Gemini에서 Google Sheets로 자동화된 학술 논문 메타데이터 및 변수 추출

• •Google 계정 및 Gmail API 인증 정보
• •Google Sheets API 인증 정보
• •Google Gemini API Key

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      "notes": "Parses input JSON, uppercases Journal, flags UTD24/FT50, computes Rank.",
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      ],
      "parameters": {
        "jsCode": "// ===== UTD24 & FT50 journal lists (UPPERCASE for matching) =====\nconst utd24 = new Set([\n  \"MANAGEMENT SCIENCE\",\n  \"MIS QUARTERLY\",\n  \"INFORMATION SYSTEMS RESEARCH\",\n  \"JOURNAL OF MARKETING\",\n  \"JOURNAL OF FINANCE\",\n  \"JOURNAL OF ACCOUNTING RESEARCH\",\n  \"JOURNAL OF ACCOUNTING AND ECONOMICS\",\n  \"JOURNAL OF POLITICAL ECONOMY\",\n  \"MARKETING SCIENCE\",\n  \"OPERATIONS RESEARCH\",\n  \"ORGANIZATION SCIENCE\",\n  \"JOURNAL OF CONSUMER RESEARCH\",\n  \"JOURNAL OF FINANCIAL ECONOMICS\",\n  \"THE ACCOUNTING REVIEW\",\n  \"STRATEGIC MANAGEMENT JOURNAL\",\n  \"ADMINISTRATIVE SCIENCE QUARTERLY\",\n  \"ACADEMY OF MANAGEMENT JOURNAL\",\n  \"ACADEMY OF MANAGEMENT REVIEW\",\n  \"REVIEW OF FINANCIAL STUDIES\",\n  \"JOURNAL OF INTERNATIONAL BUSINESS STUDIES\",\n  \"JOURNAL OF BUSINESS VENTURING\",\n  \"MANUFACTURING & SERVICE OPERATIONS MANAGEMENT\",\n  \"PRODUCTION AND OPERATIONS MANAGEMENT\",\n  \"MANAGEMENT INFORMATION SYSTEMS QUARTERLY\"\n]);\n\nconst ft50 = new Set([\n  \"ACADEMY OF MANAGEMENT ANNALS\",\n  \"ACADEMY OF MANAGEMENT JOURNAL\",\n  \"ACADEMY OF MANAGEMENT REVIEW\",\n  \"ACCOUNTING, ORGANIZATIONS AND SOCIETY\",\n  \"ADMINISTRATIVE SCIENCE QUARTERLY\",\n  \"AMERICAN ECONOMIC REVIEW\",\n  \"CONTEMPORARY ACCOUNTING RESEARCH\",\n  \"ECONOMETRICA\",\n  \"ENTREPRENEURSHIP THEORY AND PRACTICE\",\n  \"HARVARD BUSINESS REVIEW\",\n  \"HUMAN RELATIONS\",\n  \"HUMAN RESOURCE MANAGEMENT\",\n  \"INFORMATION SYSTEMS RESEARCH\",\n  \"JOURNAL OF ACCOUNTING AND ECONOMICS\",\n  \"JOURNAL OF ACCOUNTING RESEARCH\",\n  \"JOURNAL OF APPLIED PSYCHOLOGY\",\n  \"JOURNAL OF BUSINESS ETHICS\",\n  \"JOURNAL OF CONSUMER PSYCHOLOGY\",\n  \"JOURNAL OF CONSUMER RESEARCH\",\n  \"JOURNAL OF FINANCE\",\n  \"JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS\",\n  \"JOURNAL OF FINANCIAL ECONOMICS\",\n  \"JOURNAL OF INTERNATIONAL BUSINESS STUDIES\",\n  \"JOURNAL OF MANAGEMENT\",\n  \"JOURNAL OF MANAGEMENT INFORMATION SYSTEMS\",\n  \"JOURNAL OF MANAGEMENT STUDIES\",\n  \"JOURNAL OF MARKETING\",\n  \"JOURNAL OF MARKETING RESEARCH\",\n  \"JOURNAL OF OPERATIONS MANAGEMENT\",\n  \"JOURNAL OF POLITICAL ECONOMY\",\n  \"JOURNAL OF THE ACADEMY OF MARKETING SCIENCE\",\n  \"MANAGEMENT SCIENCE\",\n  \"MANUFACTURING & SERVICE OPERATIONS MANAGEMENT\",\n  \"MARKETING SCIENCE\",\n  \"MIS QUARTERLY\",\n  \"OPERATIONS RESEARCH\",\n  \"ORGANIZATION SCIENCE\",\n  \"ORGANIZATION STUDIES\",\n  \"ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES\",\n  \"PRODUCTION AND OPERATIONS MANAGEMENT\",\n  \"QUARTERLY JOURNAL OF ECONOMICS\",\n  \"RESEARCH POLICY\",\n  \"REVIEW OF ACCOUNTING STUDIES\",\n  \"REVIEW OF ECONOMIC STUDIES\",\n  \"REVIEW OF FINANCE\",\n  \"REVIEW OF FINANCIAL STUDIES\",\n  \"SLOAN MANAGEMENT REVIEW\",\n  \"STRATEGIC ENTREPRENEURSHIP JOURNAL\",\n  \"STRATEGIC MANAGEMENT JOURNAL\"\n]);\n\n// ===== MAIN =====\nconst items = $input.all();\n\nreturn items.map(item => {\n  const data = item.json;  // 直接取当前 item 的数据\n  const journalUpper = (data.Source || '').toUpperCase().trim();\n\n  const isUTD24 = utd24.has(journalUpper);\n  const isFT50  = ft50.has(journalUpper);\n\n  let Rank = 'Not listed';\n  if (isUTD24 && isFT50) Rank = 'UTD24 & FT50';\n  else if (isUTD24) Rank = 'UTD24';\n  else if (isFT50) Rank = 'FT50';\n\n  // 直接修改原数据\n  data.Rank = Rank;\n\n  return { json: data };\n});\n"
