multi-source-signal-synthesiser

Multi-Source Signal Synthesiser Skill

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.

Required Inputs

Ask the user for these if not provided:

• Signal sources (interviews, support tickets, NPS verbatims, app reviews, sales calls, analytics — any combination)
• Time period covered by the data
• Product area or feature the signals relate to (if scoped)

Source Weighting (default — adapt to context)

Source	Weight	Rationale
Direct research (interviews, usability tests)	5	Highest-fidelity, structured
Support tickets (unprompted pain signals)	4	Real pain, unfiltered
NPS verbatims	3	Broad but shallow
App store reviews	2	Public, self-selected
Sales call summaries	2	Filtered through sales lens
Anecdote or single report	1	Low confidence alone

Process

1. Tag each signal by source and apply weight
2. Look for convergence: same underlying need appearing across 3+ sources
3. Look for divergence: contradictory signals suggesting user segmentation
4. 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")
5. Produce ranked insights by weighted frequency
6. Validate — Confirm each insight has evidence from at least 2 source types. Flag any insight resting on a single source as low-confidence.

Output Structure

User Signal Synthesis — [Date / Period]

Sources included: [list with count per source] 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 next step, not generic]

[Repeat for top 3-5 insights]

Divergent Signals (Possible Segmentation)

[Where user groups appear to have genuinely different needs — specify which segments]

What the Data Does NOT Tell Us

[Gaps that require further research before acting]

Quality Checks

• Every insight references at least 2 distinct source types
• Surface requests are translated to underlying needs (not just echoed)
• Divergent signals identify the specific user segments, not just "some users disagree"
• Confidence ratings are consistent with source diversity and weighting
• "What the data does NOT tell us" section is honest about gaps

Anti-Patterns

• Do not echo surface-level feature requests as insights — translate every request to the underlying need before including it as a finding
• Do not assign High confidence to insights supported by only one source type — confidence requires corroboration across at least two distinct source types
• Do not treat all sources as equally weighted — a single interview quote and a pattern across 200 support tickets are not comparable signals
• Do not collapse divergent signals into a single finding — where user segments have genuinely different needs, name the segments explicitly rather than averaging them away
• Do not omit the research gap section when key decisions rest on thin data — acting on low-confidence findings without flagging the gaps misleads product teams