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Cardiology Visual System

drshailesh88/integrated_content_os

  • 40 installs
  • 5 repo stars
  • Updated June 18, 2026
  • drshailesh88/integrated_content_os

Route cardiology visual requests to the right tool: Fal.ai for imagery, Gemini for infographics, Mermaid for diagrams, Marp for slides, Plotly for charts.

About

This meta-skill routes cardiology visual-content requests to the optimal tool, from Fal.ai imagery and Gemini infographics to Mermaid diagrams, Marp slides, and Plotly charts. Cardiology content creators use it to handle all visual needs within Claude Code.

  • Automatic routing by request keywords to the best visual tool
  • Covers images, infographics, flowcharts, slides, and data visualization

Cardiology Visual System by the numbers

  • 40 all-time installs (skills.sh)
  • Ranked #929 of 1,337 Generative Media skills by installs in the Skillselion catalog
  • Data as of Jul 29, 2026 (Skillselion catalog sync)
npx skills add https://github.com/drshailesh88/integrated_content_os --skill cardiology-visual-system

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Installs40
repo stars★ 5
Last updatedJune 18, 2026
Repositorydrshailesh88/integrated_content_os ↗

What it does

Route cardiology visual requests to the right tool: Fal.ai for imagery, Gemini for infographics, Mermaid for diagrams, Marp for slides, Plotly for charts.

Files

SKILL.mdMarkdownGitHub ↗

Cardiology Visual System

A unified meta-skill that intelligently routes visual content requests to the optimal tool. No more switching between Napkin.ai, NotebookLM, or other subscriptions—everything happens in Claude Code.

Quick Reference: What Tool Does What

You Ask ForTool UsedOutput
Blog header, lifestyle photo, patient scenarioFal.aiPNG image
Infographic, explainer graphic, medical illustrationGeminiPNG/JPG image
Flowchart, treatment algorithm, clinical pathwayMermaidSVG/PNG diagram
Slide deck, presentationMarpPPTX/PDF/HTML slides
Data chart, trial results, trends over timePlotlyInteractive HTML or PNG
Interactive explainer, dashboardReact ArtifactInteractive HTML
Quick visualization prototype, exploratory data vizLIDA ⚠️PNG + Code (prototype only)

Automatic Routing Logic

When you ask for visuals, I determine the best tool by analyzing your request:

→ Route to Fal.ai (Stock/Human Imagery)

Keywords: blog image, header, hero image, lifestyle, patient photo, stock, person, family, emotional, scenario

Best for:

  • Blog post headers
  • Patient experience illustrations
  • Lifestyle/wellness imagery
  • Emotional/human-centered scenes
  • Recovery and hope imagery

NOT for: Medical devices, ECGs, diagrams, data, or text-heavy content

→ Route to Gemini (Infographics & Medical Illustrations)

Keywords: infographic, explainer, illustration, visual summary, concept diagram, icons, steps, process visual, simplified, educational graphic

Best for:

  • Infographics (like Napkin.ai produces)
  • Medical concept illustrations
  • Simplified explainer graphics
  • Educational visuals with icons
  • Text-in-image content
  • Visual summaries of articles

→ Route to Mermaid (Diagrams & Flowcharts)

Keywords: flowchart, algorithm, pathway, decision tree, sequence, timeline, process flow, treatment algorithm, diagnostic pathway, workflow

Best for:

  • Clinical decision trees
  • Treatment algorithms
  • Diagnostic pathways
  • Process workflows
  • Organizational charts
  • Sequence diagrams
  • Gantt charts for timelines

→ Route to Marp (Slide Decks)

Keywords: slides, presentation, deck, powerpoint, lecture, talk, keynote

Best for:

  • Conference presentations
  • Educational lectures
  • Patient education slides
  • Grand rounds presentations
  • CME content

→ Route to Plotly (Data Visualization)

Keywords: chart, graph, plot, data, statistics, trial results, forest plot, trends, comparison, survival curve, Kaplan-Meier, bar chart, line graph, scatter

