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",
  "definition": {
    "version": "0.1.0",
    "name": "Signal Garden",
    "description": "A botanical visualization of real earthquake records. Remap stem height, bloom size, and petal color; inspect each specimen or reuse the dataset. Run to refresh. No API key required.",
    "nodes": [
      {
        "node_instance_id": "feed_url",
        "node_id": "n_url_input",
        "node_version": "1.0.0",
        "title": "USGS / past day / M2.5+",
        "params": {
          "url": "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_day.geojson"
        }
      },
      {
        "node_instance_id": "fetch",
        "node_id": "n_fetch_url",
        "node_version": "1.0.0",
        "title": "Fetch earthquake snapshot",
        "params": {
          "method": "GET",
          "headers": {
            "Accept": "application/geo+json, application/json"
          },
          "timeout_seconds": 30,
          "max_response_bytes": 5000000
        },
        "exposed_inputs": [
          "url"
        ],
        "inputs_visibility_configured": true,
        "exposed_outputs": [
          "body",
          "status"
        ],
        "outputs_visibility_configured": true
      },
      {
        "node_instance_id": "parse",
        "node_id": "n_json_parse",
        "node_version": "1.0.0",
        "title": "Parse GeoJSON snapshot",
        "params": {}
      },
      {
        "node_instance_id": "encode",
        "node_id": "n_json_stringify",
        "node_version": "1.0.0",
        "title": "HTML-safe dataset",
        "params": {
          "html_safe": true
        }
      },
      {
        "node_instance_id": "features",
        "node_id": "n_json_path",
        "node_version": "1.0.0",
        "title": "Earthquake features",
        "params": {
          "path": "features"
        }
      },
      {
        "node_instance_id": "metadata",
        "node_id": "n_json_path",
        "node_version": "1.0.0",
        "title": "Source metadata",
        "params": {
          "path": "metadata"
        }
      },
      {
        "node_instance_id": "rows",
        "node_id": "n_map_fields",
        "node_version": "1.0.0",
        "title": "Reusable earthquake rows",
        "params": {
          "mapping": {
            "id": "id",
            "place": "properties.place",
            "magnitude": "properties.mag",
            "time": "properties.time",
            "lon": "geometry.coordinates.0",
            "lat": "geometry.coordinates.1",
            "depth_km": "geometry.coordinates.2",
            "url": "properties.url"
          }
        }
      },
      {
        "node_instance_id": "dataset",
        "node_id": "n_json_template",
        "node_version": "1.0.0",
        "title": "Earthquake dataset",
        "params": {
          "template": {
            "schema_version": "planet-pulse/1",
            "source": "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_day.geojson",
            "generated_at_ms": "{{ inputs.metadata.generated }}",
            "window": "past day",
            "minimum_magnitude": 2.5,
            "timezone": "UTC",
            "units": {
              "time": "Unix milliseconds",
              "lat": "degrees",
              "lon": "degrees",
              "depth_km": "kilometers",
              "magnitude": "reported USGS magnitude"
            },
            "rows": "{{ inputs.rows }}"
          }
        },
        "custom_inputs": {
          "metadata": "json",
          "rows": "json"
        },
        "exposed_inputs": [
          "metadata",
          "rows"
        ],
        "inputs_visibility_configured": true
      },
      {
        "node_instance_id": "view",
        "node_id": "n_html_template",
        "node_version": "1.0.0",
        "title": "Signal Garden / botanical renderer",
        "params": {
