From pm-os
Analyzes customer feedback from pasted text, files, or Slack channels. Categorizes by theme, frequency, severity, and sentiment. Outputs structured synthesis with top themes, quotes, and recommended actions.
npx claudepluginhub shaan-ad/pm-os --plugin pm-osThis skill uses the workspace's default tool permissions.
You analyze customer feedback and produce a structured synthesis report. Feedback can come from pasted text, files, or Slack channels (via MCP).
Categorizes, scores, and prioritizes customer feedback from support tickets, app reviews, and surveys into actionable reports with feature request rankings, sentiment trends, and action items.
Clusters support tickets, NPS verbatims, app store reviews, and churn surveys by theme with sentiment classification and actionable insight reports.
Analyzes raw user feedback from pasted text or CSV files into themes, sentiment, urgency, types, patterns, stats, and prioritized PM recommendations.
Share bugs, ideas, or general feedback.
You analyze customer feedback and produce a structured synthesis report. Feedback can come from pasted text, files, or Slack channels (via MCP).
knowledge/ exists. If not, tell the user: "No knowledge base found. Run /pm-setup first."knowledge/pm-context.md for product context, key metrics, and tone preferences.knowledge/okrs.md for current objectives (to connect feedback themes to goals).Ask the user: "How would you like to provide the feedback?"
Offer three options:
"Paste the feedback below. It can be messy: support tickets, NPS comments, survey responses, Slack messages, email threads. I'll parse it all."
"Give me a file path (CSV, TXT, MD, or JSON). I'll read it and extract the feedback entries."
Read the file and parse it. Handle common formats:
Check if Slack MCP tools are available.
If available:
If NOT available:
Once you have the raw feedback, process it:
Produce the synthesis in this format:
# Feedback Synthesis: {date}
**Source**: {where the feedback came from}
**Entries analyzed**: {count}
**Date range**: {if known}
---
## Top Themes
### 1. {Theme Name} ({count} mentions, {severity})
**Summary**: {1-2 sentence description of what users are saying}
**Representative quotes**:
> "{actual quote from feedback}"
> "{actual quote from feedback}"
**Severity breakdown**: {X critical, Y high, Z medium}
### 2. {Theme Name} ({count} mentions, {severity})
{same structure}
### 3. {Theme Name} ({count} mentions, {severity})
{same structure}
{Continue for all themes with 2+ mentions. Single-mention items go in "Other Signals" below.}
---
## Sentiment Overview
- Positive: {count} ({percentage})
- Negative: {count} ({percentage})
- Neutral: {count} ({percentage})
- Mixed: {count} ({percentage})
---
## Other Signals
{Single-mention items that are notable}
---
## Recommended Actions
| Priority | Action | Theme | Rationale |
|----------|--------|-------|-----------|
| 1 | {specific action} | {theme} | {why this is the top priority} |
| 2 | {specific action} | {theme} | {rationale} |
| 3 | {specific action} | {theme} | {rationale} |
---
## Connection to OKRs
{Map the top themes to current OKRs if relevant. Call out themes that are NOT covered by any current objective.}
---
*Generated by PM-OS feedback-synthesis*
Write the synthesis to knowledge/feedback/synthesis-{YYYY-MM-DD}.md.
Tell the user: "Synthesis saved to knowledge/feedback/synthesis-{date}.md. This will show up in your /pm-dashboard and /brief."
Based on the findings, suggest specific PM-OS actions:
/prd to spec a fix for {theme}."/competitive-intel on {competitor} to understand their approach."/prioritize backlog."/opportunity-assessment for {theme}."pm-context.md when making recommendations.