feedback-synthesis

name	feedback-synthesis
description	汇总和综合来自多个来源的用户反馈。当用户需要分析用户反馈、对反馈主题进行分类、了解用户情绪，或说"分析反馈"、"用户在说什么"、"综合反馈"时使用。即使没有明确说"反馈综合"，当用户正在处理来自任何来源的用户反馈时也应激活。 Also triggers on: analyze feedback, what are users saying, synthesize user feedback.
layer	validation
input-from	user
output-to	retrospective,product-strategy

Feedback Synthesis

Turn user noise into actionable insights.

What This Skill Does

Aggregates feedback from multiple sources, identifies themes and patterns, categorizes by severity and frequency, and synthesizes into actionable insights. Makes sense of scattered user input.

When to Use

Activate this skill when:

• User needs to analyze feedback from any source
• Phrases like "analyze feedback", "what are users saying", "synthesize feedback"
• Reviewing feedback for planning or prioritization
• Understanding user sentiment and themes
• User says "what do users think" or "feedback analysis"

How It Works

The synthesis process ensures comprehensive feedback understanding:

1. Collect feedback - Gather from all sources
2. Deduplicate - Remove duplicates and merge related
3. Categorize - Group by theme, type, severity
4. Quantify - Count frequency, measure sentiment
5. Prioritize - Rank by impact and urgency
6. Synthesize - Generate insights and recommendations

Input Parameters

Parameter	Type	Required	Description
sources	list	No	Feedback sources (default: all available)
timeframe	string	No	Period to analyze (default: "last 30 days")
focus	string	No	Specific feature or area to focus on

Feedback Sources

Source	Type	What It Provides
Support tickets	Structured	Detailed issues, urgent problems
App reviews	Public	Overall sentiment, common complaints
User interviews	Qualitative	Deep insights, nuanced feedback
Surveys	Structured	Quantifiable satisfaction, NPS
In-app feedback	Contextual	Real-time, situation-specific
Social media	Public	Brand perception, common requests
Sales/CS notes	Filtered	Prospect/customer requests

Output Structure

The skill generates synthesis outputs:

1. Feedback summary - High-level overview of themes
2. Categorized items - Feedback grouped by type
3. Prioritized list - Ranked for action
4. Sentiment analysis - Overall user sentiment trends

Feedback Summary Format

# User Feedback Synthesis
**Period**: [Start] - [End]
**Total Feedback Items**: [Number]
**Primary Sources**: [List]

## Executive Summary

### Overall Sentiment
| Metric | Value | Change from Previous |
|:-------|:-----|:----------------------|
| Overall Sentiment | 🟢 Positive / 🟡 Neutral / 🔴 Negative | [Change] |
| NPS Score | [Score] | [+/- X] |
| Satisfaction | [Score]/5 | [+/- X] |
| Total Feedback | [Count] | [+/- X] |

### Top Themes This Period
1. **[Theme 1]** - [Count] mentions, [trend]
2. **[Theme 2]** - [Count] mentions, [trend]
3. **[Theme 3]** - [Count] mentions, [trend]

## Categorized Feedback

### Bugs & Issues
| Issue | Count | Severity | Trend | Status |
|:------|:-----|:--------|:------|:--------|
| [Bug description] | 25 | High | ↗ Increasing | 🔄 Investigating |
| [Bug description] | 12 | Medium | → Stable | ✅ Fixed |
| [Bug description] | 8 | Low | ↘ Decreasing | 📋 Backlog |

**Total bugs**: 45 | **High severity**: 15

### Feature Requests
| Request | Count | Priority | Segment | Estimate |
|:--------|:-----|:---------|:--------|:---------|
| [Feature request] | 38 | P0 | Enterprise | 2 weeks |
| [Feature request] | 24 | P1 | SMB | 1 week |
| [Feature request] | 19 | P2 | All | 3 days |

**Total requests**: 81 | **Top 3 account for 45% of volume

### UX & Usability
| Issue | Count | Impact | Location | Fix |
|:------|:-----|:-------|:---------|:----|
| [UX problem] | 32 | High | Onboarding | Quick |
| [UX problem] | 21 | Medium | Settings | Moderate |
| [UX problem] | 15 | Low | Dashboard | Complex |

**Total UX issues**: 68

### Performance & Reliability
| Issue | Count | Severity | Trend |
|:------|:-----|:--------|:------|
| [Performance issue] | 18 | Medium | ↘ Improving |
| [Performance issue] | 9 | High | → Stable |
| [Performance issue] | 5 | Low | → Stable |

### Praise & Positive Feedback
| Theme | Count | Representative Quote |
|:------|:-----|:---------------------|
| [Positive theme] | 24 | "Love how easy this is!" |
| [Positive theme] | 18 | "Finally, a feature that works" |
| [Positive theme] | 12 | "Customer support is amazing" |