      },
      "notesInFlow": true,
      "typeVersion": 2
    },
    {
      "id": "6dffab19-fc4b-4cce-98e2-f3b3df75f018",
      "name": "배치 처리기",
      "type": "n8n-nodes-base.splitInBatches",
      "notes": "Process items in batches.",
      "position": [
        -320,
        1248
      ],
      "parameters": {
        "options": {},
        "batchSize": 10
      },
      "notesInFlow": true,
      "typeVersion": 3
    },
    {
      "id": "815ae6d3-d485-4c22-be41-613e2e40104f",
      "name": "WOS 필드 매퍼",
      "type": "n8n-nodes-base.set",
      "notes": "Map WOS fields to consistent keys for LLM.",
      "position": [
        -544,
        1248
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "1fe90882-f72f-44b0-9957-1972abbc0b99",
              "name": "Authors",
              "type": "string",
              "value": "={{ $json.authors }}"
            },
            {
              "id": "3dcc8233-678c-4902-8cf4-41d051c8619c",
              "name": "Title",
              "type": "string",
              "value": "={{ $json.title }}"
            },
            {
              "id": "82b3d148-7206-45e1-a98b-48c2f12110cf",
              "name": "Publication_date",
              "type": "string",
              "value": "={{ $json.publication_date }}"
            },
            {
              "id": "3557f70f-c27c-4880-81b8-661a3c6c490a",
              "name": "Source",
              "type": "string",
              "value": "={{ $json.source }}"
            },
            {
              "id": "c2978a5d-2583-4400-9556-a9967f07c535",
              "name": "Abstract",
              "type": "string",
              "value": "={{ $json.abstract }}"
            }
          ]
        }
      },
      "notesInFlow": true,
      "typeVersion": 3.4
    },
    {
      "id": "48078b19-e7e8-4ed5-91f6-2540df195daf",
      "name": "최종 데이터 매퍼",
      "type": "n8n-nodes-base.set",
      "position": [
        576,
        1248
      ],
      "parameters": {
        "options": {
          "ignoreConversionErrors": true
        },
        "assignments": {
          "assignments": [
            {
              "id": "3d64232b-c602-4bfa-867d-e19e6d5ee934",
              "name": "Authors",
              "type": "string",
              "value": "={{ $('Journal Rank Classifier').item.json.Authors }}"
            },
            {
              "id": "f05c2476-96fc-4edf-a06c-5a0e45026673",
              "name": "Title",
              "type": "string",
              "value": "={{ $('Journal Rank Classifier').item.json.Title }}"
            },
            {
              "id": "8a5667b9-8274-49fa-85a6-73b5cd440bd5",
              "name": "Publication_date",
              "type": "string",
              "value": "={{ $('Batch Processor').item.json.Publication_date }}"
            },
            {
              "id": "82c7191f-86de-4e4c-a242-655021a97fa4",
              "name": "Source",
              "type": "string",