          "template": "<!doctype html>\n<html lang=\"en\">\n  <head>\n    <meta charset=\"utf-8\" />\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n    <title>Signal Garden</title>\n    <style>\n      :root {\n        color-scheme: light;\n        --paper: #f1ecdf;\n        --ink: #253e35;\n        --muted: #647567;\n        --line: #c9cfbd;\n        --accent: #376b50;\n      }\n      * { box-sizing: border-box; }\n      body {\n        margin: 0;\n        color: var(--ink);\n        background: radial-gradient(circle at 1px 1px, #75836b14 1px, transparent 0) 0 0 / 9px 9px, var(--paper);\n        font: 15px/1.5 Georgia, serif;\n      }\n      main { max-width: 1500px; padding: 32px 36px; margin: auto; }\n      header { display: flex; justify-content: space-between; gap: 32px; align-items: end; }\n      .eyebrow, label, button, select, .caption, footer, dt, .stat { font-family: monospace; }\n      .eyebrow { font-size: 11px; letter-spacing: .2em; color: var(--accent); }\n      h1 { font-size: clamp(42px, 6vw, 80px); font-weight: 400; line-height: 1; margin: 16px 0; letter-spacing: -.04em; }\n      h1 em { color: var(--accent); }\n      p { margin: 8px 0; }\n      .caption { font-size: 11px; color: var(--muted); line-height: 1.7; }\n      .source { max-width: 350px; overflow-wrap: anywhere; }\n      .controls { display: flex; gap: 18px; flex-wrap: wrap; align-items: end; padding: 20px 0; margin-top: 22px; border-block: 1px solid var(--line); }\n      label { display: grid; gap: 6px; color: var(--muted); font-size: 11px; }\n      button, select { padding: 8px 12px; border: 1px solid #a6b6a0; background: #f9f6ed; color: var(--ink); border-radius: 3px; font-size: 12px; }\n      button { cursor: pointer; }\n      button:hover { background: #dde7d6; }\n      :focus-visible { outline: 2px solid var(--accent); outline-offset: 3px; }\n      .tally { margin-left: auto; text-align: right; }\n      .stat { font-size: 28px; }\n      .garden-scroll { overflow-x: auto; margin-top: 20px; border-bottom: 1px solid var(--line); }\n      svg { display: block; width: 100%; min-width: 720px; height: auto; }\n      .flower { cursor: pointer; outline: none; }\n      .halo { opacity: 0; transition: opacity .15s; }\n      .flower:hover .halo, .flower:focus .halo, .flower[aria-pressed=\"true\"] .halo { opacity: 1; }\n      .sway { transform-origin: 0px 0px; animation: sway 7s ease-in-out infinite alternate; }\n      @keyframes sway { from { transform: rotate(-1.3deg); } to { transform: rotate(1.3deg); } }\n      .paused .sway { animation-play-state: paused; }\n      @media (prefers-reduced-motion: reduce) { .sway { animation: none; } .halo { transition: none; } }\n      .legend { display: flex; flex-wrap: wrap; gap: 22px; padding: 12px 0; }\n      #color-scale { display: inline-block; width: 75px; height: 8px; margin-right: 8px; }\n      .below { display: grid; grid-template-columns: minmax(0, 1fr) minmax(260px, 1fr); gap: 36px; margin-top: 22px; }\n      h2 { font-size: 26px; font-weight: 400; margin: 0 0 12px; }\n      .records { max-height: 210px; overflow-y: auto; display: grid; gap: 6px; align-content: start; }\n      .records button { text-align: left; background: transparent; border-color: var(--line); }\n      .records button[aria-pressed=\"true\"] { background: #dde7d6; border-color: var(--accent); }\n      #detail { min-height: 160px; padding: 20px; background: #e2e8d8; border-left: 3px solid var(--accent); overflow-wrap: anywhere; }\n      #detail h3 { margin: 0 0 12px; font-size: 20px; font-weight: 400; }\n      dl { display: grid; grid-template-columns: 110px 1fr; gap: 6px 14px; margin: 0; }\n      dt { font-size: 11px; color: var(--muted); } dd { margin: 0; }\n      #notice, #error { margin-top: 12px; } #error { color: #9a3627; }\n      footer { font-size: 11px; color: var(--muted); margin-top: 28px; border-top: 1px solid var(--line); padding-top: 16px; }\n      @media (max-width: 700px) { main { padding: 22px 16px; } header { display: block; } .source { margin-top: 20px; } .below { grid-template-columns: 1fr; gap: 20px; } .tally { margin-left: 0; } }\n    </style>\n  </head>\n  <body>\n    <main>\n      <header>\n        <div><div class=\"eyebrow\">SUBFORK / BOTANICAL DATA STUDIES / 02</div><h1>Signal <em>Garden.</em></h1><p>Every record, a flower. The same world, seen differently.</p></div>\n        <div class=\"source caption\"><div id=\"source\">Reading dataset...</div><div id=\"snapshot\"></div></div>\n      </header>\n      <div class=\"controls\">\n        <label>Stem height<select id=\"height\" aria-label=\"Stem height field\"></select></label>\n        <label>Bloom size<select id=\"size\" aria-label=\"Bloom size field\"></select></label>\n        <label>Petal color<select id=\"color\" aria-label=\"Petal color field\"></select></label>\n        <label>Palette<select id=\"palette\"><option value=\"meadow\">Meadow</option><option value=\"sunset\">Sunset</option><option value=\"tide\">Tide</option></select></label>\n        <button id=\"motion\" type=\"button\" aria-pressed=\"false\">Pause motion</button>\n        <div class=\"tally\"><span id=\"count\" class=\"stat\">--</span> <span class=\"caption\">FLOWERS</span></div>\n      </div>\n      <p id=\"error\" role=\"alert\" hidden></p>\n      <p id=\"notice\" class=\"caption\"></p>\n      <div class=\"garden-scroll\" tabindex=\"0\" aria-label=\"Garden canvas. Scroll horizontally on small screens.