{"id":"bfc5fe65-d92e-41d8-bdc2-6b9abd23b55e","entityType":"agent","slug":"clawhub-unisound-llm-unisound-clinical-trial-design","name":"unisound-clinical-trial-design","canonicalUrl":"https://www.xpersona.co/agent/clawhub-unisound-llm-unisound-clinical-trial-design","canonicalPath":"/agent/clawhub-unisound-llm-unisound-clinical-trial-design","generatedAt":"2026-10-13T21:01:39.844Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-12T00:40:53.857Z","emptyReason":null},"description":"药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。 Skill: unisound-clinical-trial-design Owner: unisound-llm Summary: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T08:05:21.935Z | user - Removed the file skill-card.md. - Updated internal medical model configuration from u1-insuremed to u2-med in SKILL.md. v1.0.0 | 2026-05-18T05:43:23.752Z | user med-pharma-clinical-trial-desi","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. 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unisound-llm\n\nSummary: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。\n\nTags: latest:1.0.2\n\nVersion history:\n\nv1.0.2 | 2026-07-16T08:05:21.935Z | user\n\n- Removed the file skill-card.md.\n- Updated internal medical model configuration from u1-insuremed to u2-med in SKILL.md.\n\nv1.0.0 | 2026-05-18T05:43:23.752Z | user\n\nmed-pharma-clinical-trial-design v1.0.0 – Initial release\n\n- Provides structured assistance for clinical trial protocol design tailored to pharmaceutical R&D needs.\n- Supports inputs such as indication, intervention, study phase, objectives, design, population, endpoints, and visit schedule.\n- Offers guidance aligned with the protocol design section of the referenced Clinical Trial Protocol Skill.\n- Accepts a variety of input file formats (JSON, CSV, XLSX, DOCX, PDF, images via OCR) for flexible workflow integration.\n- Requires API-based inference using the specified internal medical language model (no local fallback).\n- Delivers standardized JSON outputs with both structured data and natural language explanations.\n\nArchive index:\n\nArchive v1.0.2: 5 files, 10990 bytes\n\nFiles: _meta.json (149b), scripts/preprocess.py (8744b), scripts/run.py (10678b), skill-card.md (2385b), SKILL.md (5222b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: med-pharma-clinical-trial-design\ndescription: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"📋\"\n }\n }\n---\n\n# 临床试验设计辅助\n\n概述\n----\n本 skill 对应：药企 / 药物研发辅助 / 临床试验设计辅助。\n\n要求：药研方法设计。\n\n来源核验\n--------\n- 匹配来源：Clinical Trial Protocol Skill\n- 来源类型：公开 Agent Skill\n- 来源链接：https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill\n- 匹配结论：匹配。该 skill 明确覆盖临床试验 protocol 设计、研究目的、入排标准、终点和访视安排。\n\n参考部分\n--------\n只参考 Clinical Trial Protocol Skill 的 **protocol design** 部分：\n- 研究目的\n- 研究分期\n- 研究类型\n- 入排标准\n- 主要/次要终点\n- 访视安排\n\n不参考部分\n----------\n- 不参考临床试验数据统计\n- 不参考受试者招募管理\n- 不参考监管递交文件生成\n- 不扩展到药物靶点筛选或医学事务资料生成\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的设计型 skill：\n- 输入适应症、干预措施、试验分期和研究目的\n- 整理研究设计、入排标准、终点和访视安排\n- 输出临床试验设计辅助 JSON\n\n建议输入字段\n------------\n- `indication`：适应症\n- `intervention`：干预措施\n- `phase`：试验分期\n- `objective`：研究目的\n- `population`：目标人群\n- `control`：对照方式\n- `randomization`：随机化方式\n- `blinding`：盲法\n- `endpoints`：终点列表\n- `visits`：访视安排\n\n建议输出字段\n------------\n- `skill`：`临床试验设计辅助`\n- `indication`\n- `intervention`\n- `phase`\n- `study_design`\n- `population`\n- `endpoints`\n- `visit_schedule`\n\n医疗边界\n--------\n本 skill 只做临床试验方法设计辅助，不替代伦理审查、法规审查、统计师或临床专家最终确认。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indication\": \"非小细胞肺癌\",\n \"intervention\": \"研究药物A\",\n \"phase\": \"II期\",\n \"objective\": \"评价初步有效性和安全性\",\n \"population\": \"既往治疗失败患者\",\n \"control\": \"标准治疗\",\n \"randomization\": \"1:1\",\n \"blinding\": \"开放标签\",\n \"endpoints\": [\"ORR\", \"PFS\"],\n \"visits\": [\"筛选期\", \"基线\", \"每3周一次\"]\n}\n```\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。内部医疗大模型鉴权 key，由用户在 OpenClaw 中调用时提供。本 skill 强制通过 API 提供临床试验设计建议，无本地透传模式。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内部医疗大模型进行推理：\n\n- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`\n- model：`u2-med`\n- 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions）\n- 鉴权：通过 `--appkey` 参数传入 Bearer token，由用户在 OpenClaw 中调用时提供\n\n> 本 skill 强制走 API 推理，无本地透传模式。\n\n> 强制通过 `--appkey` 调用内部医疗大模型进行推理，无本地兜底模式。