[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DSCWFATX68C4M27SS5JF18":3,"skill-same:01M6DSCWFATX68C4M27SS5JF18":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6DSCWFATX68C4M27SS5JF18","gtm-voice-of-customer-synthesizer",null,"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-scraper for public review data.","AntifragileTech\u002Fantifragile-claude-code","AntifragileTech","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F174357194?v=4","01M6DQCYMK4CDSQQAN0ZC72NTV","01M6DQCYS9YPW1WKMQ5TJMPQ43",9,"MIT","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fgtm\u002Fgtm-voice-of-customer-synthesizer\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"voice","gtm voice of customer synthesizer","gtm","voice of customer","customer","support","teams","product","sentiment","messages","analysis","review","feedback","survey","call","trend","recommendations","research","customer feedback","slack","2026-11-10 10:12:30",false,"---\nname: gtm-voice-of-customer-synthesizer\ndescription: >\n  Aggregate customer feedback from multiple sources — support tickets, NPS comments,\n  Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC\n  report with theme clustering, sentiment analysis, trend detection, and actionable\n  recommendations for product, marketing, and CS teams. Chains review-scraper for public\n  review data.\ntags: [research]\n---\n\n# Voice of Customer Synthesizer\n\nTurn 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.\n\n**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.\n\n## When to Use\n\n- \"What are our customers saying?\"\n- \"Synthesize customer feedback from last quarter\"\n- \"Build a VoC report for the product team\"\n- \"What themes are coming up in customer feedback?\"\n- \"Aggregate feedback from all our channels\"\n\n## Phase 0: Intake\n\n### Feedback Sources (provide all you have)\n1. **Support tickets** — Export from support tool (CSV: customer, date, subject, description, tags, resolution)\n2. **NPS\u002FCSAT survey responses** — Scores + verbatim comments\n3. **Slack messages** — Customer channel messages, feedback channels\n4. **G2\u002FCapterra reviews** — Will scrape if product is listed (provide product name or URL)\n5. **Call\u002Fmeeting transcripts** — Customer call recordings or notes\n6. **Churn exit survey responses** — Why did customers leave?\n7. **Feature request log** — Internal tracker of what customers have asked for\n8. **Social mentions** — Twitter\u002FLinkedIn\u002FReddit threads mentioning your product\n9. **Email threads** — Notable customer emails (praise or complaints)\n10. **In-app feedback** — Any in-product feedback submissions\n\n### Configuration\n11. **Time period** — What window to analyze? (Last 30 days, quarter, 6 months)\n12. **Product name** — For review scraping and context\n13. **Report audience** — Who's reading this? (Product team, exec team, CS team, all)\n14. **Focus areas** — Any specific themes to pay attention to? (e.g., \"onboarding experience\", \"pricing feedback\", \"mobile app\")\n\n## Phase 1: Data Collection\n\n### 1A: Internal Data Processing\n\nFrom the provided inputs, normalize all feedback into a standard format:\n\n```\nSOURCE | DATE | CUSTOMER | SEGMENT | FEEDBACK_TEXT | SENTIMENT | CATEGORY\n```\n\nSentiment classification per item:\n- **Positive** — Praise, satisfaction, delight\n- **Neutral** — Feature request, question, observation\n- **Negative** — Complaint, frustration, disappointment\n- **Critical** — Churn threat, escalation, anger\n\n### 1B: External Review Scraping (if applicable)\n\nIf product is on review platforms:\n\n```\nChain: review-scraper for G2, Capterra, Trustpilot\nFilter: reviews from the target time period\n```\n\nExtract: rating, review text, reviewer role\u002Fcompany size, date, pros, cons.\n\n### 1C: Social Listening (if applicable)\n\n```\nSearch: \"[product name]\" feedback OR review OR \"switched to\" OR \"stopped using\"\nSearch: \"[product name]\" site:reddit.com OR site:twitter.com\n```\n\n## Phase 2: Theme Clustering\n\nGroup all feedback items into themes using a bottom-up approach:\n\n### Clustering Method\n\n1. Read all feedback items\n2. Identify recurring topics (mentioned by 3+ customers or in 3+ sources)\n3. Group into theme clusters\n4. Rank by frequency AND severity\n\n### Theme Template\n\n```\nTHEME: [Name — e.g., \"Onboarding Complexity\"]\nFREQUENCY: [N mentions across M sources]\nSENTIMENT: [Predominantly positive\u002Fneutral\u002Fnegative]\nTREND: [↑ Growing \u002F → Stable \u002F ↓ Declining vs prior period]\n\nREPRESENTATIVE QUOTES:\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n\nCUSTOMER SEGMENTS AFFECTED:\n- [Segment 1: e.g., \"New customers in first 30 days\"]\n- [Segment 2: e.g., \"Enterprise accounts\"]\n\nROOT CAUSE HYPOTHESIS:\n[1-2 sentences: Why is this coming up? What's the underlying issue?]