\n\n| 触发（泛化诊断） | 不触发（建模/工程） |\n|---|---|\n| 华夏成长混合基金怎么样？ | 帮我用Python回测华夏成长混合基金策略 |\n| 这只基金适合长期持有吗？ | 帮我建一个基金组合优化模型 |\n| 它近期风险大吗？ | 把这只基金净值数据导出为CSV |\n\n## 前提条件\n\n### 1. 注册东方财富妙想账号\n\n访问 https://ai.eastmoney.com/mxClaw 注册账号并获取 API Key。\n\n### 2. 配置 Token\n\n```bash\n# macOS 添加到 ~/.zshrc，Linux 添加到 ~/.bashrc\nexport EM_API_KEY=\"your_api_key_here\"\n```\n\n然后根据系统执行对应的命令：\n\n**macOS：**\n```bash\nsource ~/.zshrc\n```\n\n**Linux：**\n```bash\nsource ~/.bashrc\n```\n\n## 快速开始\n\n### 1. 命令行调用\n\n```bash\npython3 {baseDir}/scripts/get_data.py --query \"华夏成长混合基金\"\n```\n\n**输出示例**\n```text\nSaved: /path/to/workspace/fund_diagnosis/fund_diagnosis_90bf169c.md\n（随后输出 Markdown 诊断内容）\n```\n\n**参数说明：**\n\n| 参数 | 说明 | 必填 |\n|---|---|---|\n| `--query` | 用户原始自然语言问句 | ✅（`--query` 或 stdin 二选一） |\n| `--no-save` | 仅输出结果，不写入本地文件 | 否 |\n\n### 2. 代码调用\n\n```python\nimport asyncio\nfrom pathlib import Path\nfrom scripts.get_data import diagnose_fund\n\nasync def main():\n    result = await diagnose_fund(\n        question=\"华夏成长混合基金\",\n        output_dir=Path(\"workspace/fund_diagnosis\"),\n        save_to_file=True,\n    )\n    if \"error\" in result:\n        print(result[\"error\"])\n    else:\n        print(result[\"content\"])\n        if result.get(\"output_path\"):\n            print(\"已保存至:\", result[\"output_path\"])\n\nasyncio.run(main())\n```\n## 输出规范\n通过脚本或工具拿到诊股结果后，**对用户的可见回复必须以接口返回的 Markdown 正文为主体**，避免模型二次转述。仅当接口/脚本明确返回 `error`、或正文为空时，才用简短文字说明失败原因；**禁止**在失败时杜撰报告内容。\n\n## 常见问题\n\n**错误：EM_API_KEY is required.**  \n→ 需先配置 `EM_API_KEY`，请联系官网获取并手动配置。\n\n**为什么需要保持原始问句？**  \n→ 诊基接口依赖用户自然语言语义，建议避免改写导致意图偏移。\n\n**如何只看输出，不落盘？**\n```bash\npython3 -m scripts.get_data --query \"华夏成长混合基金\" --no-save\n```\n\n## 合规说明\n\n- 诊断结果仅供参考，不构成投资建议，输出时应附风险提示。\n- 禁止在代码或提示词中硬编码账号 ID、会话 ID 或 token。\n- 环境变量按敏感信息处理，不在日志或回复中泄露。\n- 接口失败时不得编造结论，应返回明确错误或不确定性说明。\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7b4ptpdag877t9kmq8axyja182taab\",\n  \"slug\": \"fund-diagnosis\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776424707865\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nProvides single-fund diagnostic analysis for public mutual fund questions and returns a structured Markdown report covering overall judgment, holding considerations, and risk and return characteristics.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[financial-ai-analyst](https://clawhub.ai/user/financial-ai-analyst)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and financial-analysis agents use this skill to answer broad questions about a single public mutual fund, such as whether it is suitable to continue holding and what its risk and return profile looks like. It is not intended for multi-fund comparison, backtesting, portfolio optimization, or quantitative modeling workflows.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Fund questions and the EM_API_KEY are sent to Eastmoney's API.\n\nMitigation: Install only when that data transfer is acceptable, keep the API key revocable, and keep the key out of logs, prompts, and shared files.\n\nRisk: Returned fund diagnostics may be mistaken for investment advice.\n\nMitigation: Present the report as reference-only analysis and include a risk notice that it does not constitute investment advice.\n\nRisk: Markdown reports may be saved locally by default and retained longer than intended.\n\nMitigation: Use --no-save for sensitive analyses or manage the generated report files according to the user's retention needs.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/financial-ai-analyst/skills/fund-diagnosis)\n- [Eastmoney Miaoxiang service](https://ai.eastmoney.com/mxClaw)\n- [Eastmoney fund-analysis API endpoint](https://ai-saas.eastmoney.com/proxy/)\n\n## Skill Output:\n\n**Output Type(s):** [Analysis, Markdown, Files, Shell commands, Guidance]\n\n**Output Format:** [Markdown report, optionally saved as a local .md file]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The visible answer should use the API-returned Markdown as the primary content and should not invent a report when the API returns an error or empty body.