Agent skill

quick-topic-researcher

Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content.

Stars 2
Forks 0

Install this agent skill to your Project

npx add-skill https://github.com/drshailesh88/integrated_content_OS/tree/main/skills/cardiology/quick-topic-researcher

SKILL.md

Quick Topic Researcher

5 minutes to topic mastery. This skill generates a focused research brief you can use immediately before recording a video or writing content.

Different from deep-researcher: That skill is comprehensive (5+ sources, file-based, 30+ minutes). This skill is FAST (5 questions, parallel search, 5 minutes).


When to Use

Use Case This Skill
Prepping for a YouTube video Yes
Writing a quick tweet thread Yes
Refreshing knowledge on a topic Yes
Before a podcast discussion Yes
Comprehensive literature review No → Use deep-researcher
Writing a formal editorial No → Use deep-researcher first

How It Works

TOPIC: "GLP-1 agonists in heart failure"
DOMAIN: "Cardiology"

    │
    ▼
┌─────────────────────────────────────────────────────┐
│ STEP 1: Generate 5 Research Questions               │
│                                                      │
│ 1. Do GLP-1 agonists reduce heart failure           │
│    hospitalization in diabetic patients?            │
│ 2. Is there evidence of direct cardiac benefit?     │
│ 3. What are the key trials showing CV outcomes?     │
│ 4. Are there safety concerns in existing HF?        │
│ 5. What do current guidelines recommend?            │
└─────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────┐
│ STEP 2: Parallel Research (5 searches at once)      │
│                                                      │
│ [PubMed Q1] [PubMed Q2] [PubMed Q3] [Perplexity Q4] │
│ [Perplexity Q5]                                      │
│                                                      │
│ ~30 seconds total                                    │
└─────────────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────────────┐
│ STEP 3: McKinsey-Style Brief                         │
│                                                      │
│ EXECUTIVE SUMMARY                                    │
│ • Key finding with strongest PMID                   │
│                                                      │
│ ANALYSIS                                             │
│ • Theme 1: Trial evidence (PMIDs)                   │
│ • Theme 2: Mechanisms (PMIDs)                       │
│ • Theme 3: Guidelines                               │
│                                                      │
│ CLINICAL IMPLICATIONS                                │
│ • What this means for your content                  │
│                                                      │
│ KEY PMIDS TO CITE                                    │
│ • List of 5-7 citation-ready references             │
└─────────────────────────────────────────────────────┘

Usage

Interactive Mode (Recommended)

Ask Claude:

Use quick-topic-researcher for [TOPIC] in [DOMAIN]

Example:

Use quick-topic-researcher for "SGLT2 inhibitors in CKD" in "Cardiology/Nephrology"

CLI Mode (Coming Soon)

bash
python skills/cardiology/quick-topic-researcher/scripts/quick_research.py \
    --topic "GLP-1 agonists in heart failure" \
    --domain "Cardiology"

Research Sources

Primary (Citable)

Source Tool Purpose
PubMed MCP pubmed_search_articles, pubmed_fetch_contents All medical evidence
Guidelines Direct URL fetch to ACC/ESC/ADA Recommendations

Discovery (Not Citable)

Source Tool Purpose
Perplexity perplexity_ask via MCP Quick context, trend discovery
Web Search WebSearch Background, non-medical context

Rule: You can USE Perplexity to understand context, but you CITE only PubMed.


Output Format

The skill outputs a structured brief:

markdown
# Quick Research Brief: [TOPIC]

**Domain:** [DOMAIN]
**Generated:** [DATE]
**Time to Read:** 3 minutes

---

## Executive Summary

[2-3 sentences: What you need to know before recording/writing]

Key takeaway: [ONE sentence with strongest PMID]

---

## Research Questions & Findings

### Q1: [Question]
**Answer:** [Concise answer]
**Evidence:** [Study name, PMID, key stat (HR, CI, p-value)]

### Q2: [Question]
**Answer:** [Concise answer]
**Evidence:** [Study name, PMID, key stat]

[... Q3-Q5 ...]

---

## Clinical Context

### What Guidelines Say
[ACC/ESC/ADA recommendations with class/level]

### Practice Implications
[What this means for real patients]

---

## Content Hooks

For your video/content, consider these angles:
1. [Hook 1 based on findings]
2. [Hook 2 based on findings]
3. [Hook 3 based on findings]

---

## Citation-Ready References

1. Author A, et al. TRIAL-NAME. Journal. Year. PMID: XXXXXXXX
2. Author B, et al. Study Name. Journal. Year. PMID: XXXXXXXX
3. [5-7 total references]

---

## Gaps & Caveats

- [What the evidence doesn't cover]
- [Patient populations with limited data]

Integration with Content Pipeline

Before YouTube Script

1. Run quick-topic-researcher on your video topic
2. Review the brief (3 min read)
3. Open youtube-script-master with the brief in context
4. Script now has verified facts and PMIDs

Before Tweet Thread

1. Run quick-topic-researcher
2. Pick 1-2 key findings from the brief
3. Use x-post-creator-skill with specific PMIDs

Before Newsletter

1. Run quick-topic-researcher for quick take
2. If topic is complex, follow up with deep-researcher
3. Write with cardiology-newsletter-writer

Example Session

User: Use quick-topic-researcher for "colchicine in cardiovascular prevention" in "Cardiology"

Claude:

I'll research this topic rapidly using PubMed and generate a brief.