\n\nFile v1.0.2:_meta.json\n\n{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-clinical-trial-design\",\n \"version\": \"1.0.2\",\n \"publishedAt\": 1784189121935\n}\n\nFile v1.0.2:skill-card.md\n\n## Description:\n\nAssists drug-development teams with clinical trial method design by organizing study objectives, phase, population, endpoints, and visit schedules.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[unisound-llm](https://clawhub.ai/user/unisound-llm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nClinical research and drug-development teams use this skill to convert trial inputs such as indication, intervention, phase, objectives, population, endpoints, and visits into a structured clinical-trial design review. The output is decision support and still requires clinical, statistical, ethics, and regulatory review.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Clinical or proprietary R&D data may be processed through a remote medical-model endpoint and document-processing tools.\n\nMitigation: Use only approved environments and endpoints, prefer JSON input for sensitive work, and confirm data-handling approval before deployment.\n\nRisk: Credentials can be supplied on the command line as an app key.\n\nMitigation: Avoid passing real app keys on the command line when possible and use an approved secret-handling path.\n\nRisk: Document inputs and the external _shared preprocessing fallback can expand the execution and data-processing surface beyond the reviewed package.\n\nMitigation: Sandbox untrusted documents and review or remove the external _shared fallback before relying on the skill.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/unisound-llm/skills/unisound-clinical-trial-design)\n- [Clinical Trial Protocol Skill reference](https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, JSON, guidance]\n\n**Output Format:** [UTF-8 JSON containing structured trial-design data and Markdown guidance text]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires user-provided clinical-trial inputs and an app key for the remote medical-model endpoint.]\n\n## Skill Version(s):\n\n1.0.2 (source: release evidence)\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.\n\nArchive v1.0.0: 5 files, 11021 bytes\n\nFiles: _meta.json (149b), scripts/preprocess.py (8744b), scripts/run.py (10684b), skill-card.md (2479b), SKILL.md (5228b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: med-pharma-clinical-trial-design\ndescription: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"📋\"\n }\n }\n---\n\n# 临床试验设计辅助\n\n概述\n----\n本 skill 对应：药企 / 药物研发辅助 / 临床试验设计辅助。\n\n要求：药研方法设计。\n\n来源核验\n--------\n- 匹配来源：Clinical Trial Protocol Skill\n- 来源类型：公开 Agent Skill\n- 来源链接：https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill\n- 匹配结论：匹配。该 skill 明确覆盖临床试验 protocol 设计、研究目的、入排标准、终点和访视安排。\n\n参考部分\n--------\n只参考 Clinical Trial Protocol Skill 的 **protocol design** 部分：\n- 研究目的\n- 研究分期\n- 研究类型\n- 入排标准\n- 主要/次要终点\n- 访视安排\n\n不参考部分\n----------\n- 不参考临床试验数据统计\n- 不参考受试者招募管理\n- 不参考监管递交文件生成\n- 不扩展到药物靶点筛选或医学事务资料生成\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的设计型 skill：\n- 输入适应症、干预措施、试验分期和研究目的\n- 整理研究设计、入排标准、终点和访视安排\n- 输出临床试验设计辅助 JSON\n\n建议输入字段\n------------\n- `indication`：适应症\n- `intervention`：干预措施\n- `phase`：试验分期\n- `objective`：研究目的\n- `population`：目标人群\n- `control`：对照方式\n- `randomization`：随机化方式\n- `blinding`：盲法\n- `endpoints`：终点列表\n- `visits`：访视安排\n\n建议输出字段\n------------\n- `skill`：`临床试验设计辅助`\n- `indication`\n- `intervention`\n- `phase`\n- `study_design`\n- `population`\n- `endpoints`\n- `visit_schedule`\n\n医疗边界\n--------\n本 skill 只做临床试验方法设计辅助，不替代伦理审查、法规审查、统计师或临床专家最终确认。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indication\": \"非小细胞肺癌\",\n \"intervention\": \"研究药物A\",\n \"phase\": \"II期\",\n \"objective\": \"评价初步有效性和安全性\",\n \"population\": \"既往治疗失败患者\",\n \"control\": \"标准治疗\",\n \"randomization\": \"1:1\",\n \"blinding\": \"开放标签\",\n \"endpoints\": [\"ORR\", \"PFS\"],\n \"visits\": [\"筛选期\", \"基线\", \"每3周一次\"]\n}\n```\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。内部医疗大模型鉴权 key，由用户在 OpenClaw 中调用时提供。本 skill 强制通过 API 提供临床试验设计建议，无本地透传模式。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内部医疗大模型进行推理：\n\n- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`\n- model：`u1-insuremed`\n- 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions）\n- 鉴权：通过 `--appkey` 参数传入 Bearer token，由用户在 OpenClaw 中调用时提供\n\n> 本 skill 强制走 API 推理，无本地透传模式。\n\n> 强制通过 `--appkey` 调用内部医疗大模型进行推理，无本地兜底模式。\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-clinical-trial-design\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1779083003752\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:
