Multi-Source Signal Synthesiser Skill

Multi-Source Signal Synthesiser Skill

Purpose

Reconcile user signals from multiple sources — interviews, support tickets, NPS, app reviews, sales calls — into a unified, weighted insight brief that surfaces the underlying need rather than the surface-level request.

Source Weighting (default — adapt to your context)

• Direct research (interviews, usability tests): weight 5
• Support tickets (unprompted pain signals): weight 4
• NPS verbatims: weight 3
• App store reviews: weight 2
• Sales call summaries (filtered through sales lens): weight 2
• Anecdote or single report: weight 1

Process

1. Accept inputs from any combination of the source types above
2. Tag each signal by source and apply weight
3. Look for CONVERGENCE: same underlying need appearing across 3+ sources
4. Look for DIVERGENCE: contradictory signals suggesting user segmentation
5. Distinguish surface request from underlying need (e.g. "faster export" may mean "I don't trust the data will be there when I need it")
6. Produce ranked insights by weighted frequency

Output Format

User Signal Synthesis — [Date / Period]

Sources included: [list] Total signals processed: [n]

Insight 1: [Underlying need, not feature request]

• Confidence: High / Medium / Low (based on source diversity and weight)
• Evidence: [Signals from each source supporting this]
• Conflicting signals: [Any contradicting evidence and how to interpret it]
• Product implication: [Specific, not generic]

[Repeat for top 3-5 insights]

Divergent Signals (Possible Segmentation)

[Where user groups appear to have genuinely different needs]

What the Data Does NOT Tell Us

[Gaps that require further research before acting]

OpenClaw Configuration

Connect to: Notion (research docs), support inbox, NPS tool, app review feed. Schedule: weekly synthesis run, diff output showing new signals only.