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parallel-literature-search平行文献检索

Agent Skill

parallel-literature-search 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

451

周安装

19

GitHub Stars

3

下载量

158
CodexClaudeCursorGemini CLI

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

3

许可证

MIT

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:parallel-literature-search(平行文献检索)
来源仓库:https://github.com/drshailesh88/integrated_content_os
仓库路径:skills/parallel-literature-search
安装命令:
npx skills add https://github.com/drshailesh88/integrated_content_os --skill parallel-literature-search
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/drshailesh88/integrated_content_os --skill parallel-literature-search

简介

parallel-literature-search 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。

  • 适用于文献调研、学术资料整理和多源信息交叉验证等研究场景。
  • 通过 GitHub 仓库安装,使用 npx skills add 命令添加指定技能。
  • 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。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Parallel Literature Search

All sources at once. This skill searches PubMed, web, and your RAG knowledge base in parallel, then synthesizes the findings into a single coherent summary with citations.


WHAT IT DOES

SourceWhat It SearchesOutput
PubMedAcademic literature, trials, reviewsPMIDs, abstracts, citations
PerplexityWeb, recent news, guidelinesSummaries with sources
RAG (AstraDB)Your curated knowledge baseGuideline excerpts, textbook refs

THE DIFFERENCE

ApproachSourcesTimeDepth
Sequential searchOne at a time5+ minDeeper but slow
Parallel searchAll at once30-60 secFast overview
Manual searchYou do it20+ minVariable

TRIGGERS

Use this skill when you say:

  • "Search for evidence on [topic]"
  • "What does the literature say about [topic]?"
  • "Find research on [topic]"
  • "Quick literature review on [topic]"
  • "Evidence for [clinical question]"

USAGE

In Claude Code (Recommended)

"Parallel search: SGLT2 inhibitors in HFpEF"

"Find all evidence on GLP-1 and cardiovascular outcomes"

"What does literature say about statin discontinuation?"

CLI Mode

# Basic search
python scripts/parallel_search.py --query "SGLT2 inhibitors heart failure"

# Specify sources
python scripts/parallel_search.py --query "GLP-1 cardiovascular" --sources pubmed,perplexity

# Save output
python scripts/parallel_search.py --query "CAC scoring" --output ~/research/

OUTPUT FORMAT

# Literature Search: SGLT2 Inhibitors in HFpEF

**Query:** SGLT2 inhibitors heart failure preserved ejection fraction
**Searched:** 2025-01-01 09:30:45
**Sources:** PubMed, Perplexity, RAG

---

## SYNTHESIS

SGLT2 inhibitors have demonstrated significant benefit in HFpEF based on
EMPEROR-Preserved and DELIVER trials. Key findings:

1. **EMPEROR-Preserved (PMID: 34449189)**: Empagliflozin reduced composite
   endpoint of CV death/HHF by 21% (HR 0.79, 95% CI 0.69-0.90)

2. **DELIVER (PMID: 36027570)**: Dapagliflozin showed 18% reduction in
   worsening HF/CV death (HR 0.82, 95% CI 0.73-0.92)

3. Current guidelines (ACC/AHA 2022) recommend SGLT2i as Class 2a for HFpEF.

---

## PUBMED RESULTS (5 most relevant)

| # | Title | PMID | Year | Type |
|---|-------|------|------|------|
| 1 | Empagliflozin in HFpEF | 34449189 | 2021 | RCT |
| 2 | Dapagliflozin in HFpEF | 36027570 | 2022 | RCT |
| 3 | Meta-analysis SGLT2i HF | 37654321 | 2023 | MA |
| 4 | Real-world SGLT2i outcomes | 38765432 | 2024 | Obs |
| 5 | SGLT2i mechanism review | 39876543 | 2024 | Rev |

---

## WEB RESULTS (Perplexity)

- **ACC 2024 Update**: New data on SGLT2i in cardiorenal syndrome
- **ESC Guidelines 2023**: Updated recommendations for SGLT2i
- **Clinical Practice**: Real-world prescribing patterns

---

## RAG RESULTS (Your Knowledge Base)

- **Braunwald Ch. 27**: Heart failure classification and treatment
- **ACC/AHA HF Guidelines**: Class recommendations for SGLT2i
- **ESC HF Guidelines**: European perspective on SGLT2i use

---

## EVIDENCE QUALITY

| Source | Strength | Notes |
|--------|----------|-------|
| EMPEROR-Preserved | High | Large RCT, well-conducted |
| DELIVER | High | Large RCT, confirmatory |
| Meta-analyses | High | Consistent findings |
| Real-world | Moderate | Observational limitations |

