用 n8n + OpenAI 一键生成数字产品多频道发布文案包

输入产品名称、标语、价格、受众与品牌调性 → n8n LLM 链调用 GPT-4o-mini 生成博客、LinkedIn、X、Instagram 与邮件文案 → 可选写入 Google Sheets

给谁

数字产品创作者、独立卖家或内容运营者，想在产品上线时快速产出一致的多频道文案

你会得到

一次运行即可得到 5 条发布文案（博客导语、LinkedIn 帖子、X 推文、Instagram 配文、上线邮件），格式统一为纯文本 JSON，可复用或存档

你需要

n8n · google-sheets

步骤

1. 准备产品输入

   在 n8n 中手动执行工作流。检查并替换 Set Product Data 节点中的示例字段：productName（产品名称）、tagline（标语）、price（价格）、productURL（产品链接）、targetAudience（目标受众）、brandTone（品牌调性）。可直接修改 JSON，或将来连接真实数据源替换此节点。

2. 生成发布文案

   Create Launch Content（Basic LLM Chain）节点将产品数据拼接为文本，送入 OpenAI GPT-4 Model。模型使用 responseFormat=json_object，temperature=0.7。系统提示词要求返回严格 JSON，仅含 blog_intro、linkedin_post、x_post、instagram_caption、launch_email 五个键，每条文案需包含产品 URL、符合品牌调性、以利益点开头。

   运行提示词 · 来源原文。Create Launch Content 节点的 SystemMessagePromptTemplate message 字段

   复制提示词

   You are an expert launch copywriter for digital products sold on Gumroad and Etsy.
   
   Return ONLY valid JSON, no markdown, no code fences, no prose, no explanation. The JSON object must contain exactly these keys: blog_intro, linkedin_post, x_post, instagram_caption, launch_email. Return exactly this json structure:
   
   {"blog_intro": "<200-word benefit-led blog intro that hooks the reader, focuses on outcomes not features, and ends with a clear CTA linking to the product URL>", "linkedin_post": "<150-200 word professional LinkedIn post; insight-led opening, 2-3 short paragraphs, closes with CTA including the product URL>", "x_post": "<punchy X/Twitter post under 240 characters total including the product URL; hook-first>", "instagram_caption": "<100-150 word Instagram caption; warm and vivid tone, 3-5 relevant hashtags at the end, CTA with product URL>", "launch_email": "<launch email where the first line is exactly 'Subject: [your subject line here]', then a blank line, then 200-250 word benefit-led body, clear CTA with product URL, warm sign-off>"}
   
   Rules:
   - Every single asset MUST include the exact product URL from the input.
   - Lead with benefits and transformation, never with feature lists.
   - Match the stated brand tone exactly.
   - Write real, publish-ready copy — no placeholders, no filler.
   - Output the JSON object only — nothing before or after it.

   运行提示词 · 来源原文。Create Launch Content 节点的 text 字段（n8n 表达式，运行时拼接为 User 消息）

   复制提示词

   ={{ "Product Name: " + $json.productName + "\nTagline: " + $json.tagline + "\nPrice: " + $json.price + "\nProduct URL: " + $json.productURL + "\nTarget Audience: " + $json.targetAudience + "\nBrand Tone: " + $json.brandTone }}

3. 格式化输出

   Format Launch Content（Code 节点，JavaScript）接收 LLM 返回，处理两种可能形状：若 OpenAI 已自动解析 JSON，则直接取顶层 5 个键；若返回原始文本，则从 text/output/response 字段提取并 JSON.parse，同时去除 markdown 代码围栏。校验 5 个必需键是否存在，缺失则抛出错误；所有值强制转为字符串，确保下游节点获得干净文本。