\">\n        <svg id=\"garden\" viewBox=\"0 0 1200 500\" role=\"group\" aria-label=\"Interactive data garden. Each flower is also available in the record list below.\">\n          <defs><linearGradient id=\"ground\" x2=\"0\" y2=\"1\"><stop stop-color=\"#96ae7866\"/><stop offset=\"1\" stop-color=\"#96ae7800\"/></linearGradient></defs>\n          <circle cx=\"1025\" cy=\"100\" r=\"67\" fill=\"#dfb967\" opacity=\".15\"/>\n          <path d=\"M0 438 Q300 413 600 439 T1200 430 L1200 500 L0 500Z\" fill=\"url(#ground)\"/>\n          <path d=\"M20 456 Q330 431 610 457 T1180 448\" fill=\"none\" stroke=\"#9cab8b\" stroke-width=\"1\"/>\n          <g id=\"flowers\"></g>\n        </svg>\n      </div>\n      <div class=\"legend caption\"><span id=\"height-scale\"></span><span id=\"size-scale\"></span><span><i id=\"color-scale\"></i><span id=\"color-label\"></span></span></div>\n      <div class=\"below\"><section><h2>Field notes</h2><div id=\"detail\" aria-live=\"polite\">Select a flower to inspect the record behind it.</div></section><section><h2>Specimen index</h2><div id=\"records\" class=\"records\" aria-label=\"Records\"></div></section></div>\n      <footer>Run the graph again to refresh the snapshot. Growth and swaying are visual metaphors, not real-time measurements or forecasts. Layout is deterministic. Field scales use the displayed records; position and petal count are decorative.</footer>\n    </main>\n    <!-- prettier-ignore -->\n    <script id=\"dataset\" type=\"application/json\">{{ inputs.dataset_json }}</script>\n    <script>\n      'use strict';\n      const $ = id => document.getElementById(id);\n      const palettes = { meadow: ['#deb254', '#db8270', '#895f78'], sunset: ['#efc963', '#dc8353', '#a94842'], tide: ['#9cc4ae', '#5faaa4', '#3e697f'] };\n      const pretty = key => key.replaceAll('_', ' ');\n      const number = value => Number.isFinite(value) ? Number(value.toFixed(2)).toLocaleString() : '--';\n      const stamp = value => new Date(value).toISOString().replace('T', ' ').replace(/\\.\\d{3}Z$/, ' UTC');\n      function hash(text) { let value = 2166136261; for (const char of text) value = Math.imul(value ^ char.charCodeAt(0), 16777619); return value >>> 0; }\n      function element(tag, attributes = {}) {\n        const node = document.createElementNS('http://www.w3.org/2000/svg', tag);\n        for (const [key, value] of Object.entries(attributes)) node.setAttribute(key, String(value));\n        return node;\n      }\n      function prepare(dataset) {\n        if (!dataset || !Array.isArray(dataset.rows) || !Number.isFinite(dataset.generated_at_ms) || Math.abs(dataset.generated_at_ms) > 8.64e15) throw new Error('Expected a dataset with rows and a valid generated_at_ms timestamp.');\n        const valid = dataset.rows.filter(row => row && typeof row === 'object' && !Array.isArray(row));\n        const rows = valid.map((row, index) => ({ ...row, _index: index, age_hours: Number.isFinite(row.time) ? Math.max(0, (dataset.generated_at_ms - row.time) / 3600000) : null }))\n          .sort((a, b) => (Number.isFinite(b.time) ? b.time : -Infinity) - (Number.isFinite(a.time) ? a.time : -Infinity) || a._index - b._index).slice(0, 60);\n        return { rows, total: valid.length, omitted: dataset.rows.length - valid.length };\n      }\n      function extent(rows, key) {\n        const values = rows.map(row => row[key]).filter(Number.isFinite);\n        return values.length ? [Math.min(...values), Math.max(...values)] : [0, 0];\n      }\n      function fraction(value, range) {\n        if (!Number.isFinite(value)) return null;\n        return range[0] === range[1] ? .5 : Math.max(0, Math.min(1, (value - range[0]) / (range[1] - range[0])));\n      }\n      // The renderer consumes normalized rows, never provider-specific GeoJSON.\n      try {\n        const dataset = JSON.parse($('dataset').textContent);\n        const { rows, total, omitted } = prepare(dataset);\n        const numeric = [...new Set(rows.flatMap(row => Object.keys(row)))].filter(key => !['id', 'time', '_index'].includes(key) && rows.some(row => Number.isFinite(row[key]))).sort();\n        const categories = [...new Set(rows.flatMap(row => Object.keys(row)))].filter(key => !