Patient Recruitment Ad Generator
Generate ethical, compliant, and patient-friendly recruitment advertisements for clinical trials.
When to Use
- - Use this skill when the task is to Generate ethical, compliant, and patient-friendly recruitment advertisements for clinical trials.
- Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
- - Scope-focused workflow aligned to: Generate ethical, compliant, and patient-friendly recruitment advertisements for clinical trials.
- Packaged executable path(s):
scripts/main.py. - Reference material available in
references/ for task-specific guidance. - Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
See ## Prerequisites above for related details.
- -
Python: 3.10+. Repository baseline for current packaged skills. - INLINECODE5 :
not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.
Example Usage
See ## Usage above for related details.
CODEBLOCK0
Example run plan:
- 1. Confirm the user input, output path, and any required config values.
- Edit the in-file
CONFIG block or documented parameters if the script uses fixed settings. - Run
python scripts/main.py with the validated inputs. - Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
- - Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
- Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
- Primary implementation surface:
scripts/main.py. - Reference guidance:
references/ contains supporting rules, prompts, or checklists. - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
- Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
CODEBLOCK1
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
CODEBLOCK2
Workflow
- 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
- Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
- Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
- Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
- If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Purpose
This skill helps researchers, CROs, and medical institutions create patient recruitment advertisements that meet Institutional Review Board (IRB) / Ethics Committee (EC) requirements while being accessible and encouraging to potential participants.
Key Compliance Requirements
Essential Elements (IRB/EC Standards)
- 1. Trial Identity
- Study title or identifier
- Sponsor information (if required)
- 2. Purpose Statement
- Clear description of the research
- Why the study is being conducted
- 3. Eligibility Criteria
- Inclusion criteria (who can participate)
- Exclusion criteria (who cannot participate)
- 4. Study Procedures
- What participants will do
- Time commitment required
- Number of visits
- 5. Risks and Benefits
- Potential risks/discomforts
- Potential benefits (direct and societal)
- Statement that benefits are not guaranteed
- 6. Confidentiality
- How personal information is protected
- Regulatory oversight mention
- 7. Voluntary Participation
- Right to withdraw at any time
- No penalty for withdrawal
- No impact on regular medical care
- 8. Contact Information
- Principal Investigator
- Study coordinator
- IRB/EC contact for questions about rights
Prohibited Content
- - Promises of cure or guaranteed benefits
- Undue influence (excessive payment, coercion)
- Misleading language ("free treatment" when experimental)
- Stigmatizing terms ("sufferers," "victims")
- Pressure tactics (limited spots, urgency)
Usage
Input Parameters
CODEBLOCK3
Example
CODEBLOCK4
Output
Generates a structured recruitment ad with:
- - Headline (attention-grabbing, compliant)
- Study summary (plain language)
- Who can participate (eligibility)
- What's involved (procedures)
- Rights and protections (ethics)
- Contact information
Technical Notes
- - Difficulty: Medium
- Language: Patient-friendly (6th-8th grade reading level)
- Tone: Respectful, informative, empowering
- Format: Print, digital, or social media ready
- Compliance: Based on FDA, EMA, CIOMS, and ICH-GCP guidelines
References
See references/ folder for:
- -
fda_guidance.md - FDA guidance on informed consent - INLINECODE15 - European ethics requirements
- INLINECODE16 - ICH-GCP E6(R2) recruitment provisions
- INLINECODE17 - NIH Plain Language guidelines
- INLINECODE18 - Sample ads for different therapeutic areas
Safety & Ethics
- - Always include voluntary participation statement
- Never guarantee therapeutic benefit
- Ensure readability for target population
- Review with IRB/EC before use
- Avoid therapeutic misconception
Technical Difficulty: Medium
Category: Pharma / Clinical Research
Last Updated: 2026-02-05
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access |
No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
Security Checklist
- - [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] Input file paths validated (no ../ traversal)
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no stack traces exposed)
- [ ] Dependencies audited
Prerequisites
No additional Python packages required.
