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fix: sync all skill updates and new skills into plugin bundles

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- Synced 97 existing skill SKILL.md files from skills/ to their plugin bundle copies
- Added 7 new skills to plugin bundles:
  - seo-content-brief, media-pitch -> pm-gtm
  - tax-planning-checklist -> pm-finance
  - change-management-plan -> pm-hr
  - sales-forecasting-model -> pm-sales
  - workshop-facilitation-guide -> pm-operations
  - teaching-lesson-plan -> pm-cross

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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mohitagw15856
2026-04-20 21:00:00 +01:00
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plugins/pm-advanced/skills/multi-source-signal-synthesiser/SKILL.md
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---
name: multi-source-signal-synthesiser
description: Synthesises user signals from multiple research sources into a
unified insight brief, reconciling conflicting feedback. Use when user has data
from multiple sources, needs to "make sense of all this user data", "what are
users really telling us", "synthesise our research", or has conflicting feedback
from different channels.
metadata:
author: Mohit Aggarwal
version: 1.0.0
category: discovery
tags: [user-research, synthesis, discovery, insights]
documentation: https://github.com/mohitagw15856/pm-claude-skills
description: "Synthesise user signals from multiple research sources into a unified insight brief, reconciling conflicting feedback. Use when asked to make sense of data from multiple sources, synthesise user research, reconcile conflicting feedback, or when the user says 'what are users really telling us' or 'make sense of all this user data'. Produces ranked insights with confidence ratings, divergent signal analysis, and research gap identification."
---
# 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.
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
## 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. 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
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 Format
## Output Structure
### User Signal Synthesis — [Date / Period]
**Sources included:** [list]
**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, not generic]
- **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]
[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]
## OpenClaw Configuration
Connect to: Notion (research docs), support inbox, NPS tool, app review feed.
Schedule: weekly synthesis run, diff output showing new signals only.
## 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