---

## KEY CITATIONS

1. Anker SD, et al. N Engl J Med. 2021;385:1451-1461. (PMID: 34449189)
2. Solomon SD, et al. N Engl J Med. 2022;387:1089-1098. (PMID: 36027570)
3. Vaduganathan M, et al. Lancet. 2022;400:757-767. (Meta-analysis)

---

## GAPS & CONSIDERATIONS

- Limited data in specific HFpEF phenotypes
- Long-term safety data still accumulating
- Indian-specific data limited (consider local studies)

ARCHITECTURE

User Query
     │
     ├──────────────────┬──────────────────┐
     │                  │                  │
     ▼                  ▼                  ▼
[PubMed Agent]   [Perplexity Agent]  [RAG Agent]
     │                  │                  │
     ▼                  ▼                  ▼
  PMIDs &           Web sources       Guideline
  Abstracts         & summaries       excerpts
     │                  │                  │
     └──────────────────┴──────────────────┘
                        │
                        ▼
               [Synthesis Agent]
                        │
                        ▼
              Unified Report with
              Citations & Evidence

INTEGRATION

Works With:

  • quick-topic-researcher - Quick overview
  • deep-researcher - Comprehensive review
  • youtube-script-master - Evidence for scripts
  • cardiology-editorial - Literature for editorials

Feeds Into:

  • Content creation pipeline
  • Video script research
  • Editorial writing
  • Newsletter content

DEPENDENCIES

# Core
anthropic>=0.18.0
python-dotenv>=1.0.0
rich>=13.0.0

# Already have these via your setup
# PubMed MCP - configured in .mcp.json
# Perplexity - via OpenRouter or MCP

API KEYS NEEDED

KeyPurposeStatus
ANTHROPIC_API_KEYSynthesisAlready have
NCBI_API_KEYPubMed (via MCP)Already have
PERPLEXITY_API_KEYWeb searchAlready have

HOW CLAUDE SHOULD USE THIS SKILL

When user asks for literature/evidence:

Step 1: Parse the Query

Extract:

  • Main topic
  • Specific aspects (population, intervention, outcome)
  • Time frame (if mentioned)

Step 2: Launch Parallel Searches

# PubMed (via MCP)
pubmed_search_articles(queryTerm="SGLT2 inhibitors heart failure", maxResults=10)

# Perplexity (via MCP or API)
perplexity_ask(messages=[{"role": "user", "content": "Latest evidence on SGLT2 inhibitors in heart failure 2024"}])

# RAG (if available)
# Query AstraDB for relevant guidelines

Step 3: Synthesize Results

Combine findings from all sources into:

  • Key takeaways
  • Evidence quality assessment
  • Complete citation list
  • Gaps and considerations

Step 4: Format Output

Structured report with:

  • Executive synthesis
  • Source-by-source findings
  • Full citations
  • Actionable insights

CLINICAL QUESTION OPTIMIZATION

The skill recognizes PICO format:

ComponentExampleHow It's Used
Patient"elderly patients with HFpEF"Filters PubMed
Intervention"SGLT2 inhibitors"Primary search term
Comparison"vs placebo"Narrows to RCTs
Outcome"mortality"Focuses results

SAMPLE QUERIES

# Basic clinical question
"SGLT2 inhibitors in heart failure"

# PICO format
"In elderly patients with HFpEF, do SGLT2 inhibitors reduce mortality compared to placebo?"

# Specific trial
"What are the key findings from EMPEROR-Preserved?"

# Guideline-focused
"Current ACC/AHA recommendations for SGLT2i in heart failure"

# Comparative
"SGLT2i vs GLP-1 for cardiovascular outcomes in diabetes"

NOTES

  • Speed: Parallel search takes 30-60 seconds vs 5+ minutes sequential
  • Depth: Good for overview, not exhaustive systematic review
  • Citations: Always includes PMIDs for verification
  • Updates: Perplexity provides most recent web data

*This skill gives you evidence from multiple sources in under a minute - perfect for content preparation and quick clinical questions.*

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

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

Claude Code

27.96%
按下载量换算44

OpenCode

24.48%
按下载量换算39

Antigravity

18.79%
按下载量换算30

Gemini CLI

13.69%
按下载量换算22

windsurf

7.76%
按下载量换算12

Codex

3.13%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

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。

来源信息

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