Evaluation Criteria
Success Metrics
- - [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable
Test Cases
- 1. Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- - Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support
Output Requirements
Every final response should make these items explicit when they are relevant:
- - Objective or requested deliverable
- Inputs used and assumptions introduced
- Workflow or decision path
- Core result, recommendation, or artifact
- Constraints, risks, caveats, or validation needs
- Unresolved items and next-step checks
Error Handling
- - If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
- If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
- If
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. - Do not fabricate files, citations, data, search results, or execution outcomes.
Input Validation
This skill accepts requests that match the documented purpose of patient-recruitment-ad-gen and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
INLINECODE21 only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
- 1. Objective
- Inputs Received
- Assumptions
- Workflow
- Deliverable
- Risks and Limits
- Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.
患者招募广告生成器
为临床试验生成符合伦理、合规且对患者友好的招募广告。
使用场景
- - 当任务要求为临床试验生成符合伦理、合规且对患者友好的招募广告时,使用此技能。
- 用于需要明确假设、限定范围和可重复输出格式的学术写作任务。
- 当需要为缺失输入、执行错误或部分证据提供有文档记录的备用路径时,使用此技能。
主要特性
- - 聚焦范围的工作流程,与以下目标一致:为临床试验生成符合伦理、合规且对患者友好的招募广告。
- 打包的可执行路径:scripts/main.py。
- references/目录下提供参考资料,用于任务特定指导。
- 结构化执行路径,确保输出一致且可审查。
依赖项
相关详情请参见上文的## 前提条件。
- - Python:3.10+。当前打包技能的仓库基线版本。
- 第三方包:本技能包中未明确固定版本。如果此技能需要更严格的环境控制,请添加固定版本。
使用示例
相关详情请参见上文的## 用法。
bash
cd 20260318/scientific-skills/Academic Writing/patient-recruitment-ad-gen
python -m py_compile scripts/main.py
python scripts/main.py --help
示例运行计划:
- 1. 确认用户输入、输出路径以及任何必需的配置值。
- 如果脚本使用固定设置,编辑文件内的CONFIG块或文档化参数。
- 使用验证后的输入运行python scripts/main.py。
- 审查生成的输出,并返回最终成果物,同时注明所有假设。
实现细节
相关详情请参见上文的## 工作流程。
- - 执行模型:验证请求,选择打包的工作流程,生成限定范围的交付物。
- 输入控制:在运行任何脚本之前,确认源文件、范围限制、输出格式和验收标准。
- 主要实现界面:scripts/main.py。
- 参考指南:references/包含支持性规则、提示或检查清单。
- 需首先明确的参数:输入路径、输出路径、范围过滤器、阈值以及任何领域特定约束。
- 输出规范:保持结果可重复,明确标识假设,避免无文档记录的副作用。
快速检查
在深入执行前,使用此命令验证打包脚本入口点是否可解析。
bash
python -m py_compile scripts/main.py
审计就绪命令
使用这些具体命令进行验证。它们特意设计为自包含,避免使用占位符路径。
bash
python -m py_compile scripts/main.py
python scripts/main.py --help
工作流程