Best for:

  • Clinical trial results
  • Statistical comparisons
  • Trends over time
  • Forest plots
  • Survival curves
  • Before/after data
  • Multi-study comparisons

→ Route to React Artifact (Interactive)

Keywords: interactive, dashboard, calculator, tool, widget, animated, explorable

Best for:

  • Risk calculators
  • Interactive explainers
  • Animated diagrams
  • Patient education tools
  • Explorable explanations

→ Route to LIDA (Quick Prototyping) ⚠️ PROTOTYPING ONLY

Keywords: quick, prototype, exploratory, rough draft, multiple options, try, experiment, brainstorm visualization

⚠️ CRITICAL LIMITATIONS:

  • PROTOTYPING ONLY - NOT for publication or patient-facing materials
  • Quality varies - ALWAYS review for medical accuracy
  • Works best with ≤10 data columns
  • No specialized medical charts (true forest plots, Kaplan-Meier)
  • Requires manual validation before ANY use

Best for:

  • Quick exploratory data visualization ("what does this data show?")
  • Generating multiple visualization candidates
  • Brainstorming chart types for new data
  • Internal research review only

NOT for:

  • Publication-ready charts → Use Plotly instead
  • Patient-facing materials → Use production tools
  • Regulatory submissions → Never
  • Final blog posts → Use Plotly or Gemini

When in doubt, use Plotly for data visualization instead of LIDA.

---

Tool 1: Fal.ai (Blog Imagery)

Setup

export FAL_KEY="your-fal-api-key"
# Get key from: https://fal.ai/dashboard/keys

Usage

python scripts/fal_image.py "A 55-year-old man experiencing chest pain at work" --output hero.png

Models

ModelCostBest For
fal-ai/recraft-v3$0.04Default - Best quality
fal-ai/flux-pro/v1.1$0.04Photorealism
fal-ai/flux/schnell$0.003Fast/cheap drafts

What to Generate vs Not Generate

✅ GENERATE:

  • Patient symptoms/experiences (chest pain, shortness of breath)
  • Lifestyle scenes (exercise, healthy cooking)
  • Doctor-patient conversations
  • Family support moments
  • Recovery celebrations

❌ DO NOT GENERATE:

  • Medical devices (pacemakers, stents, valves)
  • Clinical imagery (ECGs, angiograms, OR scenes)
  • Anatomical diagrams
  • Medications

---

Tool 2: Gemini (Infographics)

Setup

export GEMINI_API_KEY="your-gemini-api-key"

Usage

python scripts/gemini_infographic.py \
  --topic "Heart Failure Stages" \
  --style "minimalist medical" \
  --output hf_stages.jpg

Prompting for Medical Infographics

Structure your prompt:

Create a [STYLE] infographic showing [TOPIC].

Include:
- [Key point 1]
- [Key point 2]
- [Key point 3]

Style: [clean/minimalist/modern medical], use icons, clear hierarchy, 
professional color palette (blues, teals for medical), easy to read text

Example prompts:

1. Disease Progression:

"Create a minimalist medical infographic showing the 4 stages of heart failure (A, B, C, D). Use icons for each stage, show progression with arrows, include brief descriptions. Clean layout, medical blue color scheme."

2. Treatment Comparison:

"Create an infographic comparing medication vs intervention for AFib. Two columns, icons for each approach, bullet points for pros/cons. Modern medical style."

3. Risk Factor Summary:

"Create a visual summary of 7 modifiable risk factors for heart disease. Icon for each factor, clean grid layout, actionable tips. Professional medical illustration style."

---

Tool 3: Mermaid (Diagrams)

You have Mermaid Chart MCP connected. Use it for structured diagrams.

