用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill clinical-trial-protocol-waypoint-based-design命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | clinical-trial-protocol-waypoint-based-design |
| description | Sub-skill of clinical-trial-protocol: Waypoint-Based Design (+2). |
| version | 1.0.0 |
| category | science |
| type | reference |
| scripts_exempt | true |
All analysis data is stored in waypoints/ directory as JSON/markdown files:
waypoints/
├── intervention_metadata.json # Intervention info, status, initial context
├── 01_clinical_research_summary.json # Similar trials, FDA guidance, recommendations
├── 02_protocol_foundation.md # Protocol sections 1-6 (Step 2)
├── 03_protocol_intervention.md # Protocol sections 7-8 (Step 3)
├── 04_protocol_operations.md # Protocol sections 9-12 (Step 4)
├── 02_protocol_draft.md # Complete protocol (concatenated in Step 4)
├── 02_protocol_metadata.json # Protocol metadata
└── 02_sample_size_calculation.json # Statistical sample size calculation
Rich Initial Context Support:
Users can provide substantial documentation, technical specifications, or research data when initializing the intervention (Step 0). This is preserved in intervention_metadata.json under the initial_context field. Later steps reference this context for more informed protocol development.
Each step is an independent skill in references/ directory:
references/
├── 00-initialize-intervention.md # Collect device or drug information
├── 01-research-protocols.md # Clinical trials research and FDA guidance
├── 02-protocol-foundation.md # Protocol sections 1-6 (foundation, design, population)
├── 03-protocol-intervention.md # Protocol sections 7-8 (intervention details)
├── 04-protocol-operations.md # Protocol sections 9-12 (assessments, statistics, operations)
└── 05-generate-document.md # NIH Protocol generation
scripts/
└── sample_size_calculator.py # Statistical power analysis (validated)