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

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

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

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

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

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]

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

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]

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

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

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

---
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

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

---

## Tool 5: Plotly (Data Visualization)

### Setup
```bash
pip install plotly kaleido pandas --break-system-packages

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')

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)')

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')

# 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

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

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

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

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

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

python scripts/lida_quick_viz.py --list-templates

# 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

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

⚠️  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

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

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

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

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

# 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 (%)')

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

# 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

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