   运行提示词 · 来源原文。Format Launch Content 节点的 jsCode 字段

   复制提示词

   // Shape-agnostic reader for the Basic LLM Chain ('Generate Launch Content').
   // The Basic LLM Chain can hand us the data in EITHER of two shapes, depending
   // on the model's Response Format:
   //   PATH A (already-parsed): because OpenAI Chat Model has Response Format =
   //     JSON, the chain auto-parses the model's JSON and exposes the 5 content
   //     keys DIRECTLY at the top level of item.json. There is no `text` field.
   //   PATH B (text-to-parse): the chain returns the raw string under `text`
   //     (or `output` / `response`), which we must JSON.parse ourselves.
   // This code handles both without assuming which one occurred.
   const item = $input.first();
   const data = item.json || {};
   const required = ['blog_intro', 'linkedin_post', 'x_post', 'instagram_caption', 'launch_email'];
   
   // Coerce any value to a string so downstream nodes always get clean text,
   // even if the model returned a number, null, or an object for a field.
   function asString(v) { return (v === undefined || v === null) ? '' : String(v); }
   
   let parsed;
   let pathTaken;
   
   // PATH A — the 5 keys are already present at the top level: use them directly.
   if (required.some(function (key) { return key in data; })) {
     parsed = data;
     pathTaken = 'already-parsed';
   } else {
     // PATH B — fall back to a raw text field and parse it ourselves.
     const raw = data.text ?? data.output ?? data.response;
     if (raw === undefined || raw === null || raw === '') {
       throw new Error(
         'Could not find content. No top-level content keys and no text/output/response field. ' +
         'Received keys: ' + JSON.stringify(Object.keys(data))
       );
     }
     // The model may wrap JSON in markdown code fences (```json ... ```). Strip them.
     let cleaned = String(raw).trim()
       .replace(/^```(?:json)?\s*/i, '')  // opening fence
       .replace(/\s*```$/i, '')           // closing fence
       .trim();
     try {
       parsed = JSON.parse(cleaned);
     } catch (e) {
       throw new Error(
         'LLM did not return valid JSON. Parse error: ' + e.message +
         '\n--- Raw text received ---\n' + String(raw)
       );
     }
     pathTaken = 'text-to-parse';
   }
   
   // Only fail if a key is genuinely ABSENT. A present-but-empty value is fine.
   const missing = required.filter(function (key) { return !(key in parsed); });
   if (missing.length > 0) {
     throw new Error(
       'Content is missing required key(s): ' + missing.join(', ') +
       ' (path: ' + pathTaken + '). Received keys: ' + JSON.stringify(Object.keys(parsed))
     );
   }
   
   return [{
     json: {
       blog_intro: asString(parsed.blog_intro),
       linkedin_post: asString(parsed.linkedin_post),
       x_post: asString(parsed.x_post),
       instagram_caption: asString(parsed.instagram_caption),
       launch_email: asString(parsed.launch_email)
     }
   }];

4. 可选：写入 Google Sheets

   如需存档，启用 Append Launch Content to Sheets 节点（默认禁用）。在节点中配置 Google Sheets OAuth2 凭证，填写目标 spreadsheet ID 与 sheet 名称（默认 Sheet1）。节点使用 autoMapInputData 自动映射上一步的 5 个字段为新增行。注意：首次配置需完成 Google OAuth2 授权流程。

你是一个 n8n 工作流搭建助手。用户想复现「数字产品多频道发布文案生成」模板。请按以下顺序协助：
1. 确认用户已有 n8n 实例（云或自托管）和 OpenAI API key。
2. 引导导入 workflow JSON 或从模板库安装 ID 16670。
3. 检查 Set Product Data 节点中的示例产品信息，协助替换为真实数据。
4. 说明 OpenAI 凭证创建与绑定到 OpenAI GPT-4 Model 节点的步骤。
5. 解释 Format Launch Content 节点的作用（容错解析两种 LLM 返回形状）。
6. 如需 Google Sheets 存档，说明启用节点、配置 OAuth2、填写 spreadsheet ID 的流程。
7. 建议首次运行前用示例数据手动执行，检查 5 条文案是否符合预期。