一篇 r/n8n 帖子在 GPT 提示中发布了一个 12 行加权评分标准，适用于 220 人的 SaaS，每周处理 120 个销售线索。对于表单未捕获的内容，它采用与 Scavio 丰富步骤相同的模式。
前置条件
- n8n（云或自托管）
- OpenAI/Anthropic API 密钥
- Scavio API 密钥
- 有效的 CRM Webhook 或表单
操作指南
步骤 1: 触发入站表单/CRM Webhook
捕获铅有效载荷。
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// n8n: Webhook node, POST receiver, returns 200 fast步骤 2: 通过 Scavio 丰富：公司规模、最新新闻、角色背景
评分前的 HTTP 请求节点。
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// HTTP node: POST https://api.scavio.dev/api/v1/search
// Body: { "query": "site:linkedin.com/company {{$json.company}}" }步骤 3: 在 LLM 提示中撰写标题
12 行，加权，明确。
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// LLM node prompt:
// Score the lead 0-100 using ONLY this rubric.
// Title fit: 30 (VP/Dir/C-level = full, IC = 0)
// Industry match: 25 (logistics/transport/3PL = full)
// Company size: 20 (200-2000 employees = full)
// Intent signal in form: 15 (asked demo or pricing = full)
// Fit notes: 10 (anything in form indicating budget/timeline)
// Lead JSON: {{$json}}
// Enrichment: {{$node['Scavio'].json.organic_results.slice(0,3)}}步骤 4: 解析分数+原因JSON
强制模型输出 JSON。
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// Add to prompt: Return ONLY { "score": <int>, "reason": "<one sentence>" }步骤 5: 得分路线：热到 Slack、热到点滴、冷到滋养
按分数范围切换节点。
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// Switch: score>=70 -> Slack hot, 40-69 -> drip campaign, <40 -> nurture步骤 6: 将分数 + 原因写回 CRM
可审计的踪迹。
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// HubSpot/Salesforce/Pipedrive node: update lead with score, reason, and timestampPython 示例
Python
# Per-lead cost: 1 Scavio call + 1 LLM call + 1 CRM write = ~$0.01-0.04. 120 leads/week = under $5/week.JavaScript 示例
JavaScript
// n8n is the deliverable. The Python/JS equivalent is just translating the HTTP+LLM+CRM call sequence.预期输出
JSON
Inbound lead → score within 60 seconds → routed to right team → reason logged in CRM. Replaces ~15 hours/week of manual triage. The rubric IS the product; treat it like code (PR review, version, audit).