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研究检索external-servicegithub未标认证来源可访问许可证需确认审计提醒

doctorgdoctorg 搜索

Agent Skill

doctorg 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

1,752

周安装

73

GitHub Stars

141

下载量

584
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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

unknown

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:doctorg(doctorg 搜索)
来源仓库:https://github.com/glebis/claude-skills
仓库路径:skills/doctorg
安装命令:
npx skills add https://github.com/glebis/claude-skills --skill doctorg
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/glebis/claude-skills --skill doctorg

简介

基于可信来源的健康研究问答系统,提供证据强度评级。

  • 支持快速回答或深度调研两种模式,可选是否包含个人健康上下文。
  • 整合 WebSearch、Tavily 和 Firecrawl 进行多轮信息检索。
  • 聚焦于健身、营养和医学领域的循证建议。
  • doctorg 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Doctor G -- Evidence-Based Health Research

Answer health and wellness questions using only trusted, evidence-based sources with explicit evidence strength ratings.

Usage

# Quick answer (WebSearch only, ~30s)
/doctorg Is creatine safe for daily use?

# Deep research (WebSearch + Tavily, ~90s)
/doctorg --deep Huberman vs Attia on fasted training

# Full investigation (WebSearch + Tavily + Firecrawl, ~3min)
/doctorg --full What does current evidence say about GLP-1 agonists for non-diabetic weight loss?

# Without personal health context
/doctorg --no-personal Best stretching protocol for lower back pain

Depth Levels

LevelFlagToolsTimeUse When
Quick*(default)*WebSearch~30sSimple factual questions
Deep--deepWebSearch + Tavily~90sCompeting claims, nuanced topics
Full--fullWebSearch + Tavily + Firecrawl~3minControversial topics, need primary sources

How It Works

1. Parse Query & Detect Topic Category

Classify the question into one of:

  • Nutrition/Supplements (examine.com gets priority)
  • Exercise/Training (PubMed + ACSM get priority)
  • Sleep (focus sleep-specific databases)
  • Disease/Condition (condition-specific orgs + clinical guidelines)
  • Medication/Treatment (FDA, EMA, Cochrane get priority)
  • Mental Health (APA, mental health orgs)
  • General Wellness (broad search across all tiers)

2. Search Evidence Sources (Tiered)

Search sources in priority order. See references/sources.md for complete domain list.

Tier 1 -- Primary Research (highest weight):

  • PubMed/PMC, Cochrane Library, WHO, ClinicalTrials.gov

Tier 2 -- Clinical/Institutional (high weight):

  • Mayo Clinic, Hopkins Medicine, Cleveland Clinic, Harvard Health
  • Condition-specific: AHA, ACS, ADA, Alzheimer's Association

Tier 3 -- Expert Analysis (medium weight):

  • Examine.com, STAT News, Health News Review
  • Consensus.app, Epistemonikos

Tier 4 -- Quality Journalism (context/framing):

  • The Atlantic, NYT, NPR, Guardian, FiveThirtyEight

Search Strategy by Depth

Quick (default):

WebSearch(query, allowed_domains=[Tier 1 + Tier 2 domains])
WebSearch(query + "systematic review OR meta-analysis", allowed_domains=[Tier 1])

Deep (--deep): All Quick searches PLUS:

tavily-search(query, include_domains=[Tier 1-3])
WebSearch(query + "expert opinion OR position statement", allowed_domains=[Tier 2-3])
WebSearch(query + "risks OR side effects OR contraindications")

Full (--full): All Deep searches PLUS:

firecrawl-research for top 2-3 most relevant results from Tier 1
WebSearch for competing/contrarian viewpoints
WebSearch(query + "retracted OR debunked OR misleading")

3. Pull Personal Health Context (unless --no-personal)

Query Apple Health database for relevant metrics:

python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json vitals
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json daily
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json sleep --days 7
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json workouts --days 30

Select ONLY metrics relevant to the query:

  • Exercise question -> recent workouts, activity, resting HR, VO2 max
  • Sleep question -> sleep data, HRV
  • Nutrition question -> weight trends, activity level
  • Heart question -> HR, HRV, resting HR, blood pressure

4. Synthesize with Evidence Grading

Rate each claim using simplified GRADE scale:

RatingMeaningBased On
StrongConsistent evidence from systematic reviews/meta-analyses or multiple large RCTsLevel I-II evidence
ModerateSupported by well-designed studies but some inconsistency or limitationsLevel II-III evidence
WeakLimited evidence, small studies, or conflicting resultsLevel III-IV evidence
MinimalExpert opinion, case reports, or preliminary/animal studies onlyLevel V evidence
ContestedActive scientific debate with credible evidence on both sidesMixed levels

5. Format Output

# [Topic Title]

**Short answer**: [1-2 sentence direct answer]

## [Expert/Position A] (if comparing viewpoints)
- Key claim 1
- Key claim 2
- Has **evolved stance**: [if applicable]

## [Expert/Position B]
- Key claim 1
- Key claim 2

## Where They Actually Agree (if comparing)
- Agreement point 1
- Agreement point 2

## What Research Shows

| Claim | Evidence Strength |
|-------|------------------|
| Claim 1 | **Strong** |
| Claim 2 | **Weak** (reason) |
| Claim 3 | **Contested** |

## For You Specifically (if --personal context available)

[Personalized interpretation based on user's health data]

[Specific actionable recommendation]

## Sources
- [Source 1 title](url) -- Tier, year
- [Source 2 title](url) -- Tier, year

## Limitations
- [Any caveats about the evidence or this analysis]

Output rules:

  • NEVER give medical diagnoses or replace professional advice
  • ALWAYS include disclaimer: "This is research synthesis, not medical advice"
  • When evidence is Weak or Minimal, explicitly say so
  • When claims are Contested, present both sides fairly
  • Prefer recent sources (last 5 years) over older ones
  • Flag if key studies have been retracted or challenged
  • Include the "For You Specifically" section only when health data adds meaningful context

6. Disclaimer (always append)

---
*Research synthesis, not medical advice. Consult a healthcare provider for personal decisions.*

Examples

Quick

/doctorg Is 10000 steps a day backed by science?

Deep (comparing experts)

/doctorg --deep Huberman vs Attia on fasted training

Full (controversial topic)

/doctorg --full Safety profile of long-term melatonin supplementation

Integration with Other Skills

  • health-data: Pulls Apple Health metrics for personalization
  • tavily-search: Deep research at Tier 1-3 sources
  • firecrawl-research: Full-text extraction from primary sources
  • fact-checker: Can be chained for verification of specific claims

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

37.61%
按下载量换算220

Claude

27.86%
按下载量换算163

Cursor

17.96%
按下载量换算105

Gemini CLI

10.28%
按下载量换算60

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

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