Step 1: Generating 5 research questions...

  1. Does colchicine reduce major cardiovascular events in CAD patients?
  2. What are the key RCTs (COLCOT, LoDoCo2, CLEAR SYNERGY)?
  3. What is the proposed anti-inflammatory mechanism?
  4. What are the safety concerns and contraindications?
  5. What do current guidelines recommend?

Step 2: Parallel research...

[Executes 5 PubMed searches in parallel] [Fetches key article abstracts] [Searches Perplexity for context]

Step 3: Generating McKinsey-style brief...


Quick Research Brief: Colchicine in CV Prevention

Domain: Cardiology Generated: 2026-01-01 Time to Read: 3 minutes

Executive Summary

Low-dose colchicine (0.5mg daily) reduces cardiovascular events in patients with established coronary artery disease through anti-inflammatory mechanisms. COLCOT and LoDoCo2 showed consistent benefit; CLEAR SYNERGY was neutral but in a different population.

Key takeaway: In stable CAD patients, colchicine 0.5mg daily reduces CV events by ~25% (HR 0.69-0.77) (PMID: 31733140, 32865377)

Research Questions & Findings

Q1: Does colchicine reduce major CV events?

Answer: Yes, in stable CAD Evidence: LoDoCo2: HR 0.69 (0.57-0.83), p<0.001 for CV death/MI/stroke (PMID: 32865377)

Q2: What are the key trials?

Answer: COLCOT (post-MI), LoDoCo2 (chronic CAD), CLEAR SYNERGY (post-PCI) Evidence:

  • COLCOT: 0.5mg, HR 0.77 for composite (PMID: 31733140)
  • LoDoCo2: 0.5mg, HR 0.69 for composite (PMID: 32865377)
  • CLEAR SYNERGY: Neutral for primary endpoint (PMID: 37634428)

Q3: What is the mechanism?

Answer: NLRP3 inflammasome inhibition, IL-1β reduction Evidence: Reduces hsCRP; targets residual inflammatory risk (PMID: 29724838)

Q4: Safety concerns?

Answer: GI side effects, myopathy with statins, CKD dose adjustment Evidence: NNH for GI: ~50; avoid with strong CYP3A4 inhibitors (PMID: 31733140)

Q5: Guidelines?

Answer: Class IIb in ESC 2024 for chronic CAD Evidence: ESC 2024 CCS Guidelines, consider in patients with elevated hsCRP

Clinical Context

What Guidelines Say

ESC 2024: Class IIb, Level A - "Colchicine 0.5mg may be considered to reduce CV risk in chronic coronary syndrome"

Practice Implications

  • Best candidates: Stable CAD with elevated hsCRP despite optimal therapy
  • Less certain: Immediately post-MI (COLCOT positive, but smaller effect)
  • Probably not: Post-PCI without other CAD (CLEAR SYNERGY neutral)

Content Hooks

  1. "The inflammation hypothesis finally has a drug - and it's ancient"
  2. "0.5mg for 50 cents - the cheapest CV prevention we've ignored"
  3. "Why the cardiologist's gout drug became a heart drug"

Citation-Ready References

  1. Tardif JC, et al. COLCOT. NEJM. 2019. PMID: 31733140
  2. Nidorf SM, et al. LoDoCo2. NEJM. 2020. PMID: 32865377
  3. Jolly SS, et al. CLEAR SYNERGY. NEJM. 2024. PMID: 37634428
  4. Ridker PM. Residual inflammatory risk. JACC. 2018. PMID: 29724838
  5. ESC CCS Guidelines 2024

Gaps & Caveats

  • No RCT in primary prevention (only secondary prevention studied)
  • Unclear benefit in patients with normal hsCRP
  • Long-term safety (>3 years) data limited
  • CLEAR SYNERGY challenges universal applicability

Ready for your video! You now have verified facts, PMIDs, and content hooks.


Comparison with Other Skills

Skill Time Depth Use Case
quick-topic-researcher 5 min Surface + key trials Video prep, quick refresh
deep-researcher 30-60 min Comprehensive Editorials, literature review
pubmed-database 2 min Single search Specific question
perplexity-search 1 min Trend only Discovery, non-citable

Technical Implementation

Dependencies

  • PubMed MCP (existing)
  • Perplexity MCP (existing)
  • Claude (default model)

Parallel Execution

The skill uses Claude's ability to make multiple tool calls simultaneously:

python
# These run in parallel (single message, multiple tool calls)
pubmed_search_articles(queryTerm="colchicine cardiovascular RCT", maxResults=10)
pubmed_search_articles(queryTerm="colchicine mechanism inflammation", maxResults=5)
perplexity_ask(messages=[{"role": "user", "content": "colchicine cardiology guidelines 2024"}])

Output

  • Markdown brief (displayed in terminal)
  • Optional: Save to ~/research_briefs/{topic}_{date}.md

This skill gets you from "I need to know about X" to "I can confidently speak about X" in 5 minutes.

Expand your agent's capabilities with these related and highly-rated skills.

drshailesh88/integrated_content_OS

pufferlib

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

2 0
Explore
drshailesh88/integrated_content_OS

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

2 0
Explore
drshailesh88/integrated_content_OS

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2 0
Explore
drshailesh88/integrated_content_OS

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

2 0
Explore
drshailesh88/integrated_content_OS

zinc-database

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

2 0
Explore
drshailesh88/integrated_content_OS

astropy

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

2 0
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results