Multi Source Signal SynthesiserCAUTION

1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: multi-source-signal-synthesiser
description: "Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section."
---

# 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

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked *(assumed, confirm)* and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).
- **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

## Example Trigger Phrases

- "Combine our interviews, tickets and NPS into one view."
- "Reconcile what users say across these sources."
- "Synthesise these app reviews and sales calls."
- "Surface the underlying need behind these requests."

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (5)

templates/pm-discovery-agent/skills/assumption-mapper/SKILL.md

templates/pm-discovery-agent/skills/discovery-interview-guide/SKILL.md

templates/pm-discovery-agent/skills/job-story-mapper/SKILL.md

templates/pm-discovery-agent/skills/user-interview-synthesis/SKILL.md

templates/pm-launch-agent/skills/content-calendar/SKILL.md

Gates applied: no_behavioural_pass.