- 1. 在进行详细工作前,确认用户目标、必需输入和不可协商的约束条件。
- 验证请求是否与文档化范围匹配,如果任务需要不支持的假设,则提前停止。
- 仅使用实际可用的输入,使用打包脚本路径或文档化的推理路径。
- 返回结构化结果,区分假设、交付物、风险和未解决事项。
- 如果执行失败或输入不完整,切换到备用路径,并准确说明阻止完整完成的原因。
目的
此技能帮助研究人员、合同研究组织和医疗机构创建符合机构审查委员会/伦理委员会要求,同时易于潜在参与者理解并具有鼓励作用的患者招募广告。
关键合规要求
基本要素(IRB/EC标准)
- 1. 试验身份
- 研究标题或标识符
- 申办方信息(如需要)
- 2. 目的说明
- 清晰描述研究内容
- 进行该研究的原因
- 3. 资格标准
- 纳入标准(谁可以参与)
- 排除标准(谁不能参与)
- 4. 研究程序
- 参与者需要做什么
- 所需时间投入
- 访视次数
- 5. 风险与获益
- 潜在风险/不适
- 潜在获益(直接获益和社会获益)
- 声明不保证获益
- 6. 保密性
- 个人信息如何保护
- 提及监管监督
- 7. 自愿参与
- 随时退出研究的权利
- 退出不受处罚
- 不影响常规医疗护理
- 8. 联系信息
- 主要研究者
- 研究协调员
- 关于权利的疑问联系IRB/EC
禁止内容
- - 治愈承诺或保证获益
- 不当影响(过度补偿、胁迫)
- 误导性语言(实验性治疗称为免费治疗)
- 污名化术语(患者、受害者)
- 施压策略(名额有限、紧迫性)
用法
输入参数
python
{
disease_condition: str, # 目标疾病/状况
study_phase: str, # I/II/III/IV期
intervention_type: str, # 药物、器械、程序等
target_population: str, # 人口统计学特征、年龄范围
study_duration: str, # 预期时间投入
site_location: str, # 研究地点位置
compensation: Optional[str], # 参与者补偿(如有)
pi_name: str, # 主要研究者
contact_info: str, # 咨询电话/邮箱
irb_reference: str # IRB/EC批准编号
}
示例
python
python /Users/z04030865/.openclaw/workspace/skills/patient-recruitment-ad-gen/scripts/main.py \
--disease 2型糖尿病 \
--phase II期 \
--intervention 研究性口服药物 \
--population 18-65岁2型糖尿病成人患者 \
--duration 12周,6次临床访视 \
--location 市医疗中心,C栋 \
--pi 陈莎拉医生 \
--contact (555) 123-4567 或 diabetes-study@cmc.edu \
--irb IRB-2024-001
输出
生成结构化的招募广告,包含:
- - 标题(引人注目、合规)
- 研究摘要(通俗语言)
- 谁可以参与(资格条件)
- 涉及内容(程序)
- 权利和保护(伦理)
- 联系信息
技术说明
- - 难度:中等
- 语言:对患者友好(6-8年级阅读水平)
- 语气:尊重、信息丰富、赋权
- 格式:适用于印刷、数字或社交媒体
- 合规性:基于FDA、EMA、CIOMS和ICH-GCP指南
参考资料
参见references/文件夹:
- - fdaguidance.md - FDA关于知情同意的指南
- emaguidelines.md - 欧洲伦理要求
- ichgcp.md - ICH-GCP E6(R2)招募规定
- plainlanguageguide.pdf - NIH通俗语言指南
- templateexamples/ - 不同治疗领域的示例广告
安全与伦理
- - 始终包含自愿参与声明
- 绝不保证治疗效果
- 确保目标人群可读性
- 使用前经IRB/EC审查
- 避免治疗误解
技术难度:中等
类别:制药/临床研究
最后更新:2026-02-05
风险评估
| 风险指标 | 评估 | 级别 |
|---|
| 代码执行 | Python/R脚本本地执行 | 中等 |
| 网络访问 |
无外部API调用 | 低 |
| 文件系统访问 | 读取输入文件,写入输出文件 | 中等 |
| 指令篡改 | 标准提示指南 | 低 |
| 数据暴露 | 输出文件保存到工作区 | 低 |
安全检查清单
- - [ ] 无硬编码凭据或API密钥
- [ ] 无未经授权的文件系统访问(../)
- [ ] 输出不暴露敏感信息
- [ ] 已实施提示注入保护
- [ ] 输入文件路径已验证(无../遍历)
- [ ] 输出目录限制在工作区内
- [ ] 脚本在沙盒环境中执行
- [ ] 错误消息已清理(不暴露堆栈跟踪)
- [ ] 依赖项已审计
前提条件
无需额外的Python包。
评估标准
成功指标
- - [ ] 成功执行主要功能
- [ ] 输出符合质量标准
- [ ] 优雅处理边缘情况
- [ ] 性能可接受
测试用例
- 1. 基本功能:标准输入 → 预期输出
- 边缘情况:无效输入 → 优雅的错误处理
- 性能:大数据集 → 可接受的处理时间
生命周期状态
- - 当前阶段:草案
- 下次审查日期:2026-03-06
- 已知问题:无