Detect Repeat Upwork Client Demand with n8n Data Tables

Detect repeat Upwork client demand with n8n Data Tables

This n8n workflow spots “repeat-client, repeat-demand” opportunities from incoming Upwork job events—deduplicating history with n8n Data Tables and deterministically logging evidence-backed matches, with Discord alerts on errors.

What this workflow does

• Ingests Upwork job events via a header-authenticated webhook (or runs an evaluation using a case from a Data Table).
• Normalizes and validates inputs, rejecting events that are missing jobId, clientId, or jobTitle.
• Deduplicates deterministically by generating stable client/job keys and checking the UpworkJobHistory Data Table for an existing eventId or jobKey; duplicates are skipped.
• Scores repeat demand by loading the client’s prior job history and comparing the current job using:
  • category match
  • skill overlap
  • title-token overlap
• Upserts history and opportunities:
  • Updates client aggregates in UpworkClientHistory
  • When repeat demand is detected, writes an evidence-backed record to UpworkOpportunities
  • Upserts the current job into UpworkJobHistory
• Returns a structured result indicating whether a repeat-client opportunity was detected.
• Handles failures safely by writing an audit record to UpworkWorkflowErrors and sending a Discord webhook alert.

Use cases

• Monitor incoming Upwork postings and get notified when the same client shows overlapping category/skills/title demand again.
• Build an automated pipeline that enriches leads using stored client/job history instead of reprocessing duplicates.
• Run repeat-client evaluations from UpworkRepeatClientEvalCas for backtesting or QA.

Technical details

• Nodes / components: if, set, code, discord, webhook, data table
• Required Data Tables: UpworkJobHistory, UpworkClientHistory, UpworkOpportunities, UpworkRepeatClientEvalCas (plus UpworkWorkflowErrors for audits)