Common Cardiology Diagram Types

1. Treatment Algorithm:

flowchart TD
    A[Acute Chest Pain] --> B{STEMI?}
    B -->|Yes| C[Primary PCI < 90 min]
    B -->|No| D{High-risk NSTEMI?}
    D -->|Yes| E[Early invasive < 24h]
    D -->|No| F[Ischemia-guided strategy]

2. Clinical Pathway:

flowchart LR
    A[Diagnosis] --> B[Risk Stratification]
    B --> C[Treatment Selection]
    C --> D[Follow-up Protocol]

3. Diagnostic Decision Tree:

flowchart TD
    A[Dyspnea] --> B{BNP elevated?}
    B -->|Yes| C{Echo findings?}
    B -->|No| D[Consider other causes]
    C -->|HFrEF| E[GDMT initiation]
    C -->|HFpEF| F[Diuretics + address comorbidities]

4. Timeline (Gantt):

gantt
    title Post-MI Care Timeline
    dateFormat  YYYY-MM-DD
    section Acute
    Hospital stay        :a1, 2024-01-01, 5d
    section Recovery
    Cardiac rehab        :a2, after a1, 12w
    section Long-term
    Medication titration :a3, after a1, 6m

---

Tool 4: Marp (Slides)

Setup

npm install -g @marp-team/marp-cli

Usage

1. I write Markdown with Marp syntax 2. Save as presentation.md 3. Convert:

marp presentation.md --pptx           # PowerPoint
marp presentation.md --pdf            # PDF
marp presentation.md -o slides.html   # HTML

Marp Template for Medical Slides

---
marp: true
theme: default
paginate: true
backgroundColor: #ffffff
color: #333333
---

# Heart Failure Management
## Modern Approaches in 2024

Dr. [Your Name]
Interventional Cardiology

---

# Agenda

1. Current Guidelines
2. New Therapies
3. Case Discussion

---

# Key Statistics

- 6.7 million Americans with HF
- 50% mortality at 5 years
- $30.7 billion annual cost

![bg right:40%](path/to/chart.png)

---

# Treatment Algorithm

flowchart TD A[HFrEF Diagnosis] --> B[GDMT Initiation] B --> C[Titrate to target doses]


---

# Take-Home Points

1. Early initiation matters
2. Quadruple therapy is standard
3. Device therapy in appropriate patients

---

# Questions?

Contact: your@email.com

---

Tool 5: Plotly (Data Visualization)

Setup

pip install plotly kaleido pandas --break-system-packages

Common Medical Visualizations

1. Bar Chart (Trial Results):

import plotly.express as px

data = {
    'Treatment': ['Drug A', 'Drug B', 'Placebo'],
    'Event Rate (%)': [12.3, 15.1, 18.7]
}
fig = px.bar(data, x='Treatment', y='Event Rate (%)',
             title='Primary Endpoint: Major Cardiovascular Events',
             color='Treatment')
fig.write_html('trial_results.html')
fig.write_image('trial_results.png')

2. Forest Plot Style:

import plotly.graph_objects as go

studies = ['PARADIGM-HF', 'DAPA-HF', 'EMPEROR-Reduced']
hr = [0.80, 0.74, 0.75]
lower = [0.73, 0.65, 0.65]
upper = [0.87, 0.85, 0.86]

fig = go.Figure()
for i, study in enumerate(studies):
    fig.add_trace(go.Scatter(
        x=[lower[i], upper[i]], y=[study, study],
        mode='lines', line=dict(color='gray', width=2)
    ))
    fig.add_trace(go.Scatter(
        x=[hr[i]], y=[study],
        mode='markers', marker=dict(size=12, color='navy')
    ))

fig.add_vline(x=1.0, line_dash="dash", line_color="red")
fig.update_layout(title='Hazard Ratios for Heart Failure Trials',
                  xaxis_title='Hazard Ratio (95% CI)')

3. Trend Over Time:

import plotly.express as px

fig = px.line(df, x='Year', y='Mortality Rate', 
              color='Treatment Era',
              title='Heart Failure Mortality Trends 1990-2024')
fig.write_html('trends.html')

