feat: release Vibeflow n8n v0.6.0

recipes/ai-lead-enrichment.md

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+# Recipe: AI Lead Enrichment
+
+## Goal
+
+Capture a new inbound lead, enrich the company context, generate a short structured summary, score the lead using simple rules, and write the result to the CRM.
+
+## Typical trigger
+
+- new CRM lead
+- form submission
+- webhook from a landing page
+
+## Systems involved
+
+- CRM such as HubSpot or Pipedrive
+- enrichment source such as Clearbit-like data or internal lookup
+- optional LLM step for summarization
+- Slack for alerts
+
+## Core steps
+
+1. Receive the lead payload.
+2. Normalize fields such as company name, email domain, and source.
+3. Query enrichment data.
+4. Generate a concise structured summary.
+5. Apply a scoring rule based on company size, geography, and source.
+6. Update the CRM record.
+7. Notify Slack if the lead crosses a threshold.
+
+## Important assumptions
+
+- scoring starts with safe defaults
+- enrichment can fail without blocking the whole workflow
+- missing optional fields should not break CRM updates
+
+## What makes this recipe good for demos
+
+- shows practical AI usage
+- stays grounded in business logic
+- produces an output people immediately understand
+
+## Validation checklist
+
+- lead payload is parsed correctly
+- enrichment failures are captured clearly
+- summary format is stable
+- score thresholds are visible and editable
+- CRM update succeeds even when optional data is missing