\nHelps pharmaceutical R&D teams structure clinical trial design inputs and receive protocol-design review suggestions for indications, interventions, phases, populations, endpoints, and visit schedules.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[unisound-llm](https://clawhub.ai/user/unisound-llm)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nPharmaceutical R&D and clinical development users use this skill to prepare structured clinical trial design inputs and receive protocol-design review suggestions. It supports design work only and does not replace medical, statistical, ethics, or regulatory review.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Sensitive clinical trial design details may be sent to an external LLM provider.
\nMitigation: Require explicit disclosure and approval before use; redact proprietary or patient-adjacent material or route inference through an approved private endpoint.
\nRisk: Complex document parsers and OCR may process untrusted clinical documents.
\nMitigation: Run document conversion in a sandboxed, low-privilege environment and restrict accepted files to trusted sources.
\nRisk: Generated trial-design guidance may be incomplete, incorrect, or unsuitable for a specific study.
\nMitigation: Require review by clinical, statistical, ethics, and regulatory experts before using outputs in a protocol or decision process.
\n\n\n## Reference(s):
\n- [ClawHub release page](https://clawhub.ai/unisound-llm/unisound-clinical-trial-design)
\n- [Clinical Trial Protocol Skill reference](https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill)
\n\n\n## Skill Output:
\n**Output Type(s):** [JSON, Markdown, Guidance]
\n**Output Format:** [UTF-8 JSON with structured trial-design fields and a Markdown natural-language review field]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Requires caller-provided API credentials for external medical LLM inference.]
\n\n## Skill Version(s):
\n1.0.0 (source: server release metadata)
\n\n## Ethical Considerations:
\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: unisound-clinical-trial-design Owner: unisound-llm Summary: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T08:05:21.935Z | user - Removed the file skill-card.md. - Updated internal medical model configuration from u1-insuremed to u2-med in SKILL.md. v1.0.0 | 2026-05-18T05:43:23.752Z | user med-pharma-clinical-trial-desi","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY"},{"language":"json","snippet":"{\n \"indication\": \"非小细胞肺癌\",\n \"intervention\": \"研究药物A\",\n \"phase\": \"II期\",\n \"objective\": \"评价初步有效性和安全性\",\n \"population\": \"既往治疗失败患者\",\n \"control\": \"标准治疗\",\n \"randomization\": \"1:1\",\n \"blinding\": \"开放标签\",\n \"endpoints\": [\"ORR\", \"PFS\"],\n \"visits\": [\"筛选期\", \"基线\", \"每3周一次\"]\n}"},{"language":"json","snippet":"{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}"},{"language":"bash","snippet":"python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY"},{"language":"json","snippet":"{\n \"indication\": \"非小细胞肺癌\",\n \"intervention\": \"研究药物A\",\n \"phase\": \"II期\",\n \"objective\": \"评价初步有效性和安全性\",\n \"population\": \"既往治疗失败患者\",\n \"control\": \"标准治疗\",\n \"randomization\": \"1:1\",\n \"blinding\": \"开放标签\",\n \"endpoints\": [\"ORR\", \"PFS\"],\n \"visits\": [\"筛选期\", \"基线\", \"每3周一次\"]\n}"},{"language":"json","snippet":"{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: med-pharma-clinical-trial-design\ndescription: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"📋\"\n }\n }\n---\n\n# 临床试验设计辅助\n\n概述\n----\n本 skill 对应：药企 / 药物研发辅助 / 临床试验设计辅助。\n\n要求：药研方法设计。\n\n来源核验\n--------\n- 匹配来源：Clinical Trial Protocol Skill\n- 来源类型：公开 Agent Skill\n- 来源链接：https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill\n- 匹配结论：匹配。该 skill 明确覆盖临床试验 protocol 设计、研究目的、入排标准、终点和访视安排。