What makes unisound-clinical-trial-statistics notable?

1K downloads. Trust evidence available

How should unisound-clinical-trial-statistics be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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unisound-clinical-trial-statistics

药企药物研发辅助临床试验数据统计。参考 Statistical Analysis skill 的 descriptive statistics 与 group comparison 部分，构建试验分析支持能力。 Skill: unisound-clinical-trial-statistics Owner: unisound-llm Summary: 药企药物研发辅助临床试验数据统计。参考 Statistical Analysis skill 的 descriptive statistics 与 group comparison 部分，构建试验分析支持能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T08:06:07.278Z | user - Removed the file skill-card.md. - Updated model configuration in SKILL.md: changed model from "u1-insuremed" to "u2-med". - No other user-facing changes. v1.0.0 | 2

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.2release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1770nyybxj4gn0vk38n6wr6k584x8ea:unisound-clinical-trial-statistics

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-unisound-llm-unisound-clinical-trial-statistics/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

13,390 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: med-pharma-clinical-trial-statistics
description: 药企药物研发辅助临床试验数据统计。参考 Statistical Analysis skill 的 descriptive statistics 与 group comparison 部分，构建试验分析支持能力。
metadata:
  {
    "openclaw":
      {
        "emoji": "📊"
      }
  }
---

# 临床试验数据统计

概述
----
本 skill 对应：药企 / 药物研发辅助 / 临床试验数据统计。

要求：试验分析支持。

来源核验
--------
- 匹配来源：Statistical Analysis
- 来源类型：公开 Agent Skill
- 来源链接：https://agent-skills.md/skills/Jst-Well-Dan/Skill-Box/statistical-analysis
- 匹配结论：匹配。该 skill 明确覆盖统计分析、描述性统计、组间比较和结果报告。

参考部分
--------
只参考 Statistical Analysis skill 的 **descriptive statistics 与 group comparison** 部分：
- 试验数据分组
- 终点变量描述性统计
- 组间均值差异整理
- 样本量计数

不参考部分
----------
- 不参考临床试验方案设计
- 不参考 EDC 或数据库管理
- 不参考医学写作或学术资料生成
- 不扩展到复杂统计建模

构建方式
--------
OpenClaw 中应构建为一个独立的统计型 skill：
- 输入临床试验记录数据
- 按试验组对终点变量做描述性统计
- 输出统计分析支持 JSON

建议输入字段
------------
- `trial_id`：试验编号
- `population`：分析集
- `group_field`：分组字段名，默认 `group`
- `endpoint_fields`：终点字段列表
- `records`：试验记录列表

建议输出字段
------------
- `skill`：`临床试验数据统计`
- `trial_id`
- `population`
- `statistics`
- `analysis_note`

医疗边界
--------
本 skill 只做临床试验数据统计整理，不替代统计分析计划、注册统计师复核或监管申报分析。

快速开始
--------
从本 skill 目录执行：

```bash
python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY
```

最小输入示例
------------
```json
{
  "trial_id": "trial-001",
  "population": "FAS",
  "group_field": "group",
  "endpoint_fields": ["change_from_baseline"],
  "records": [
    {"subject_id": "001", "group": "试验组", "change_from_baseline": -2.1},
    {"subject_id": "002", "group": "对照组", "change_from_baseline": -0.8}
  ]
}
```

输出约定
--------
输出 UTF-8 JSON，采用统一格式：

```json
{
  "skill": "技能名称",
  "status": "ok",
  "data": { /* 结构化数据 */ },
  "text": "API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户"
}
```

- `data`：本地预处理得到的结构化数据
- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式

支持的输入格式
--------------
除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：

| 格式 | 说明 |
|------|------|
| JSON | 默认，直接读取结构化输入 |
| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |
| TXT / MD | key:value 文本格式（支持中文/英文字段名） |
| PDF / DOC / DOCX | 文档，提取文本后解析 |
| PNG / JPG 等图片 | OCR 提取文本后解析 |

统一入口附加参数
----------------
- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。
- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。
- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。
- `--save-prepared`：保存预处理后的 JSON，便于调试。
- `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。

依赖
----
### 运行环境
- Python 3.7+

### Python 第三方包（可选，按输入格式需要）
| 包名 | 用途 | 必要条件 |
|------|------|---------|
| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |
| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |

### 外部工具（可选，按输入格式需要）
| 工具 | 用途 | 必要条件 |
|------|------|---------|
| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |
| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |
| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |

> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。

模型配置
--------
本 skill 执行时通过内部医疗大模型进行推理：

- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`
- model：`u2-med`
- 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions）

_meta.json

{
  "ownerId": "kn7ft98qwhxyrgcy7b7rzdhrrd8366k6",
  "slug": "unisound-clinical-trial-statistics",
  "version": "1.0.2",
  "publishedAt": 1784189167278
}

skill-card.md

## Description:

Supports clinical trial data statistics for pharmaceutical research by computing descriptive endpoint summaries, group comparisons, and AI-assisted interpretation.

This skill is ready for commercial/non-commercial use.

## Publisher:

[unisound-llm](https://clawhub.ai/user/unisound-llm)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and clinical analytics teams use this skill to prepare clinical trial records, calculate per-group descriptive statistics for endpoint fields, and generate a Markdown interpretation for review. It is intended as analysis support and does not replace a statistical analysis plan, registered statistician review, or regulatory submission analysis.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Trial-derived data is sent to a remote medical-model endpoint for interpretation.

Mitigation: Use only when authorized to transmit the data, minimize or de-identify sensitive trial records before use, and trust the endpoint and credential handling.

Risk: AI-generated clinical interpretation may be incomplete or misleading for regulated clinical decisions.

Mitigation: Have a qualified statistician review the generated interpretation and rely on approved statistical analysis plans for formal analyses.

Risk: Office, PDF, and image inputs require optional parsers or external tools and may expand the processing surface for untrusted files.

Mitigation: Prefer JSON, CSV, or XLSX inputs from trusted sources and run preprocessing in a contained environment when handling documents or images.

Risk: The skill can fall back to an external shared preprocessor when local preprocessing fails.

Mitigation: Review the runtime environment and available shared preprocessor before deployment, or disable unsupported input types.

## Reference(s):

- [Source reference: Statistical Analysis skill](https://agent-skills.md/skills/Jst-Well-Dan/Skill-Box/statistical-analysis)

## Skill Output:

**Output Type(s):** [JSON, Markdown, Analysis, Files]

**Output Format:** [UTF-8 JSON containing structured statistics data and Markdown natural-language interpretation]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires an app key for a remote medical model endpoint; optional preprocessing supports JSON, CSV, XLSX, XLS, TXT, Markdown, PDF, DOC, DOCX, and image inputs when dependencies are available.]

## Skill Version(s):

1.0.2 (source: server release metadata)

## Ethical Considerations:

Users 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.

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