              "value": "={{ $('Journal Rank Classifier').item.json.Source }}"
            },
            {
              "id": "5541b0bf-dbef-4724-a526-870ad9f08005",
              "name": "JournalRank",
              "type": "string",
              "value": "={{ $('Journal Rank Classifier').item.json.Rank }}"
            },
            {
              "id": "31c89435-c6b7-46c7-ab8b-b471b2c48f9d",
              "name": "AiSummary",
              "type": "string",
              "value": "={{ $json.output.Summary }}"
            },
            {
              "id": "f62c5f71-8219-420d-87ab-aba347f7673d",
              "name": "IndependentVariable",
              "type": "string",
              "value": "={{ Array.isArray($json.output.IV) ? $json.output.IV.join('; ') : $json.output.IV }}"
            },
            {
              "id": "5df47e1a-9772-4f42-bc7f-f4210cfa50cd",
              "name": "DependentVariable",
              "type": "string",
              "value": "={{ Array.isArray($json.output.DV) ? $json.output.DV.join('; ') : $json.output.DV }}"
            },
            {
              "id": "23f93176-157e-4e71-8c5f-29debc575297",
              "name": "Mediator",
              "type": "string",
              "value": "={{ Array.isArray($json.output.Mediator) ? $json.output.Mediator.join('; ') : $json.output.Mediator }}"
            },
            {
              "id": "655b398a-a201-45a5-becd-05f05ccaa567",
              "name": "Moderator",
              "type": "string",
              "value": "={{ Array.isArray($json.output.Moderator) ? $json.output.Moderator.join('; ') : $json.output.Moderator }}"
            },
            {
              "id": "4eaf14e2-bb55-459a-a31e-01d53393c979",
              "name": "Overaching_theory",
              "type": "string",
              "value": "={{ ($json.output.Overarching_theory || $json.output.Overaching_theory || []).join('; ') }}"
            },
            {
              "id": "46490da9-11af-4450-845e-5035e729842a",
              "name": "Abstract",
              "type": "array",
              "value": "={{ $('Journal Rank Classifier').item.json.Abstract }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "27414957-4c91-4ceb-8a3f-c0c31446f99a",
      "name": "체크포인트 데이터 읽기",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        -768,
        1248
      ],
      "parameters": {
        "options": {},
        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "Checkpoint"
        },
        "documentId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Get Sheet id').item.json.ID }}"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "i2QQ5h70aiZ7H0s3",
          "name": "Google Sheets(owen)"
        }
      },
      "typeVersion": 4.7
    },
    {
      "id": "8a66f6d9-8024-4f74-8e08-bc7ac8268671",
      "name": "최종 결과 저장",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        800,
        1248
      ],
      "parameters": {
        "columns": {
          "value": {},
          "schema": [],
          "mappingMode": "autoMapInputData",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {
          "cellFormat": "RAW"
        },
        "operation": "appendOrUpdate",
        "sheetName": {
          "__rl": true,
          "mode": "name",
          "value": "FinalResult"