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers 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.","readmeExcerpt":"Skill: fund-diagnosis Owner: financial-ai-analyst Summary: 面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-17T11:18:27.865Z | user Publish 1.0.0 Archive index: Archive v1.0.0: 4 files, 7286 bytes Files: scripts/get_data.py (10994b), skill-card.md (2444b), SKILL.md (4989b), _","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"# macOS 添加到 ~/.zshrc，Linux 添加到 ~/.bashrc\nexport EM_API_KEY=\"your_api_key_here\""},{"language":"bash","snippet":"source ~/.zshrc"},{"language":"bash","snippet":"source ~/.bashrc"},{"language":"bash","snippet":"python3 {baseDir}/scripts/get_data.py --query \"华夏成长混合基金\""},{"language":"text","snippet":"Saved: /path/to/workspace/fund_diagnosis/fund_diagnosis_90bf169c.md\n（随后输出 Markdown 诊断内容）"},{"language":"python","snippet":"import asyncio\nfrom pathlib import Path\nfrom scripts.get_data import diagnose_fund\n\nasync def main():\n    result = await diagnose_fund(\n        question=\"华夏成长混合基金\",\n        output_dir=Path(\"workspace/fund_diagnosis\"),\n        save_to_file=True,\n    )\n    if \"error\" in result:\n        print(result[\"error\"])\n    else:\n        print(result[\"content\"])\n        if result.get(\"output_path\"):\n            print(\"已保存至:\", result[\"output_path\"])\n\nasyncio.run(main())"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: fund-diagnosis\ndescription: 面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。\nmetadata:\n  {\n    \"openclaw\": {\n      \"requires\": {\n        \"env\":[\"EM_API_KEY\"]\n      }\n    }\n  }\n---\n\n# 基金综合诊断\n\n通过**自然语言问句**对单只基金进行综合分析，返回 Markdown 诊断报告，适用场景包括：\n- **基金整体判断（收益表现 + 风险特征 + 持仓结构）**\n- **持有决策参考（继续持有/观望/调整仓位）**\n- **波动市场中的基金风险排查**\n- **用户泛化问法的一站式诊断回答**\n\n## 密钥来源与安全说明\n\n- 本技能仅使用一个环境变量：`EM_API_KEY`。\n- `EM_API_KEY` 由东方财富妙想服务（`https://ai.eastmoney.com/mxClaw`）签发，用于接口鉴权。\n- 在提供密钥前，请先确认密钥来源、可用范围、有效期及是否支持重置/撤销。\n- 禁止在代码、提示词、日志或输出文件中硬编码/明文暴露密钥。\n\n## 功能范围\n\n### 基础诊断能力\n- 输入自然语言问句，调用诊基接口生成结构化结论\n- 每次仅处理**一只**基金\n- 返回可读 Markdown 报告（优先提取 `data.displayData`）\n- 支持将结果保存为本地 `.md` 文件，便于复盘追踪\n\n### 触发规则（何时使用本技能）\n- 用户问题是**笼统/概括性**诊断：如“这只基金怎么样”“值得继续持有吗”“风险大吗”\n- 问句中未明确要求具体指标计算、回测建模或程序化导出\n- 若上下文已明确基金实体，用户后续使用“它/这只基金”等代词继续提问，也应触发\n\n### 不触发规则（何时不要使用本技能）\n- 用户要求 Python 回测、组合优化、量化建模等高级计算任务\n- 用户明确要求导出净值明细、构建CSV或做数据工程处理\n- 用户要求多只基金横向对比（应走多标的分析类能力）\n\n### 触发示例\n\n| 触发（泛化诊断） | 不触发（建模/工程） |\n|---|---|\n| 华夏成长混合基金怎么样？ | 帮我用Python回测华夏成长混合基金策略 |\n| 这只基金适合长期持有吗？ | 帮我建一个基金组合优化模型 |\n| 它近期风险大吗？ | 把这只基金净值数据导出为CSV |\n\n## 前提条件\n\n### 1. 注册东方财富妙想账号\n\n访问 https://ai.eastmoney.com/mxClaw 注册账号并获取 API Key。\n\n### 2. 配置 Token\n\n```bash\n# macOS 添加到 ~/.zshrc，Linux 添加到 ~/.bashrc\nexport EM_API_KEY=\"your_api_key_here\"\n```\n\n然后根据系统执行对应的命令：\n\n**macOS：**\n```bash\nsource ~/.zshrc\n```\n\n**Linux：**\n```bash\nsource ~/.bashrc\n```\n\n## 快速开始\n\n### 1. 命令行调用\n\n```bash\npython3 {baseDir}/scripts/get_data.py --query \"华夏成长混合基金\"\n```\n\n**输出示例**\n```text\nSaved: /path/to/workspace/fund_diagnosis/fund_diagnosis_90bf169c.md\n（随后输出 Markdown 诊断内容）\n```\n\n**参数说明：**\n\n| 参数 | 说明 | 必填 |\n|---|---|---|\n| `--query` | 用户原始自然语言问句 | ✅（`--query` 或 stdin 二选一） |\n| `--no-save` | 仅输出结果，不写入本地文件 | 否 |\n\n### 2. 