Token导航 LogoToken导航TokenDH.com
研究检索需要联网clawhub未标认证来源可访问clear审计通过

fhir-questionnaire调查问卷

Agent Skill

fhir-questionnaire 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

29,303

周安装

1,174

GitHub Stars

公开资料未说明

下载量

9,486
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fhir-questionnaire(调查问卷)
来源仓库:https://github.com/elmariachi111/fhir-questionnaire
安装命令:
openclaw skills install fhir-questionnaire
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install fhir-questionnaire

简介

帮助从简单的需求构思文档创建符合 FHIR 的调查问卷定义。包含用于从官方编码 API 中查找有关医疗状况、发现、观察结果、药物、程序的 LOINC 和 SNOMED CT 代码的脚本。目前不需要 API 密钥。

SKILL.md

name
design-fhir-loinc-questionnaires
description
Helps creating FHIR conforming questionnaire definitions from plain requirement ideation docs. Contains scripts to look up LOINC and SNOMED CT codes for medical conditions, findings, observations, medications, procedures from a official coding APIs. No API keys required at the moment.
metadata
dependencies
python>=3.8, jsonschema>=4.0.0

FHIR Questionnaire Skill

⚠️ CRITICAL RULES - READ FIRST

NEVER suggest LOINC or SNOMED CT codes from memory or training data. ALWAYS use the search and query scripts in this skill.

When any clinical code is needed:

  1. For clinical questions/observations: ALWAYS run python scripts/search_loinc.py "search term" FIRST
  2. For clinical concepts/conditions: ALWAYS run python scripts/search_snomed.py "search term" FIRST
  3. ONLY use codes returned by the scripts
  4. If search fails or returns no results, DO NOT make up codes

Clinical codes from AI memory are highly unreliable and will cause incorrect clinical coding.

Network Access Requirements

Requires whitelisted network access:

  • clinicaltables.nlm.nih.gov (LOINC search)
  • tx.fhir.org (FHIR terminology server for LOINC answer lists and SNOMED CT search)

If network access fails, STOP. Do not suggest codes.

Essential Scripts (Use These Every Time)

1. Search LOINC Codes

ALWAYS run this before suggesting any LOINC code (clinical questions/observations):

python scripts/search_loinc.py "depression screening"
python scripts/search_loinc.py "blood pressure" --format fhir

2. Search SNOMED CT Codes

ALWAYS run this before suggesting any SNOMED CT code (clinical concepts/conditions):

python scripts/search_snomed.py "diabetes"
python scripts/search_snomed.py "hypertension" --format fhir
python scripts/search_snomed.py "diabetes mellitus" --semantic-tag "disorder"

Note: The --semantic-tag filter works best when the semantic tag appears in the display name (e.g., "Diabetes mellitus (disorder)").

3. Find Answer Options

For questions with standardized answers:

python scripts/query_valueset.py --loinc-code "72166-2"
python scripts/query_valueset.py --loinc-code "72166-2" --format fhir

4. Validate Questionnaire

Before finalizing:

python scripts/validate_questionnaire.py questionnaire.json

Templates

Start with assets/templates/:

  • minimal.json - Bare bones structure
  • basic.json - Simple questionnaire
  • advanced.json - Complex with conditional logic

Workflows

Standardized Clinical Instruments (PHQ-9, GAD-7, etc.)

# Step 1: Find panel code (NEVER skip this)
python scripts/search_loinc.py "PHQ-9 panel"

# Step 2: Find answer options
python scripts/query_valueset.py --loinc-code "FOUND-CODE" --format fhir

# Step 3: See examples/templates
# Check references/examples.md for complete implementations

Custom Organizational Questionnaires

# Step 1: Start with template
cp assets/templates/advanced.json my-questionnaire.json

# Step 2: For any clinical questions, search LOINC
python scripts/search_loinc.py "body weight"

# Step 3: Add answer options if available
python scripts/query_valueset.py --loinc-code "FOUND-CODE"

# Step 4: For custom questions without LOINC results, use inline answerOptions
# (no coding system needed - just code + display)

