# Multi Source Signal Synthesiser

> Synthesises user signals from multiple research sources into a Use when this capability is needed.

- Skill: `tomevault-io/multi-source-signal-synthesiser` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/multi-source-signal-synthesiser`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/multi-source-signal-synthesiser/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/multi-source-signal-synthesiser

---

# Multi-Source Signal Synthesiser Skill

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

## Pipeline Context

**Primary use: Step 6 (/validate-problems)** — when combining:
- Analytics data (from analyst)
- Survey results (from research)
- Interview notes (from PM)
- Synthetic interviews (from step 2)

Also useful at any step where multiple data sources need reconciliation.

## Evidence Typing

Every signal must be tagged with evidence type and confidence score:

| Type | Confidence | Source examples |
|------|-----------|----------------|
| **REAL** | 0.6 - 1.0 | Analytics data, survey results, user interviews, A/B test results |
| **SYNTHETIC** | 0.2 - 0.4 | AI-generated interviews, synthetic personas |
| **INFERRED** | 0.3 - 0.5 | Logical deductions, cross-referencing patterns |
| **AMBIGUOUS** | 0.1 - 0.3 | Contradictory signals, unclear data |

**Conflict resolution**: When REAL contradicts SYNTHETIC, REAL wins. Document the delta — the gap between what synthetic research predicted and what real data showed is itself an insight.

## Source Weighting (default — adapt to your context)
- Direct research (interviews, usability tests): weight 5
- Analytics data (funnels, cohorts, events): weight 5
- Support tickets (unprompted pain signals): weight 4
- Survey results (structured quantitative): weight 4
- NPS verbatims: weight 3
- App store reviews: weight 2
- Sales call summaries (filtered through sales lens): weight 2
- Synthetic research (AI-generated): weight 1
- Anecdote or single report: weight 1

## Process
1. Accept inputs from any combination of source types
2. Tag each signal by source, apply weight, and assign evidence type + confidence
3. **CONVERGENCE**: same underlying need appearing across 3+ sources
   - Calculate combined confidence: highest individual confidence × (1 + 0.1 × number of confirming sources)
   - Cap at 1.0
4. **DIVERGENCE**: contradictory signals suggesting user segmentation
   - Don't average away disagreement — it usually means different user segments
5. **FREQUENCY RANKING**: count how many independent sources mention each insight
   - "N out of M sources mention this" (inspired by frequency-based evidence ranking)
6. 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")
7. Produce ranked insights by weighted frequency

## Output Format

### User Signal Synthesis — [Date / Period]
**Sources included:** [list with evidence type for each]
**Total signals processed:** [n]
**Evidence quality**: [% REAL / % SYNTHETIC / % INFERRED]

#### Insight 1: [Underlying need, not feature request]
- **Frequency**: [N/M sources] — [list which sources]
- **Evidence type**: [REAL/SYNTHETIC/INFERRED] combined confidence: [0.0-1.0]
- **Evidence**:
  - Analytics: [specific data point] (REAL, 0.8)
  - Survey: [specific finding] (REAL, 0.85)
  - Interviews: [quote or pattern] (REAL, 0.9)
  - Synthetic: [what AI predicted] (SYNTHETIC, 0.3)
- **Conflicting signals:** [Any contradicting evidence and how to interpret it]
- **REAL vs SYNTHETIC delta**: [Where synthetic research got it wrong/right]
- **Product implication:** [Specific, not generic]

[Repeat for top 3-5 insights, ordered by combined confidence × frequency]

#### Divergent Signals (Possible Segmentation)
[Where user groups appear to have genuinely different needs]
- Segment A says X (evidence: ...) while Segment B says Y (evidence: ...)
- Implication: [consider separate solutions or prioritize one segment]

#### What the Data Does NOT Tell Us
[Gaps that require further research before acting]
- [Gap 1]: would need [research method] to resolve
- [Gap 2]: low confidence because only SYNTHETIC evidence exists

#### Confidence Summary
| Hypothesis | Evidence Sources | Combined Confidence | Recommendation |
|-----------|-----------------|--------------------:|---------------|
| P1: ... | Analytics + Survey + Interviews | 0.93 | Confirmed |
| P2: ... | Synthetic only | 0.35 | Needs validation |
| P3: ... | Analytics + contradicts Survey | 0.55 | Investigate segment split |

---
> Source: [lenar-amirov/product-pipeline-public](https://github.com/lenar-amirov/product-pipeline-public) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-18 -->