Shot Scraper

shot-scraper

Quick Start

Extract data by executing JavaScript on a webpage and save as JSON:

# Install
pip install shot-scraper sqlite-utils
shot-scraper install

# Scrape data
shot-scraper javascript https://example.com/news "({
  articles: Array.from(document.querySelectorAll('article')).map(a => ({
    title: a.querySelector('h2')?.innerText,
    link: a.querySelector('a')?.href,
    date: a.querySelector('.date')?.innerText
  })),
  timestamp: new Date().toISOString()
})" -o data.json

# Import to SQLite (Datasette format)
sqlite-utils insert data.db articles data.json --pk=link --alter

Core Workflow

1. Write JavaScript extractor - Returns JSON object or array
2. Run via GitHub Actions - Scheduled or on-demand
3. Import to SQLite - Use sqlite-utils for Datasette compatibility
4. Commit results - Track data changes over time

JavaScript Execution

Basic extraction

shot-scraper javascript URL "document.title"
shot-scraper javascript URL -i script.js -o output.json

Return JSON objects

// Wrap object literals in parentheses
({
  title: document.title,
  items: Array.from(document.querySelectorAll('.item')).map(i => i.innerText)
})

Handle dynamic content

new Promise(done => {
  setTimeout(() => {
    done({ data: document.querySelector('.dynamic')?.innerText });
  }, 2000);
});

Common options

• --wait MILLISECONDS - Wait before executing
• --timeout MILLISECONDS - Max execution time
• --auth FILE - Authentication context
• --user-agent STRING - Custom user agent
• --log-console - Show console.log output

GitHub Actions Integration

Basic scheduled scraper workflow:

name: Scrape Website
on:
  schedule:
    - cron: '0 6 * * *'  # Daily at 6 AM
  workflow_dispatch:

jobs:
  scrape:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/​checkout@v4
      - uses: actions/​setup-python@v5
        with:
          python-version: '3.12'
      
      - name: Install tools
        run: |
          pip install shot-scraper sqlite-utils
          shot-scraper install
      
      - name: Scrape data
        run: |
          shot-scraper javascript https://example.com \
            -i scrape.js \
            -o data/​output-$(date +%Y%m%d).json
      
      - name: Import to database
        run: |
          sqlite-utils insert data/​scraper.db records \
            data/​output-*.json \
            --pk=id --alter
      
      - name: Commit results
        run: |
          git config user.name "Bot"
          git config user.email "[email protected]"
          git add data/
          git commit -m "Update $(date +%Y-%m-%d)" || exit 0
          git push

Data Export Patterns

Single record per run

({ timestamp: new Date().toISOString(), total: document.querySelectorAll('.item').length })

sqlite-utils insert data.db snapshots output.json --alter

Multiple records per run

Array.from(document.querySelectorAll('.product')).map(p => ({
  id: p.dataset.id,
  name: p.querySelector('.name')?.innerText,
  price: parseFloat(p.querySelector('.price')?.innerText.replace(/[^0-9.]/​g, '')),
  timestamp: new Date().toISOString()
}))

sqlite-utils insert data.db products output.json --pk=id --replace

With nested data

# Flatten nested objects
sqlite-utils insert data.db items output.json --flatten --alter

Common Patterns

Extract tables:

Array.from(document.querySelectorAll('table tr')).map(row => {
  const cells = row.querySelectorAll('td');
  return { col1: cells[0]?.innerText, col2: cells[1]?.innerText };
})

Parse prices/​numbers:

parseFloat(text.replace(/[^0-9.]/​g, ''))

Wait for element:

new Promise(done => {
  const check = setInterval(() => {
    const el = document.querySelector('.target');
    if (el) { clearInterval(check); done({ data: el.innerText }); }
  }, 100);
  setTimeout(() => { clearInterval(check); done({ error: 'timeout' }); }, 10000);
});

sqlite-utils Commands

# Insert with primary key
sqlite-utils insert db.db table data.json --pk=id

# Replace existing records
sqlite-utils insert db.db table data.json --pk=id --replace

# Auto-alter schema to fit new data
sqlite-utils insert db.db table data.json --alter

# Create indexes for Datasette
sqlite-utils create-index db.db table column_name

# Query data
sqlite-utils query db.db "SELECT * FROM table" --csv

Advanced Topics

Authentication: Store auth.json as GitHub secret, write to file in workflow:

env:
  AUTH_JSON: ${{ secrets.AUTH_JSON }}
run: |
  echo "$AUTH_JSON" > auth.json
  shot-scraper javascript URL -a auth.json -i script.js -o data.json

Error handling: Add retry logic to workflow (see workflows.md)

Multiple pages: Process URL list (see examples.md)

Screenshots: Take alongside data for verification:

shot-scraper URL -o screenshot.png
shot-scraper javascript URL -i script.js -o data.json

References

• examples.md - Complete workflow examples (price tracking, monitoring, error handling)
• scraping-patterns.md - JavaScript patterns for common scraping scenarios
• workflows.md - Advanced GitHub Actions patterns

External Resources

• shot-scraper: https://github.com/simonw/shot-scraper
• sqlite-utils: https://sqlite-utils.datasette.io/
• Datasette: https://datasette.io/