Agent skill

parallel-literature-search

Parallel search across PubMed, Perplexity, and your knowledge base. Searches all sources simultaneously and synthesizes findings with citations. Faster evidence gathering for clinical questions.

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npx add-skill https://github.com/drshailesh88/integrated_content_OS/tree/main/skills/cardiology/parallel-literature-search

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

Source What It Searches Output
PubMed Academic literature, trials, reviews PMIDs, abstracts, citations
Perplexity Web, recent news, guidelines Summaries with sources
RAG (AstraDB) Your curated knowledge base Guideline excerpts, textbook refs

THE DIFFERENCE

Approach Sources Time Depth
Sequential search One at a time 5+ min Deeper but slow
Parallel search All at once 30-60 sec Fast overview
Manual search You do it 20+ min Variable

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

bash
# 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

markdown
# 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

python
# 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

Key Purpose Status
ANTHROPIC_API_KEY Synthesis Already have
NCBI_API_KEY PubMed (via MCP) Already have
PERPLEXITY_API_KEY Web search Already 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

python
# 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:

Component Example How 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.

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