['id', 'url', 'place', 'name', 'title'].includes(key) && rows.some(row => typeof row[key] === 'string') && !numeric.includes(key)).sort();\n        const label = row => String(row.place || row.name || row.title || row.id || 'Record ' + (row._index + 1));\n        const units = key => key === 'age_hours' ? 'hours' : String(dataset.units?.[key] || '');\n        $('source').textContent = String(dataset.source || 'User-supplied dataset');\n        $('snapshot').textContent = 'Snapshot: ' + stamp(dataset.generated_at_ms) + (Date.now() - dataset.generated_at_ms > 7200000 ? ' (older than 2 hours)' : '');\n        $('count').textContent = String(rows.length);\n        $('notice').textContent = (total > 60 ? 'Showing the newest 60 of ' + total + ' records. ' : '') + (omitted ? omitted + ' invalid records omitted. ' : '') + (rows.length ? 'Hover to highlight; select or keyboard-focus a flower to read its field notes.' : 'No records in this snapshot. Run again or connect another dataset.');\n        let selected = null;\n        const links = new Map();\n        function populate(id, fields, preferred) {\n          for (const field of fields) { const option = document.createElement('option'); option.value = field; option.textContent = pretty(field); $(id).append(option); }\n          $(id).disabled = !fields.length;\n          $(id).value = fields.includes(preferred) ? preferred : fields[0] || '';\n        }\n        populate('height', numeric, 'age_hours'); populate('size', numeric, 'magnitude'); populate('color', [...numeric, ...categories], 'depth_km');\n        function inspect(row) {\n          selected = row;\n          $('detail').replaceChildren();\n          const heading = document.createElement('h3'); heading.textContent = label(row);\n          const list = document.createElement('dl');\n          const fields = [...new Set(['time', $('height').value, $('size').value, $('color').value, 'lat', 'lon'])].filter(key => key && Object.hasOwn(row, key));\n          for (const key of fields) {\n            const dt = document.createElement('dt'), dd = document.createElement('dd'); dt.textContent = pretty(key);\n            dd.textContent = key === 'time' && Number.isFinite(row.time) && Math.abs(row.time) <= 8.64e15 ? stamp(row.time) : (Number.isFinite(row[key]) ? number(row[key]) + ' ' + units(key) : row[key] == null ? 'Not reported' : String(row[key]));\n            list.append(dt, dd);\n          }\n          $('detail').append(heading, list);\n          for (const [candidate, pair] of links) for (const node of pair) node.setAttribute('aria-pressed', String(candidate === row));\n        }\n        function draw() {\n          const heightKey = $('height').value, sizeKey = $('size').value, colorKey = $('color').value;\n          const heightRange = extent(rows, heightKey), sizeRange = extent(rows, sizeKey), colorRange = extent(rows, colorKey);\n          const categorical = categories.includes(colorKey);\n          const categoryValues = [...new Set(rows.map(row => row[colorKey]).filter(value => typeof value === 'string'))].sort();\n          const colors = palettes[$('palette').value];\n          const rangeLabel = (key, range) => key ? pretty(key) + ': ' + number(range[0]) + ' to ' + number(range[1]) + ' ' + units(key) : 'uniform';\n          $('height-scale').textContent = 'Height / ' + rangeLabel(heightKey, heightRange);\n          $('size-scale').textContent = 'Bloom / ' + rangeLabel(sizeKey, sizeRange);\n          $('color-scale').style.background = 'linear-gradient(to right, ' + colors.join(',') + ')';\n          $('color-label').textContent = categorical ? pretty(colorKey) + ': ' + categoryValues.map((value, index) => value + ' (color ' + (index % 3 + 1) + ')').join(', ') : rangeLabel(colorKey, colorRange) + ' / low to high';\n          $('flowers').replaceChildren(); $('records').replaceChildren(); links.clear();\n          // Three beds keep 60 specimens bounded and each flower keyboard reachable.