        },
        "documentId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Get Sheet id').item.json.ID }}"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "i2QQ5h70aiZ7H0s3",
          "name": "Google Sheets(owen)"
        }
      },
      "typeVersion": 4.7
    },
    {
      "id": "1de00028-e39b-446d-9f18-db67380ce4d1",
      "name": "자동 수정 출력 파서1",
      "type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
      "position": [
        208,
        1472
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "b7ec38b4-5666-4c57-988c-f24dcfbc4e4e",
      "name": "3초 대기",
      "type": "n8n-nodes-base.wait",
      "position": [
        1024,
        1424
      ],
      "webhookId": "8d861ed5-69d4-480b-914c-1d312ad1ef37",
      "parameters": {
        "amount": 3
      },
      "typeVersion": 1.1
    },
    {
      "id": "faa63b27-907a-48e7-bb85-b30763f7827d",
      "name": "call",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        704,
        336
      ],
      "parameters": {
        "options": {
          "waitForSubWorkflow": false
        },
        "workflowId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $workflow.id}}",
          "cachedResultUrl": "/workflow/=%7B%7B%20$workflow.id%7D%7D"
        },
        "workflowInputs": {
          "value": {
            "GoogleSheetID": "={{ $('Create spreadsheet').item.json.spreadsheetId }}"
          },
          "schema": [
            {
              "id": "GoogleSheetID",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "GoogleSheetID",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "GoogleSheetID"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "94cb61b6-0096-4820-aa67-c6939a3801e6",
      "name": "스티키 노트",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2848,
        208
      ],
      "parameters": {
        "width": 704,
        "height": 784,
        "content": "# 🚀 Quick start\n\n## 📋 Workflow Overview\nThis automated workflow helps you:\n- 📥 Ingest academic bibliographic files (CSV/XLS/XLSX) you upload in chat\n- 🧾 Normalize academic paper metadata (authors, title, abstract, date, - source) into a Checkpoint sheet and ✉️ send progress notifications via Gmail\n- 🧠 Use an LLM to extract study variables (IV/DV; optional mediator, moderator, overarching theory)\n- 🏷️ Classify journal rank (UTD24 / FT50) and merge with metadata\n- 📊 Append the finalized table to FinalResult in Google Sheets and ✉️ send completion notifications via Gmail\n\n## 🔑 Credentials\n- **Google Sheets OAuth2** (read/write)\n- **Gmail OAuth2** (send notifications)\n- **Google Gemini** (or any LLM you prefer)\n    \n\n## 🛠️ How to set up\n1. Connect **Google Sheets**, **Gmail**, and **Gemini (or your LLM)** credentials.\n2. Open `File Upload Trigger` → upload your **CSV/XLSX/XLS** file and type a **name** in chat (used as the Google Sheets spreadsheet title).\n3. Watch your inbox for status emails and open the Google Sheets spreadsheet to review **Checkpoint** and **FinalResult**.\n\n## 📝 Note\n- 📝 **Make sure your file includes abstracts.** If the academic paper data you upload doesn’t contain an abstract, the extracted results will be far less useful.\n- 🧩 **CSV yields no items?** Encoding mismatches can break the workflow. If this happens, convert the CSV to `.xls` or `.xlsx` and try again."