---

Tool 6: LIDA (Quick Prototyping) ⚠️ PROTOTYPING ONLY

⚠️ Critical Warning

LIDA is a PROTOTYPING TOOL ONLY. Do NOT use for:

  • Publication-ready visualizations
  • Patient-facing materials
  • Regulatory submissions
  • Final blog posts or social media

For production visualizations, use:

  • Plotly (data charts)
  • Gemini (infographics)
  • Fal.ai (images)

Setup

# Install LIDA
pip install lida llmx openai --break-system-packages

# Set API key (choose one)
export OPENAI_API_KEY="your-key"      # Recommended
export GOOGLE_API_KEY="your-key"      # Free tier (Gemini)
export ANTHROPIC_API_KEY="your-key"   # Claude

What LIDA Does

LIDA (Automatic Visualization Generation) uses LLMs to: 1. Analyze your data structure 2. Generate visualization code from natural language 3. Create multiple visualization candidates 4. Support multiple libraries (Plotly, Matplotlib, Seaborn, Altair)

Published: ACL 2023 (Microsoft Research)

Limitations

1. ≤10 columns recommended - LLM context constraints 2. Quality varies - AI-generated, needs review 3. No specialized medical charts - No true forest plots, Kaplan-Meier 4. Error rate: <3.5% reported, but ALWAYS verify medical accuracy 5. Not production-ready - Use for exploration only

Usage

Basic Usage:

python scripts/lida_quick_viz.py "Show mortality by treatment group" trial_data.csv

Multiple Candidates:

python scripts/lida_quick_viz.py "Compare outcomes" data.csv --candidates 3

Use Medical Template:

python scripts/lida_quick_viz.py "Trial results" data.csv --template trial_comparison

Specify Library:

python scripts/lida_quick_viz.py "Trends over time" data.csv --library plotly

Interactive Mode:

python scripts/lida_quick_viz.py --interactive data.csv

List Templates:

python scripts/lida_quick_viz.py --list-templates

Medical Templates

TemplateDescriptionUse Case
trial_comparisonCompare treatment armsPrimary endpoint results
patient_demographicsBaseline characteristicsPatient population summary
outcome_comparisonPrimary/secondary endpointsMultiple outcomes comparison
trend_analysisTrends over timeLongitudinal data
survival_curveTime-to-event (simplified)Event-free survival (NOT true KM)

Example Workflow

1. Exploratory Analysis (LIDA):

# Quick exploration of new trial data
python scripts/lida_quick_viz.py \
  "Show primary endpoint by treatment arm with confidence intervals" \
  trial_results.csv \
  --template trial_comparison \
  --candidates 3

2. Review Candidates:

  • Check medical accuracy
  • Verify data interpretation
  • Select best approach

3. Production Version (Plotly):

# Recreate selected visualization in production quality
python scripts/plotly_charts.py bar --data trial_results.csv --output final_chart.png

Quality Validation Checklist

Every LIDA output includes this checklist:

⚠️  QUALITY VALIDATION CHECKLIST - REVIEW BEFORE USE

Medical Accuracy:
[ ] Data interpretation is correct
[ ] Statistical measures are appropriate
[ ] Confidence intervals/error bars are correct
[ ] P-values and significance are accurate
[ ] Sample sizes are represented correctly

Visual Design:
[ ] Chart type is appropriate
[ ] Color scheme is professional
[ ] Labels are clear and complete
[ ] Legend is accurate
[ ] Title describes the content

Medical Standards:
[ ] Follows publication standards
[ ] No misleading visualizations
[ ] Appropriate precision
[ ] Context is provided
[ ] Source attribution if needed

When to Use LIDA vs Plotly

ScenarioToolReason
"What's the best way to show this data?"LIDAExploration, multiple options
"Show me trial results quickly"LIDASpeed over perfection
"Generate 3 chart options"LIDAMultiple candidates
"Publication-ready chart"PlotlyProduction quality
"Blog post visualization"PlotlyFinal output
"Patient education"PlotlyAccuracy critical
"Regulatory submission"PlotlyNever use LIDA