\n          rows.forEach((row, index) => {\n            const bed = index % 3, column = Math.floor(index / 3), columns = Math.ceil((rows.length - bed) / 3);\n            const x = 50 + (column + .5) * 1100 / Math.max(1, columns), base = 365 + bed * 43;\n            const seed = hash(String(row.id || label(row)) + ':' + row._index);\n            const height = 65 + (fraction(row[heightKey], heightRange) ?? .5) * 180;\n            const radius = 8 + (fraction(row[sizeKey], sizeRange) ?? .5) * 14;\n            const colorValue = fraction(row[colorKey], colorRange);\n            const color = categorical ? (typeof row[colorKey] === 'string' ? colors[categoryValues.indexOf(row[colorKey]) % colors.length] : '#99a394') : colorValue === null ? '#99a394' : colors[Math.min(2, Math.floor(colorValue * 3))];\n            const bend = (seed % 19) - 9;\n            const group = element('g', { class: 'flower', transform: 'translate(' + x + ',' + base + ')', tabindex: 0, role: 'button', 'aria-label': label(row), 'aria-pressed': String(row === selected) });\n            const sway = element('g', { class: 'sway', style: 'animation-delay:-' + (seed % 7) + 's;animation-duration:' + (6 + seed % 5) + 's' });\n            sway.append(element('path', { d: 'M0 0 Q' + bend + ' ' + -height / 2 + ' ' + bend + ' ' + -height, fill: 'none', stroke: '#63815b', 'stroke-width': 1.8 }));\n            for (const side of [-1, 1]) { const y = -height * (side < 0 ? .35 : .6); sway.append(element('path', { d: 'M0 ' + y + ' Q' + side * 30 + ' ' + (y - 30) + ' ' + side * 36 + ' ' + (y - 13) + ' Q' + side * 19 + ' ' + (y + 7) + ' 0 ' + y, fill: bed === 1 ? '#8ca67a' : '#718e67', opacity: .8 })); }\n            const bloom = element('g', { transform: 'translate(' + bend + ',' + -height + ')' });\n            bloom.append(element('circle', { class: 'halo', r: radius * 2 + 7, fill: 'none', stroke: '#376b50', 'stroke-width': 1.5, 'stroke-dasharray': '3 4' }));\n            const petals = 7 + seed % 3;\n            for (let petal = 0; petal < petals; petal++) bloom.append(element('ellipse', { cx: 0, cy: -radius * .9, rx: radius * .58, ry: radius, transform: 'rotate(' + petal * 360 / petals + ')', fill: color, 'fill-opacity': .83, stroke: '#ffffff66', 'stroke-width': .6 }));\n            bloom.append(element('circle', { r: radius * .43, fill: '#745b35' }), element('circle', { r: radius * .18, fill: '#f2d68a' }));\n            sway.append(bloom); group.append(sway); $('flowers').append(group);\n            group.addEventListener('click', () => inspect(row)); group.addEventListener('focus', () => inspect(row));\n            group.addEventListener('keydown', event => { if (event.key === 'Enter' || event.key === ' ') { event.preventDefault(); inspect(row); } });\n            const button = document.createElement('button'); button.type = 'button'; button.textContent = String(index + 1).padStart(2, '0') + ' / ' + label(row); button.setAttribute('aria-pressed', String(row === selected)); button.addEventListener('click', () => inspect(row)); $('records').append(button); links.set(row, [group, button]);\n          });\n          if (selected) inspect(selected);\n          else if (!rows.length) $('detail').textContent = 'No specimens to inspect.';\n        }\n        const reduced = window.matchMedia('(prefers-reduced-motion: reduce)');\n        let paused = reduced.matches;\n        function updateMotion() { $('garden').classList.toggle('paused', paused); $('motion').setAttribute('aria-pressed', String(paused)); $('motion').textContent = reduced.matches ? 'Reduced motion' : paused ? 'Resume motion' : 'Pause motion'; $('motion').disabled = reduced.matches; }\n        $('motion').addEventListener('click', () => { paused = !paused; updateMotion(); });\n        reduced.addEventListener('change', () => { if (reduced.matches) paused = true; updateMotion(); });\n        for (const id of ['height', 'size', 'color', 'palette']) $(id).addEventListener('change', draw);\n        updateMotion(); draw();\n      } catch (error) { $('error').hidden = false; $('error').textContent = error instanceof Error ? error.message : 'Unable to grow this dataset.'; $('source').textContent = 'Dataset unavailable'; }\n    </script>\n  </body>\n</html>\n"
        },
        "custom_inputs": {
          "dataset_json": "str"
        },
        "exposed_inputs": [
          "dataset_json"
        ],
        "inputs_visibility_configured": true
      },
      {
        "node_instance_id": "response",
        "node_id": "n_html_response",
        "node_version": "1.0.0",
        "title": "Signal Garden page",
        "params": {}
      },
      {
        "node_instance_id": "data_response",
        "node_id": "n_json_response",
        "node_version": "1.0.0",
        "title": "Reusable dataset response",
        "params": {}
      }
    ],
    "edges": [
      {
        "source_node_id": "feed_url",
        "source_output": "url",
        "target_node_id": "fetch",
        "target_input": "url"
      },
      {