      },
      "typeVersion": 1
    },
    {
      "id": "3b5ee089-b1a8-4824-b9bb-4e3cf6ceba0a",
      "name": "스티키 노트1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1584,
        -192
      ],
      "parameters": {
        "color": 2,
        "width": 960,
        "height": 1168,
        "content": "## 1) File Intake & Type Routing\n\n### **Purpose:**\n- Receive academic files, parse them into structured records by file type, and create a Google Sheet named by message; prepares for subsequent **normalize academic paper metadata** and variable extraction.  \n### **Covers nodes:**\n`File Upload Trigger` → `Create spreadsheet` → `File Type Router (Switch)` → (`CSV Data Extractor` | `XLS Data Extractor` | `XLSX Data Extractor`)\n\n### **What it does:**\n- Supports uploading `.csv`, `.xls`, and `.xlsx` files and a **message** in chat (used as the Google Sheets spreadsheet title).\n- Creates a Google Sheets spreadsheet titled from the chat input, with two tabs: `Checkpoint` and `FinalResult`.\n- Routes processing by file extension (CSV/XLS/XLSX); unsupported types return “Pls attach a file”.   \n\n### **Inputs consumed:**\n- Uploaded file (binary)\n- `SpreadsheetTitle` (from chat input)\n### **Output produced:**\n- Parsed records array (JSON) for downstream per-item processing.\n- Target spreadsheet `spreadsheetId` (includes `Checkpoint` / `FinalResult`)."
      },
      "typeVersion": 1
    },
    {
      "id": "35cc183c-93f8-4b76-8fd6-b609c6dd53f0",
      "name": "스티키 노트2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -592,
        -192
      ],
      "parameters": {
        "color": 3,
        "width": 1264,
        "height": 1168,
        "content": "## 2) Per-Record Processing & Metadata Extraction\n\n### **Purpose:** \n- Batch the parsed results, **normalize academic paper metadata** for each record, write core fields to `Checkpoint`, and send progress notifications.\n### **Covers nodes:** \n`Loop Over Items` → `Paper Metadata Extractor` → `Data Formatter` → `Save to Checkpoint Sheet` → `Wait for 3s.` → `Read Write in Data` → `Send process notication`\n\n### **What it does:**\n- Uses `Loop Over Items` (batch size = 10) to process records efficiently.\n- Runs `Paper Metadata Extractor`(Gemini+Structured Output), to extract **authors, title, abstract, publication_date, source** from each raw JSON record into a strict schema.\n- Normalizes fields in `Data Formatter` (e.g., joins authors with semicolons) and appends them using `Save to Checkpoint Sheet`.\n- Pauses briefly with `Wait for 3s.` make api happy.\n- After finished loop,sends an email via `Send notication` (Gmail)\n\n### **Inputs consumed:**\n- Parsed record JSON\n### **Output produced:**\n- Five normalized columns in the `Checkpoint` sheet (result of organizing bibliographic metadata).\n- Stage notifications via Gmail.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "3d10bbf2-50c8-4ac8-82a3-6fa848770ca3",
      "name": "스티키 노트3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1968,
        1008
      ],
      "parameters": {
        "color": 5,
        "width": 720,
        "height": 832,
        "content": "## 3) Journal Ranking & Study Variable Extraction\n\n### **Purpose:**\n* Use `Checkpoint` metadata to assign journal ranks and run a second LLM pass to extract study variables (DV/IV/Mediator/Moderator/Overarching_theory) plus a brief summary, then write results to `FinalResult`.\n### **Covers nodes:**\n`Get Sheet id` → `Read Checkpoint Data` → `WOS Field Mapper` → `Batch Processor` → `Journal Rank Classifier (Code)` → `Academic Variables Extractor (LLM Agent)` → `Final Data Mapper (Set)` → `Save Final Results (Append/Update)` → `Wait 3s` → `Send done notication`\n\n### **What it does:**\n* Controls throughput with batched processing for stable API calls and sheet writes.\n* Classifies journals against built-in UTD24/FT50 lists and assigns `Rank ∈ {UTD24, FT50, UTD24 & FT50, Not listed}`.