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