        "source_node_id": "fetch",
        "source_output": "body",
        "target_node_id": "parse",
        "target_input": "text"
      },
      {
        "source_node_id": "dataset",
        "source_output": "value",
        "target_node_id": "encode",
        "target_input": "value"
      },
      {
        "source_node_id": "parse",
        "source_output": "value",
        "target_node_id": "features",
        "target_input": "value"
      },
      {
        "source_node_id": "parse",
        "source_output": "value",
        "target_node_id": "metadata",
        "target_input": "value"
      },
      {
        "source_node_id": "features",
        "source_output": "value",
        "target_node_id": "rows",
        "target_input": "value"
      },
      {
        "source_node_id": "metadata",
        "source_output": "value",
        "target_node_id": "dataset",
        "target_input": "metadata"
      },
      {
        "source_node_id": "rows",
        "source_output": "items",
        "target_node_id": "dataset",
        "target_input": "rows"
      },
      {
        "source_node_id": "encode",
        "source_output": "text",
        "target_node_id": "view",
        "target_input": "dataset_json"
      },
      {
        "source_node_id": "view",
        "source_output": "html",
        "target_node_id": "response",
        "target_input": "html"
      },
      {
        "source_node_id": "dataset",
        "source_output": "value",
        "target_node_id": "data_response",
        "target_input": "value"
      }
    ],
    "graph_outputs": {
      "html": {
        "node_instance_id": "view",
        "output_name": "html"
      },
      "response": {
        "node_instance_id": "response",
        "output_name": "response"
      },
      "dataset": {
        "node_instance_id": "dataset",
        "output_name": "value"
      },
      "data_response": {
        "node_instance_id": "data_response",
        "output_name": "response"
      },
      "rows": {
        "node_instance_id": "rows",
        "output_name": "items"
      },
      "geojson": {
        "node_instance_id": "parse",
        "output_name": "value"
      }
    },
    "metadata": {
      "template": {
        "category": "html",
        "status": "provider-backed-probe",
        "summary": "Reusable SVG garden from a normalized dataset, initially wired to the same USGS rows as Planet Pulse.",
        "requires": {
          "nodes": [
            "n_url_input",
            "n_fetch_url",
            "n_json_parse",
            "n_json_stringify",
            "n_json_path",
            "n_map_fields",
            "n_json_template",
            "n_html_template",
            "n_html_response",
            "n_json_response"
          ],
          "panels": [
            "html",
            "table",
            "json"
          ],
          "capabilities": [
            "public-http"
          ],
          "secrets": []
        }
      },
      "canvas": {
        "positions": {
          "feed_url": {
            "x": 40,
            "y": 200
          },
          "fetch": {
            "x": 320,
            "y": 200
          },
          "parse": {
            "x": 600,
            "y": 200
          },
          "features": {
            "x": 880,
            "y": 60
          },
          "metadata": {
            "x": 880,
            "y": 280
          },
          "rows": {
            "x": 1160,
            "y": 60
          },
          "dataset": {
            "x": 1440,
            "y": 180
          },
          "encode": {
            "x": 1720,
            "y": 100
          },
          "view": {
            "x": 2000,
            "y": 100
          },
          "response": {
            "x": 2280,
            "y": 100
          },
          "data_response": {
            "x": 1720,
            "y": 340
          }
        },
        "recommended_panels": [
          {
            "kind": "html",
            "binding": {
              "nodeInstanceId": "view",
              "outputName": "html"
            }
          },
          {
            "kind": "table",
            "binding": {
              "nodeInstanceId": "rows",
              "outputName": "items"
            }
          },
          {
            "kind": "json",
            "binding": {
              "nodeInstanceId": "dataset",
              "outputName": "value"
            }
          }
        ]
      }
    }
  },
  "tags": [
    "html",
    "visualizations"
  ]
}