\n* Uses Title + Abstract to extract a strict-schema JSON payload: `AiSummary, DependentVariable, IndependentVariable, Mediator, Moderator, Overarching_theory`; validates with an auto-fixing parser and the chosen LLM.\n* Merges base metadata, journal rank, and AI-extracted variables, then appends/updates the `FinalResult` sheet.\n* Briefly waits (`Wait 3s`) to reduce the chance of rate limiting.\n\n### **Inputs consumed:**\n* `Checkpoint`-normalized metadata\n### **Output produced:**\n* `FinalResult`: an integrated table combining organized bibliographic metadata, journal ranking, and study variables.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "88ab6929-3801-4134-857a-4faaefa7586a",
      "name": "스티키 노트4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1264,
        1008
      ],
      "parameters": {
        "color": 5,
        "width": 2544,
        "height": 832,
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "b401df9b-f757-4c4c-91ec-2b4a27b74dcb",
      "name": "처리 알림 전송",
      "type": "n8n-nodes-base.gmail",
      "position": [
        416,
        336
      ],
      "webhookId": "ec263e2e-8a0a-4e49-bb00-9e830b9ab59f",
      "parameters": {
        "sendTo": "your gmail",
        "message": "=🎉Metadata Normalization Workflow Done\n📖 write 【{{ $items('Read Write in Data').length ? Math.max(...$items('Read Write in Data').map(i => Number(i.json.row_number))) -1: null }}】 paper into checkpoint sheet\n🕓{{ new Date().toLocaleString(\"en-CA\", { timeZone: \"Asia/Singapore\", hour12: false }).replace(',', '') }}",
        "options": {},
        "subject": "🎉Metadata Normalization Workflow Done",
        "emailType": "text"
      },
      "credentials": {
        "gmailOAuth2": {
          "id": "jCTNCiXplqBJRbmj",
          "name": "Gmail(owen)"
        }
      },
      "executeOnce": true,
      "typeVersion": 2.1
    },
    {
      "id": "890dcae0-0417-4a2b-9267-7e1714a65f45",
      "name": "완료 알림 전송",
      "type": "n8n-nodes-base.gmail",
      "position": [
        -96,
        1072
      ],
      "webhookId": "ec263e2e-8a0a-4e49-bb00-9e830b9ab59f",
      "parameters": {
        "sendTo": "your gmail",
        "message": "=🎉Study Variable Extraction Workflow Done\n📖 Time to study\n🕓{{ new Date().toLocaleString(\"en-CA\", { timeZone: \"Asia/Singapore\", hour12: false }).replace(',', '') }}",
        "options": {},
        "subject": "🎉Study Variable Extraction Workflow Done",
        "emailType": "text"
      },
      "credentials": {
        "gmailOAuth2": {
          "id": "jCTNCiXplqBJRbmj",
          "name": "Gmail(owen)"
        }
      },
      "executeOnce": true,
      "typeVersion": 2.1
    },
    {
      "id": "07f67c54-ba3c-41c3-9133-649a9215bf48",
      "name": "스티키 노트5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2800,
        1168
      ],
      "parameters": {
        "color": 4,
        "width": 576,
        "height": 208,
        "content": "# Tips\nSince the workflow makes multiple LLM calls, to avoid unexpected interruptions that waste time and tokens, a `checkpoint` is used to temporarily store data. If Step 3 fails and the workflow stops, activate the `manual trigger`, disable `When Executed by Another Workflow`, and manually adjust `Read Checkpoint Data`(id) so it reads from the previously written `checkpoint` sheet and then continue the workflow.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "676d7e8c-d66a-4fb5-8c3d-6734e7604d26",
      "name": "수동 트리거",
      "type": "n8n-nodes-base.manualTrigger",
      "disabled": true,
      "position": [
        -1200,
        1376
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "7b82e214-b931-4181-8018-63fdcc585c73",
      "name": "시트 ID 가져오기",
      "type": "n8n-nodes-base.set",
      "position": [
        -992,
        1184
      ],
      "parameters": {
        "options": {},
        "assignments": {
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