Cost

ModelCostSpeedQuality
OpenAI (GPT-4o-mini)$0.60/M tokensFastGood
Gemini (Free tier)FREEFastGood
Claude (Sonnet)VariableMediumExcellent

Typical cost per visualization: $0.01-0.05 (negligible)

Example: Interactive Session

python scripts/lida_quick_viz.py --interactive trial_data.csv

# In interactive mode:
> list                              # Show templates
> template trial_comparison         # Set template
> library plotly                    # Set library
> viz Show mortality by treatment   # Generate viz
> viz Compare age distribution      # Another viz
> quit

Output Structure

lida_output/
├── candidate_1_code.py           # Generated Python code
├── candidate_1.png                # Rendered visualization
├── candidate_2_code.py
├── candidate_2.png
└── ...

---

Integrated Workflow Example

Scenario: You're writing a blog post about heart failure medications.

Step 1: Hero Image (Fal.ai)

python scripts/fal_image.py \
  "Elderly patient having hopeful conversation with cardiologist about new treatment options" \
  --output images/hero.png

Step 2: Treatment Algorithm (Mermaid)

Create a Mermaid flowchart showing HFrEF GDMT initiation:
- Start with diagnosis
- Branch to ARNI/ACEi + Beta-blocker
- Add SGLT2i + MRA
- Device consideration

Step 3: Trial Data (Plotly)

# Compare mortality reduction across trials
trials_df = pd.DataFrame({
    'Trial': ['PARADIGM-HF', 'DAPA-HF', 'EMPEROR-Reduced'],
    'Mortality Reduction': [20, 17, 14]
})
fig = px.bar(trials_df, x='Trial', y='Mortality Reduction',
             title='Mortality Reduction in Landmark HF Trials (%)')

Step 4: Key Concepts Infographic (Gemini)

Create a clean medical infographic summarizing the "4 Pillars of HFrEF Therapy":
1. ARNI/ACEi - heart icon
2. Beta-blocker - heart rate icon  
3. MRA - kidney/electrolyte icon
4. SGLT2i - glucose/kidney icon

Style: modern medical, blue color scheme, minimal text, icon-focused

---

API Key Checklist

Before using this system, ensure these are set:

# Fal.ai (blog images)
export FAL_KEY="your-key"

# Gemini (infographics)
export GEMINI_API_KEY="your-key"

# LIDA (prototyping) - choose one
export OPENAI_API_KEY="your-key"      # Recommended
export GOOGLE_API_KEY="your-key"      # FREE (Gemini)
export ANTHROPIC_API_KEY="your-key"   # Claude

# Mermaid - uses MCP, no key needed
# Plotly - local, no key needed
# Marp - local, no key needed

---

Cost Summary

ToolCostTypical Use
Fal.ai (Recraft)$0.04/image3-4 per blog = $0.16
GeminiFree tier availableInfographics
LIDA (prototyping)$0.01-0.05/vizQuick exploration (optional)
MermaidFree (MCP)Diagrams
PlotlyFreeData viz
MarpFreeSlides

Total per blog post: ~$0.16-0.25 (vs separate subscriptions)

Note: LIDA is optional - use FREE Gemini model for zero-cost prototyping

---

Files in This Skill

cardiology-visual-system/
├── SKILL.md                    # This file
├── scripts/
│   ├── fal_image.py           # Fal.ai image generation
│   ├── gemini_infographic.py  # Gemini infographic generation
│   ├── plotly_charts.py       # Common chart templates
│   ├── lida_quick_viz.py      # LIDA prototyping (⚠️ prototype only)
│   └── convert_slides.sh      # Marp conversion helper
├── templates/
│   ├── marp_medical.md        # Medical slide template
│   └── plotly_medical.py      # Medical chart templates
└── references/
    └── prompt_examples.md     # Curated prompts for each tool

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