**Positive mentions**: 54 (35% of total feedback)

## Detailed Analysis

### Top Issues (by Impact)

#### 1. [Issue Title]
**VolumeSeverityTrendUser QuotesImpactRecommended ActionPriorityEffortVolumeSeverityTrend

Week 1 |████████| 4.2/5 - Mostly Positive Week 2 |███████| 3.9/5 - Slight Decline Week 3 |██████| 3.7/5 - Quality Issue? Week 4 |█████████| 4.3/5 - Recovery After Fix

**Key Event**: [Explanation of notable changes]

## Recommendations

### Immediate (This Week)
1. **Fix blocking bug**: [Bug] affecting [X] users
2. **Address top complaint**: [Complaint] with [Y] mentions
3. **Acknowledge feedback**: Respond to [Z] pending items

### Short-term (This Month)
1. **Implement quick win**: [Feature] requested by [X] users
2. **Improve onboarding**: [Y]% drop off at step [Z]
3. **Address performance**: [Performance issue]

### Long-term (This Quarter)
1. **Evaluate strategic request**: [Feature] with revenue impact
2. **UX improvements**: [Top UX issues] grouped
3. **Proactive outreach**: [Segment] showing declining sentiment

## Feedback Loop Actions

This analysis triggers the following actions:
- **New high-severity bugs** → Engineering escalation
- **Common feature requests** → Consider for prioritization
- **Declining sentiment trend** → Activate market-research skill
- **UX patterns** → Update design system

## Appendices

### Source Breakdown
| Source | Items | % of Total | Primary Themes |
|:--------|:-----|:----------|:---------------|
| Support tickets | 95 | 40% | Bugs, how-to |
| App reviews | 62 | 26% | Features, UX |
| In-app feedback | 42 | 18% | UX, bugs |
| User interviews | 24 | 10% | Deep insights |
| Social media | 16 | 6% | Brand, requests |

### Methodology
- **Deduplication**: Items merged if similar intent + same source area
- **Severity**: Based on user impact, not volume
- **Trend**: Compared to previous period (last 30 days)
- **Sentiment**: Calculated from coded feedback (positive/neutral/negative)

Feedback Categories

Category	Subtypes	Example
Bugs	Broken, error, crash	"App crashes when I click save"
Features	Request, enhancement	"Would love to see X feature"
UX	Confusing, hard to use	"Can't find how to do X"
Performance	Slow, laggy	"Takes too long to load"
Praise	Compliment, love	"This is amazing!"
Pricing	Expensive, confused	"Too expensive for what I get"

Quality Standards

Before delivering, ensure synthesis:

• Covers all feedback sources
• Removes duplicates appropriately
• Quantifies frequency and severity
• Identifies trends, not just snapshots
• Provides actionable recommendations
• Triggers appropriate feedback loops

Context Integration

Reads:

• docs/product/release-plan.md - Release scope to categorize feedback
• docs/product/.ompm/impact-analysis.json - Combine quantitative and qualitative

Writes:

• docs/product/.ompm/feedback-synthesis.json - Structured synthesis data
• docs/product/feedback-reports/[period].md - Full synthesis report

Read By:

• retrospective - Uses feedback for planning
• product-strategy - Incorporates requests into prioritization
• market-research - Identifies areas needing deeper research

Example Usage

User: "What are users saying about the new feature?"
→ Synthesizes feedback specifically about that feature

User: "Analyze all feedback from last month"
→ Generates comprehensive synthesis report

User: "What are the top complaints?"
→ Summarizes highest-volume negative feedback

Best Practices

1. Look for patterns - One-off vs. systemic
2. Read the quotes - Numbers don't tell the whole story
3. Segment users - Different users have different needs
4. Track trends - Is it getting better or worse?
5. Close the loop - Let users know their feedback matters

Execution Profile (执行建议)

• 模型建议：轻量(haiku 级)——宿主支持多模型/子代理时按此分配；单模型宿主忽略
• 工具姿态：读写（无需联网，反馈材料由用户提供）——遵循最小权限
• 记忆文件：docs/product/.ompm/memory/feedback-synthesis.md——跨会话积累经验（优质信源、领域基线、分析框架）；执行开始时读取、结束时更新；文件不存在则新建
• Claude Code 增强：本 profile 在该宿主由 feedback-collector subagent 以隔离上下文实现

下一步

本 skill 完成后，如果用户没有明确下一步，引导用户使用 ompm skill 做意图路由——它会读取本轮产出和当前状态，判断最有价值的下一步。不要替用户预设固定长链；output-to 声明的是数据流向，不是强制路径。