# Feedback Synthesizer > Analyzes user feedback from multiple sources, identifies patterns and themes, prioritizes by impact, and produces stakeholder-ready reports - Skill: `vodouai/feedback-synthesizer` (Agent Skill) - Install (CLI): `npx skillmds@latest add vodouai/feedback-synthesizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/vodouai/feedback-synthesizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: VodouAI (https://skillmd.com/u/vodouai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/vodouai/feedback-synthesizer --- # Feedback Synthesizer Agent ## Overview This agent turns raw user feedback -- reviews, support tickets, surveys, interview transcripts, social mentions -- into structured insights that drive product decisions. Use it when you have a pile of feedback and need to extract what matters, or when stakeholders need a clear picture of what users are saying. The agent works across three modes: batch analysis (process a set of feedback at once), ongoing synthesis (track trends over time), and stakeholder reporting (package findings for decision-makers). **STOPPING POINT 1**: What do you need right now? 1. **Analyze a batch of feedback** - Process a collection of reviews, tickets, or survey responses and extract themes 2. **Identify patterns across sources** - Cross-reference feedback from multiple channels to find recurring issues 3. **Prioritize issues by impact** - Rank identified themes by user impact, frequency, and business risk 4. **Build a stakeholder report** - Package feedback analysis into a presentation-ready format 5. **Track trends over time** - Compare current feedback against historical data to spot shifts 6. **Design a feedback collection system** - Set up processes to systematically gather and categorize feedback --- ## Workflow 1: Analyze a Batch of Feedback ### Step 1: Prepare the feedback corpus Gather all feedback into a single working document. For each piece of feedback, capture: ``` FEEDBACK ENTRY: - Source: [app store review / support ticket / survey / interview / social / internal] - Date: [when received] - User segment: [new user / power user / churned / enterprise / free tier] - Verbatim: [exact user words] - Sentiment: [positive / negative / neutral / mixed] - Product area: [onboarding / core feature / billing / performance / UX / other] ``` ### Step 2: First-pass categorization Read through every entry and tag each one with: **Category tags** (a single entry can have multiple): - `bug` -- something is broken - `ux-friction` -- it works but is confusing or slow - `feature-request` -- user wants something new - `praise` -- user highlights something positive - `churn-signal` -- user indicates they may leave or has left - `pricing` -- feedback about cost or value perception - `performance` -- speed, reliability, uptime concerns - `onboarding` -- first-time experience issues - `integration` -- connecting with other tools **Severity assessment:** - **Critical**: User cannot accomplish their goal, mentions leaving, or reports data loss - **High**: Significant friction but workaround exists - **Medium**: Annoyance or nice-to-have improvement - **Low**: Minor cosmetic or preference issue ### Step 3: Theme extraction Group tagged feedback into clusters. A theme requires at least 3 entries from at least 2 different sources to qualify as a pattern (not an outlier). For each theme, document: ``` THEME: [descriptive name] Entry count: [number] Sources: [which channels] User segments affected: [which segments] Representative quotes: 1. "[verbatim quote]" - [source, date] 2. "[verbatim quote]" - [source, date] 3. "[verbatim quote]" - [source, date] Root cause hypothesis: [why this is happening] Severity distribution: [X critical / Y high / Z medium] ``` **STOPPING POINT 2**: Themes have been extracted. How should we proceed? 1. **Deep-dive on top 3 themes** - Analyze the highest-impact themes in detail with root cause analysis 2. **Quantify all themes** - Build a frequency and impact matrix across all identified themes 3. **Cross-reference with product roadmap** - Map themes against planned work to find gaps and overlaps 4. **Generate quick-hit recommendations** - Produce a list of fast actions the team can take immediately 5. **Segment analysis** - Break down themes by user segment to understand who is most affected --- ## Workflow 2: Prioritize Issues by Impact ### Impact Scoring Framework Score each theme on four dimensions (1-5 scale each): | Dimension | 1 (Low) | 3 (Medium) | 5 (High) | |-----------|---------|------------|----------| | **Frequency** | < 5 mentions | 10-25 mentions | 50+ mentions | | **Severity** | Cosmetic annoyance | Workflow disruption | Blocks core task or causes churn | | **Breadth** | Single user segment | Multiple segments | All users affected | | **Trend** | Declining or stable | Consistent | Accelerating | **Impact Score** = (Frequency x 1) + (Severity x 2) + (Breadth x 1.5) + (Trend x 1.5) Maximum possible score: 30. Prioritize anything above 20 as urgent. ### Effort Estimation For each high-impact theme, estimate what fixing it would require: - **Quick win** (< 1 week): Config change, copy update, minor UI tweak - **Small project** (1-2 weeks): Feature modification, new component, API change - **Medium project** (2-6 weeks): New feature, significant refactor, cross-team work - **Large initiative** (6+ weeks): Architecture change, new system, platform shift ### Impact/Effort Matrix Plot themes on a 2x2: ``` HIGH IMPACT | DO NEXT | PLAN & SCHEDULE (High impact, | (High impact, low effort) | high effort) | ----------------------+------------------------ | FILL-IN WORK | DEPRIORITIZE (Low impact, | (Low impact, low effort) | high effort) | LOW IMPACT ``` **STOPPING POINT 3**: Impact analysis is complete. What next? 1. **Build the prioritized backlog** - Turn the top themes into specific, actionable tickets 2. **Create the stakeholder report** - Package the full analysis for leadership review 3. **Design validation plan** - Plan how to verify that fixes actually address the feedback 4. **Set up ongoing tracking** - Create a system to monitor these themes going forward --- ## Workflow 3: Stakeholder Report ### Report Structure ``` FEEDBACK ANALYSIS REPORT Period: [date range] Sources analyzed: [list with counts] Total feedback entries: [number] Report prepared: [date] EXECUTIVE SUMMARY - [1-2 sentence overview of the most important finding] - [Key trend or shift from previous period] - [Top recommendation] TOP THEMES (ranked by impact score) 1. [Theme name] - Impact Score: [X/30] What users are saying: [2-3 sentence summary] Representative quote: "[verbatim]" Affected segments: [list] Recommended action: [specific next step] Effort estimate: [quick win / small / medium / large] 2. [Theme name] - Impact Score: [X/30] ... POSITIVE SIGNALS - [What users love - important to protect these] TREND ANALYSIS - [How this period compares to previous] - [Emerging issues not yet critical] - [Issues that have improved] RECOMMENDED ACTIONS (prioritized) 1. [Action] - Owner: [team] - Timeline: [estimate] 2. [Action] - Owner: [team] - Timeline: [estimate] 3. [Action] - Owner: [team] - Timeline: [estimate] APPENDIX - Full theme breakdown with entry counts - Raw data summary by source - Methodology notes ``` ### Delivery guidance - Lead with the single most important finding, not a data dump - Include verbatim quotes -- they carry more weight than summaries - Always pair problems with recommended actions - Show trends, not just snapshots -- stakeholders want to know direction - Highlight what is going well, not just problems -- teams need to know what to protect --- ## Workflow 4: Track Trends Over Time ### Tracking cadence Set up a recurring synthesis (weekly or biweekly): 1. Process new feedback since last synthesis 2. Tag and categorize using the same framework 3. Update theme counts and severity distributions 4. Compare against previous period: - New themes that appeared - Existing themes that grew or shrank - Themes that resolved (count dropped to near zero) 5. Update the running trend document ### Trend indicators For each tracked theme, maintain: ``` THEME TREND LOG: [name] First identified: [date] Current status: [growing / stable / declining / resolved] Period | Count | Avg Severity | Notable shifts ----------|-------|--------------|---------------- [date] | [n] | [1-5] | [notes] [date] | [n] | [1-5] | [notes] ``` Flag any theme where count increased by more than 50% period-over-period as an emerging risk. **STOPPING POINT 4**: Trend analysis is ready. What would you like to do? 1. **Generate a trend alert** - Create a focused alert on the fastest-growing issues 2. **Build a historical report** - Show how feedback has evolved over multiple periods 3. **Correlate with product changes** - Map feedback shifts against releases and changes 4. **Update the stakeholder report** - Refresh the report with new trend data 5. **Redesign collection strategy** - Adjust what feedback you collect based on what you have learned --- ## Feedback Categorization Reference ### Sentiment classification rules - **Positive**: User expresses satisfaction, recommends product, describes delight - **Negative**: User expresses frustration, describes failure, threatens to leave - **Neutral**: User states facts without emotional charge, asks questions - **Mixed**: User praises some aspects while criticizing others (tag both areas separately) ### Source reliability weighting Not all feedback sources carry equal signal: | Source | Weight | Rationale | |--------|--------|-----------| | Churned user exit interviews | Highest | They actually left -- this is the strongest signal | | Support tickets (repeated) | High | User took effort to contact, multiple times | | In-app feedback | High | Contextual, in-the-moment | | Survey responses | Medium | Prompted, may not reflect top-of-mind issues | | App store reviews | Medium | Public, but skews to extremes | | Social mentions | Lower | Often missing context, can be performative | | Internal team feedback | Variable | Useful but can reflect builder bias, not user reality | ### Common analysis pitfalls - **Loudest voice bias**: One vocal user submitting 20 tickets is not a pattern. Deduplicate by user. - **Recency bias**: New feedback feels more urgent than old feedback. Check whether the issue is actually new. - **Survivorship bias**: Current users cannot tell you why non-users did not sign up. Supplement with acquisition data. - **Solution bias**: Users often request specific solutions ("add a button for X") when the real problem is different. Always look for the underlying need. - **Positive feedback blindness**: Teams naturally focus on complaints. Actively track what users love to avoid breaking it.