# Step 5: Validate
python scripts/validate_questionnaire.py my-questionnaire.json

Custom Answer Lists (When LOINC Has No Match)

When LOINC search returns no suitable answer list, use inline answerOption with system-less valueCoding by default. This is the simplest, spec-compliant approach for custom answer lists:

{
  "linkId": "sleep-quality",
  "type": "choice",
  "text": "How would you rate your sleep quality?",
  "answerOption": [
    {"valueCoding": {"code": "good", "display": "Good"}},
    {"valueCoding": {"code": "fair", "display": "Fair"}},
    {"valueCoding": {"code": "poor", "display": "Poor"}}
  ]
}

Do NOT invent a coding system URI. Omitting system is valid FHIR and signals that these are local, questionnaire-scoped codes.

Opt-in: Reusable Welshare Coding System

If the user explicitly requests reusable codes that can be shared across questionnaires, use the Welshare namespace (http://codes.welshare.app) via the helper script:

python scripts/create_custom_codesystem.py --interactive

This creates a CodeSystem + ValueSet pair. To convert an inline answer list to the reusable format, add "system": "http://codes.welshare.app/CodeSystem/<category>/<id>.json" to each valueCoding and optionally reference the ValueSet via answerValueSet. See references/loinc_guide.md for details.

Common Patterns

  • Conditional display: Use enableWhen to show/hide questions
  • Repeating groups: Set "repeats": true for medications, allergies, etc.
  • Standardized answers: Use query_valueset.py --loinc-code "CODE" for LOINC-backed answer lists
  • Custom answers: Use inline answerOption with valueCoding (no system) for non-standardized choices

See references/examples.md for complete working examples.

Script Reference

search_loinc.py - Find LOINC Codes

python scripts/search_loinc.py "blood pressure"
python scripts/search_loinc.py "depression" --limit 10 --format fhir

search_snomed.py - Find SNOMED CT Codes

python scripts/search_snomed.py "diabetes"
python scripts/search_snomed.py "hypertension" --limit 10 --format fhir
python scripts/search_snomed.py "asthma" --format table
python scripts/search_snomed.py "diabetes mellitus" --semantic-tag "disorder"

Formats: json (default), table, fhir Semantic tags (when present in results): disorder, finding, procedure, body structure, substance, organism Note: Semantic tag filtering only works when tags are included in the display name from the terminology server.

query_valueset.py - Find Answer Options

python scripts/query_valueset.py --loinc-code "72166-2"
python scripts/query_valueset.py --loinc-code "72166-2" --format fhir
python scripts/query_valueset.py --search "smoking"

Alternative servers (if tx.fhir.org fails):

  • --server https://hapi.fhir.org/baseR4
  • --server https://r4.ontoserver.csiro.au/fhir

validate_questionnaire.py - Validate Structure

python scripts/validate_questionnaire.py questionnaire.json
python scripts/validate_questionnaire.py questionnaire.json --verbose

extract_loinc_codes.py - Analyze Codes

python scripts/extract_loinc_codes.py questionnaire.json
python scripts/extract_loinc_codes.py questionnaire.json --validate

create_custom_codesystem.py - Reusable Custom Codes (Opt-in)

python scripts/create_custom_codesystem.py --interactive

Only use when the user explicitly requests reusable codes across questionnaires. Uses the Welshare namespace: http://codes.welshare.app. Default for custom answers is inline answerOption without a coding system.

Troubleshooting

  • No LOINC results: Use broader search terms (e.g., "depression" not "PHQ-9 question 1")
  • Network errors: Try alternative servers with --server flag
  • Validation errors: Check references/fhir_questionnaire_spec.md for requirements
  • No answer list found: Use inline answerOption with system-less valueCoding (code + display only). Do NOT fall back to a custom coding system unless the user explicitly requests it

Deep Knowledge References

We've assembled deep knowledge for you to consult on specific topics. Checkout the index file on See REFERENCE.md and drill down the knowledge path for highly detailed instructions on modelling questionnaires.

Reference Links

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

89.68%
按下载量换算8,507

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills