voice-of-customer-synthesizer
Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.
- Installs
- 51
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill voice-of-customer-synthesizer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads voice-of-customer-synthesizer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Voice of Customer Synthesizer
Turn scattered customer feedback into a single source of truth. Aggregates signals from every source you have, clusters them into themes, and produces a report that product, marketing, and CS teams can actually act on.
Built for: Startups where customer feedback lives in 6 different places and nobody has time to synthesize it. The founder says "what are customers saying?" and nobody has a clear answer. This skill produces that answer.
When to Use
- "What are our customers saying?"
- "Synthesize customer feedback from last quarter"
- "Build a VoC report for the product team"
- "What themes are coming up in customer feedback?"
- "Aggregate feedback from all our channels"
Phase 0: Intake
Feedback Sources (provide all you have)
- Support tickets — Export from support tool (CSV: customer, date, subject, description, tags, resolution)
- NPS/CSAT survey responses — Scores + verbatim comments
- Slack messages — Customer channel messages, feedback channels
- G2/Capterra reviews — Will scrape if product is listed (provide product name or URL)
- Call/meeting transcripts — Customer call recordings or notes
- Churn exit survey responses — Why did customers leave?
- Feature request log — Internal tracker of what customers have asked for
- Social mentions — Twitter/LinkedIn/Reddit threads mentioning your product
- Email threads — Notable customer emails (praise or complaints)
- In-app feedback — Any in-product feedback submissions
Configuration
- Time period — What window to analyze? (Last 30 days, quarter, 6 months)
- Product name — For review scraping and context
- Report audience — Who's reading this? (Product team, exec team, CS team, all)
- Focus areas — Any specific themes to pay attention to? (e.g., "onboarding experience", "pricing feedback", "mobile app")
Phase 1: Data Collection
1A: Internal Data Processing
From the provided inputs, normalize all feedback into a standard format:
SOURCE | DATE | CUSTOMER | SEGMENT | FEEDBACK_TEXT | SENTIMENT | CATEGORY
Sentiment classification per item:
- Positive — Praise, satisfaction, delight
- Neutral — Feature request, question, observation
- Negative — Complaint, frustration, disappointment
- Critical — Churn threat, escalation, anger
1B: External Review Scraping (if applicable)
If product is on review platforms:
Chain: review-site-scraper for G2, Capterra, Trustpilot
Filter: reviews from the target time period
Extract: rating, review text, reviewer role/company size, date, pros, cons.
1C: Social Listening (if applicable)
Search: "[product name]" feedback OR review OR "switched to" OR "stopped using"
Search: "[product name]" site:reddit.com OR site:twitter.com
Phase 2: Theme Clustering
Group all feedback items into themes using a bottom-up approach:
Clustering Method
- Read all feedback items
- Identify recurring topics (mentioned by 3+ customers or in 3+ sources)
- Group into theme clusters
- Rank by frequency AND severity
Theme Template
THEME: [Name — e.g., "Onboarding Complexity"]
FREQUENCY: [N mentions across M sources]
SENTIMENT: [Predominantly positive/neutral/negative]
TREND: [↑ Growing / → Stable / ↓ Declining vs prior period]
REPRESENTATIVE QUOTES:
- "[Exact quote]" — [Source, Customer segment, Date]
- "[Exact quote]" — [Source, Customer segment, Date]
- "[Exact quote]" — [Source, Customer segment, Date]
CUSTOMER SEGMENTS AFFECTED:
- [Segment 1: e.g., "New customers in first 30 days"]
- [Segment 2: e.g., "Enterprise accounts"]
ROOT CAUSE HYPOTHESIS:
[1-2 sentences: Why is this coming up? What's the underlying issue?]
IMPACT:
- On retention: [High/Medium/Low]
- On expansion: [High/Medium/Low]
- On acquisition: [High/Medium/Low]
Phase 3: Analysis
3A: Sentiment Overview
Overall Sentiment Distribution:
Positive: [N] items ([X%]) ████████░░
Neutral: [N] items ([X%]) ████░░░░░░
Negative: [N] items ([X%]) ██░░░░░░░░
Critical: [N] items ([X%]) █░░░░░░░░░
3B: Source Comparison
| Source | Volume | Avg Sentiment | Top Theme |
|---|---|---|---|
| Support tickets | [N] | [Pos/Neg score] | [Theme] |
| NPS comments | [N] | [Score] | [Theme] |
| G2 reviews | [N] | [Score] | [Theme] |
| Slack | [N] | [Score] | [Theme] |
| Calls | [N] | [Score] | [Theme] |
Insight: Different sources often reveal different stories. Support tickets skew negative (problems). Reviews skew bipolar (love/hate). Calls reveal nuance. Note where themes appear across sources for highest confidence.
3C: Segment Analysis
| Customer Segment | Dominant Sentiment | Top Request | Key Pain |
|---|---|---|---|
| [New customers] | [Sentiment] | [Request] | [Pain] |
| [Power users] | [Sentiment] | [Request] | [Pain] |
| [Enterprise] | [Sentiment] | [Request] | [Pain] |
| [Churned] | [Sentiment] | [Request] | [Pain] |
3D: Trend Detection
Compare against prior period (if available):
| Theme | Prior Period | This Period | Trend | Alert |
|---|---|---|---|---|
| [Theme 1] | [N mentions] | [N mentions] | [↑X%] | [New/Growing/Stable/Declining] |
| [Theme 2] | ... | ... | ... | ... |
New themes this period: [Themes that weren't present before] Resolved themes: [Themes that decreased significantly — things you fixed]
Phase 4: Recommendations
For Product Team
| Priority | Theme | Recommendation | Evidence Strength |
|---|---|---|---|
| P0 | [Theme] | [Specific action] | [N mentions, M sources, includes churn signals] |
| P1 | [Theme] | [Action] | [Evidence] |
| P2 | [Theme] | [Action] | [Evidence] |
For CS/Support Team
| Action | Theme | Expected Impact |
|---|---|---|
| [Create help article for X] | [Theme] | Deflect ~[N] tickets/month |
| [Add onboarding step for Y] | [Theme] | Reduce confusion for new users |
| [Proactive outreach to segment Z] | [Theme] | Prevent churn in at-risk segment |
For Marketing Team
| Action | Theme | Opportunity |
|---|---|---|
| [Use this proof point in messaging] | [Positive theme] | "[Customer quote ready for marketing]" |
| [Address this objection on website] | [Negative theme] | Counter common concern pre-sale |
| [Build case study around X] | [Positive theme] | [N] customers mentioned this win |
Phase 5: Output Format
# Voice of Customer Report — [Period]
Sources analyzed: [list]
Total feedback items: [N]
Date range: [start] — [end]
---
## Executive Summary
[3-5 sentences: What are customers saying? What's the overall sentiment?
What's the single most important thing to act on?]
---
## Sentiment Overview
Positive: [X%] | Neutral: [X%] | Negative: [X%] | Critical: [X%]
Net Sentiment Score: [calculated — % positive minus % negative]
vs Prior Period: [+/- X points]
---
## Top Themes (Ranked by Impact)
### 1. [Theme Name] — [Sentiment] — [N mentions]
**Summary:** [2-3 sentences]
**Key quotes:**
> "[Quote]" — [Source]
> "[Quote]" — [Source]
**Recommended action:** [What to do]
**Owner:** [Product / CS / Marketing]
### 2. [Theme Name] — ...
### 3. [Theme Name] — ...
[Continue for top 5-8 themes]
---
## What Customers Love (Preserve These)
| Strength | Evidence | Marketing Opportunity |
|----------|---------|----------------------|
| [Feature/experience] | "[Quote]" — [N mentions] | [How to use in messaging] |
---
## What Customers Want (Feature Requests)
| Request | Frequency | Segments | Product Priority |
|---------|-----------|----------|-----------------|
| [Feature] | [N mentions] | [Who wants it] | [P0/P1/P2] |
---
## What Causes Pain (Fix These)
| Pain Point | Severity | Churn Risk | Recommended Fix |
|-----------|----------|------------|----------------|
| [Issue] | [High/Med/Low] | [Yes/No] | [Action] |
---
## Trends vs Prior Period
[What's getting better, what's getting worse, what's new]
---
## Team-Specific Action Items
### Product Team
1. [Action] — [Evidence]
### CS Team
1. [Action] — [Evidence]
### Marketing Team
1. [Action] — [Evidence]
---
## Appendix: All Themes Detail
[Full theme cards with all quotes and analysis]
Save to voc-report-[YYYY-MM-DD].md in the current working directory.