\n\n参考部分\n--------\n只参考 Clinical Trial Protocol Skill 的 **protocol design** 部分：\n- 研究目的\n- 研究分期\n- 研究类型\n- 入排标准\n- 主要/次要终点\n- 访视安排\n\n不参考部分\n----------\n- 不参考临床试验数据统计\n- 不参考受试者招募管理\n- 不参考监管递交文件生成\n- 不扩展到药物靶点筛选或医学事务资料生成\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的设计型 skill：\n- 输入适应症、干预措施、试验分期和研究目的\n- 整理研究设计、入排标准、终点和访视安排\n- 输出临床试验设计辅助 JSON\n\n建议输入字段\n------------\n- `indication`：适应症\n- `intervention`：干预措施\n- `phase`：试验分期\n- `objective`：研究目的\n- `population`：目标人群\n- `control`：对照方式\n- `randomization`：随机化方式\n- `blinding`：盲法\n- `endpoints`：终点列表\n- `visits`：访视安排\n\n建议输出字段\n------------\n- `skill`：`临床试验设计辅助`\n- `indication`\n- `intervention`\n- `phase`\n- `study_design`\n- `population`\n- `endpoints`\n- `visit_schedule`\n\n医疗边界\n--------\n本 skill 只做临床试验方法设计辅助，不替代伦理审查、法规审查、统计师或临床专家最终确认。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indication\": \"非小细胞肺癌\",\n \"intervention\": \"研究药物A\",\n \"phase\": \"II期\",\n \"objective\": \"评价初步有效性和安全性\",\n \"population\": \"既往治疗失败患者\",\n \"control\": \"标准治疗\",\n \"randomization\": \"1:1\",\n \"blinding\": \"开放标签\",\n \"endpoints\": [\"ORR\", \"PFS\"],\n \"visits\": [\"筛选期\", \"基线\", \"每3周一次\"]\n}\n```\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。内部医疗大模型鉴权 key，由用户在 OpenClaw 中调用时提供。本 skill 强制通过 API 提供临床试验设计建议，无本地透传模式。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-clinical-trial-design\",\n \"version\": \"1.0.2\",\n \"publishedAt\": 1784189121935\n}"},{"path":"skill-card.md","content":"## Description:\n\nAssists drug-development teams with clinical trial method design by organizing study objectives, phase, population, endpoints, and visit schedules.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[unisound-llm](https://clawhub.ai/user/unisound-llm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nClinical research and drug-development teams use this skill to convert trial inputs such as indication, intervention, phase, objectives, population, endpoints, and visits into a structured clinical-trial design review. The output is decision support and still requires clinical, statistical, ethics, and regulatory review.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Clinical or proprietary R&D data may be processed through a remote medical-model endpoint and document-processing tools.\n\nMitigation: Use only approved environments and endpoints, prefer JSON input for sensitive work, and confirm data-handling approval before deployment.\n\nRisk: Credentials can be supplied on the command line as an app key.\n\nMitigation: Avoid passing real app keys on the command line when possible and use an approved secret-handling path.\n\nRisk: Document inputs and the external _shared preprocessing fallback can expand the execution and data-processing surface beyond the reviewed package.\n\nMitigation: Sandbox untrusted documents and review or remove the external _shared fallback before relying on the skill.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/unisound-llm/skills/unisound-clinical-trial-design)\n- [Clinical Trial Protocol Skill reference](https://agent-skills.md/skills/anthropics/healthcare/clinical-trial-protocol-skill)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, JSON, guidance]\n\n**Output Format:** [UTF-8 JSON containing structured trial-design data and Markdown guidance text]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires user-provided clinical-trial inputs and an app key for the remote medical-model endpoint.]\n\n## Skill Version(s):\n\n1.0.2 (source: release evidence)\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":"药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。 Skill: unisound-clinical-trial-design Owner: unisound-llm Summary: 药企药物研发辅助临床试验设计辅助。参考 Clinical Trial Protocol Skill 的 protocol design 部分，构建药研方法设计能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T08:05:21.935Z | user - Removed the file skill-card.md. - Updated internal medical model configuration from u1-insuremed to u2-med in SKILL.md. v1.0.0 | 2026-05-18T05:43:23.752Z | user med-pharma-clinical-trial-desi","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":953,"uniquenessScore":52,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-12T00:40:53.857Z","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":"2026-10-12T00:40:53.857Z","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":"2026-10-13T21:01:39.844Z","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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