代码调用\n\n```python\nimport asyncio\nfrom pathlib import Path\nfrom scripts.get_data import diagnose_fund\n\nasync def main():\n    result = await diagnose_fund(\n        question=\"华夏成长混合基金\",\n        output_dir=Path(\"workspace/fund_diagnosis\"),\n        save_to_file=True,\n    )\n    if \"error\" in result:\n        print(result[\"error\"])\n    else:\n        print(result[\"content\"])\n        if result.get(\"output_path\"):\n            print(\"已保存至:\", result[\"output_path\"])\n\nasyncio.run(main())\n```\n## 输出规范\n通过脚本或工具拿到诊股结果后，**对用户的可见回复必须以接口返回的 Markdown 正文为主体**，避免模型二次转述。仅当接口/脚本明确返回 `error`、或正文为空时，才用简短文字说明失败原因；**禁止**在失败时杜撰报告内容。\n\n## 常见问题\n\n**错误：EM_API_KEY is required.**  \n→ 需先配置 `EM_API_KEY`，请联系官网获取并手动配置。\n\n**为什么需要保持原始问句？**  \n→ 诊基接口依赖用户自然语言语义，建议避免改写导致意图偏移。\n\n**如何只看输出，不落盘？**\n```bash\npython3 -m scripts.get_data --query \"华夏成长混合基金\" --no-save\n```\n\n## 合规说明\n\n- 诊断结果仅供参考，不构成投资建议，输出时应附风险提示。\n- 禁止在代码或提示词中硬编码账号 ID、会话 ID 或 token。\n- 环境变量按敏感信息处理，不在日志或回复中泄露。\n- 接口失败时不得编造结论，应返回明确错误或不确定性说明。"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7b4ptpdag877t9kmq8axyja182taab\",\n  \"slug\": \"fund-diagnosis\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776424707865\n}"},{"path":"skill-card.md","content":"## Description:\n\nProvides single-fund diagnostic analysis for public mutual fund questions and returns a structured Markdown report covering overall judgment, holding considerations, and risk and return characteristics.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[financial-ai-analyst](https://clawhub.ai/user/financial-ai-analyst)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and financial-analysis agents use this skill to answer broad questions about a single public mutual fund, such as whether it is suitable to continue holding and what its risk and return profile looks like. It is not intended for multi-fund comparison, backtesting, portfolio optimization, or quantitative modeling workflows.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Fund questions and the EM_API_KEY are sent to Eastmoney's API.\n\nMitigation: Install only when that data transfer is acceptable, keep the API key revocable, and keep the key out of logs, prompts, and shared files.\n\nRisk: Returned fund diagnostics may be mistaken for investment advice.\n\nMitigation: Present the report as reference-only analysis and include a risk notice that it does not constitute investment advice.\n\nRisk: Markdown reports may be saved locally by default and retained longer than intended.\n\nMitigation: Use --no-save for sensitive analyses or manage the generated report files according to the user's retention needs.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/financial-ai-analyst/skills/fund-diagnosis)\n- [Eastmoney Miaoxiang service](https://ai.eastmoney.com/mxClaw)\n- [Eastmoney fund-analysis API endpoint](https://ai-saas.eastmoney.com/proxy/)\n\n## Skill Output:\n\n**Output Type(s):** [Analysis, Markdown, Files, Shell commands, Guidance]\n\n**Output Format:** [Markdown report, optionally saved as a local .md file]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The visible answer should use the API-returned Markdown as the primary content and should not invent a report when the API returns an error or empty body.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers 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."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。 Skill: fund-diagnosis Owner: financial-ai-analyst Summary: 面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-17T11:18:27.865Z | user Publish 1.0.0 Archive index: Archive v1.0.0: 4 files, 7286 bytes Files: scripts/get_data.py (10994b), skill-card.md (2444b), SKILL.md (4989b), _","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":729,"uniquenessScore":60,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:44:44.364Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-09T18:44:44.364Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T05:43:26.290Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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