Scheduling
Run monthly or quarterly:
0 8 1 */3 * python3 run_skill.py voice-of-customer-synthesizer --client <client-name>
Cost
| Component | Cost |
|---|---|
| Review scraping (via review-site-scraper) | ~$0.50-1.00 |
| Web search (social mentions) | Free |
| All analysis and synthesis | Free (LLM reasoning) |
| Total | Free — $1 |
Tools Required
- Optional:
review-site-scraperfor G2/Capterra/Trustpilot reviews - Optional:
twitter-mention-trackerfor social mentions - Optional:
reddit-post-finderfor community feedback - All analysis is pure LLM reasoning on provided data
Trigger Phrases
- "What are customers saying?"
- "Build a VoC report"
- "Synthesize our customer feedback"
- "Run voice of customer analysis"
- "Customer feedback summary for [period]"
FAQ
What does the voice-of-customer-synthesizer skill do?
Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.
How do I install voice-of-customer-synthesizer?
Run npx skills add https://github.com/gooseworks-ai/goose-skills --skill voice-of-customer-synthesizer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/voice-of-customer-synthesizer/ (personal) or .claude/skills/voice-of-customer-synthesizer/ (project).
Which AI agents can use voice-of-customer-synthesizer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is voice-of-customer-synthesizer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains voice-of-customer-synthesizer?
It is published by gooseworks-ai in gooseworks-ai/goose-skills. It has 51 recorded installs.
More from gooseworks-ai/goose-skills
restaurant-booking
gooseworks-ai/goose-skills
Book restaurant reservations via browser automation. Use when asked to make dinner reservations, book a table, or find availability at restaurants. Supports OpenTable, Resy, and direct restaurant booking sites.
send-sms-textbelt
gooseworks-ai/goose-skills
Send SMS text messages to phone numbers. Use when the user asks to send a text, send an SMS, text someone, message a phone number, or send a notification via text message.
job-scraper
gooseworks-ai/goose-skills
Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.
linkedin-outreach
gooseworks-ai/goose-skills
End-to-end LinkedIn outreach campaign builder. Takes leads from Supabase, upstream skills, or CSV. Aligns on campaign goal and tone, writes personalized LinkedIn message sequences (connection request + follow-ups + optional InMail), presents for review, and exports for the user's outreach tool (Dripify, Botdog, Expandi, or manual CSV). Logs to Supabase outreachlog.
find-skill
gooseworks-ai/goose-skills
Find and install skills from the Orthogonal skill library. Use when you need capabilities you don't have, want to discover available skills, or need to add new tools to your agent.
linkedin-job-scraper
gooseworks-ai/goose-skills
Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.
Popular media & creative skills
hyperframes
heygen-com/hyperframes
Read first to make, edit, animate or render any video with HyperFrames (HTML rendered to video), including launch films, explainers, music videos, charts, maps, vertical clips, title cards, captions and overlays. Holds the render contract, the work loop and what each kind of video needs.
media-use
heygen-com/hyperframes
Get or make any media for a HyperFrames video - music, sound effects, images, icons, logos, voiceover, avatar and generated video, transcripts, cutouts, colour grades and looks - with HeyGen or free local tools, and mix placed audio. Also for footage that looks dark, flat or dull, or should be stylized or hidden.
hyperframes-registry
heygen-com/hyperframes
About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.
hyperframes-creative
heygen-com/hyperframes
Non-animation creative direction for HyperFrames videos. Use for design spec (frame.md / design.md) handling, palettes, typography, narration, beat planning, audio-reactive visuals, composition patterns, and brand / style decisions. For atomic motion patterns and scene blueprints, use hyperframes-animation.
ai-video-generation
101-skills/superpowers
Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, reference-to-video, video editing, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative, seedance, happyhorse
ai-image-generation
101-skills/superpowers
Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image