\n\nIMPACT:\n- On retention: [High\u002FMedium\u002FLow]\n- On expansion: [High\u002FMedium\u002FLow]\n- On acquisition: [High\u002FMedium\u002FLow]\n```\n\n## Phase 3: Analysis\n\n### 3A: Sentiment Overview\n\n```\nOverall Sentiment Distribution:\n  Positive:  [N] items ([X%])  ████████░░\n  Neutral:   [N] items ([X%])  ████░░░░░░\n  Negative:  [N] items ([X%])  ██░░░░░░░░\n  Critical:  [N] items ([X%])  █░░░░░░░░░\n```\n\n### 3B: Source Comparison\n\n| Source | Volume | Avg Sentiment | Top Theme |\n|--------|--------|---------------|-----------|\n| Support tickets | [N] | [Pos\u002FNeg score] | [Theme] |\n| NPS comments | [N] | [Score] | [Theme] |\n| G2 reviews | [N] | [Score] | [Theme] |\n| Slack | [N] | [Score] | [Theme] |\n| Calls | [N] | [Score] | [Theme] |\n\n**Insight:** Different sources often reveal different stories. Support tickets skew negative (problems). Reviews skew bipolar (love\u002Fhate). Calls reveal nuance. Note where themes appear across sources for highest confidence.\n\n### 3C: Segment Analysis\n\n| Customer Segment | Dominant Sentiment | Top Request | Key Pain |\n|-----------------|-------------------|-------------|----------|\n| [New customers] | [Sentiment] | [Request] | [Pain] |\n| [Power users] | [Sentiment] | [Request] | [Pain] |\n| [Enterprise] | [Sentiment] | [Request] | [Pain] |\n| [Churned] | [Sentiment] | [Request] | [Pain] |\n\n### 3D: Trend Detection\n\nCompare against prior period (if available):\n\n| Theme | Prior Period | This Period | Trend | Alert |\n|-------|-------------|-------------|-------|-------|\n| [Theme 1] | [N mentions] | [N mentions] | [↑X%] | [New\u002FGrowing\u002FStable\u002FDeclining] |\n| [Theme 2] | ... | ... | ... | ... |\n\n**New themes this period:** [Themes that weren't present before]\n**Resolved themes:** [Themes that decreased significantly — things you fixed]\n\n## Phase 4: Recommendations\n\n### For Product Team\n\n| Priority | Theme | Recommendation | Evidence Strength |\n|----------|-------|---------------|-------------------|\n| P0 | [Theme] | [Specific action] | [N mentions, M sources, includes churn signals] |\n| P1 | [Theme] | [Action] | [Evidence] |\n| P2 | [Theme] | [Action] | [Evidence] |\n\n### For CS\u002FSupport Team\n\n| Action | Theme | Expected Impact |\n|--------|-------|----------------|\n| [Create help article for X] | [Theme] | Deflect ~[N] tickets\u002Fmonth |\n| [Add onboarding step for Y] | [Theme] | Reduce confusion for new users |\n| [Proactive outreach to segment Z] | [Theme] | Prevent churn in at-risk segment |\n\n### For Marketing Team\n\n| Action | Theme | Opportunity |\n|--------|-------|------------|\n| [Use this proof point in messaging] | [Positive theme] | \"[Customer quote ready for marketing]\" |\n| [Address this objection on website] | [Negative theme] | Counter common concern pre-sale |\n| [Build case study around X] | [Positive theme] | [N] customers mentioned this win |\n\n## Phase 5: Output Format\n\n```markdown\n# Voice of Customer Report — [Period]\nSources analyzed: [list]\nTotal feedback items: [N]\nDate range: [start] — [end]\n\n---\n\n## Executive Summary\n\n[3-5 sentences: What are customers saying? What's the overall sentiment?\nWhat's the single most important thing to act on?]\n\n---\n\n## Sentiment Overview\n\nPositive: [X%] | Neutral: [X%] | Negative: [X%] | Critical: [X%]\n\nNet Sentiment Score: [calculated — % positive minus % negative]\nvs Prior Period: [+\u002F- X points]\n\n---\n\n## Top Themes (Ranked by Impact)\n\n### 1. [Theme Name] — [Sentiment] — [N mentions]\n**Summary:** [2-3 sentences]\n**Key quotes:**\n> \"[Quote]\" — [Source]\n> \"[Quote]\" — [Source]\n**Recommended action:** [What to do]\n**Owner:** [Product \u002F CS \u002F Marketing]\n\n### 2. [Theme Name] — ...\n\n### 3. [Theme Name] — ...\n\n[Continue for top 5-8 themes]\n\n---\n\n## What Customers Love (Preserve These)\n\n| Strength | Evidence | Marketing Opportunity |\n|----------|---------|----------------------|\n| [Feature\u002Fexperience] | \"[Quote]\" — [N mentions] | [How to use in messaging] |\n\n---\n\n## What Customers Want (Feature Requests)\n\n| Request | Frequency | Segments | Product Priority |\n|---------|-----------|----------|-----------------|\n| [Feature] | [N mentions] | [Who wants it] | [P0\u002FP1\u002FP2] |\n\n---\n\n## What Causes Pain (Fix These)\n\n| Pain Point | Severity | Churn Risk | Recommended Fix |\n|-----------|----------|------------|----------------|\n| [Issue] | [High\u002FMed\u002FLow] | [Yes\u002FNo] | [Action] |\n\n---\n\n## Trends vs Prior Period\n\n[What's getting better, what's getting worse, what's new]\n\n---\n\n## Team-Specific Action Items\n\n### Product Team\n1. [Action] — [Evidence]\n\n### CS Team\n1. [Action] — [Evidence]\n\n### Marketing Team\n1. [Action] — [Evidence]\n\n---\n\n## Appendix: All Themes Detail\n\n[Full theme cards with all quotes and analysis]\n```\n\nSave to `clients\u002F\u003Cclient-name>\u002Fcustomer-success\u002Fvoc\u002Fvoc-report-[YYYY-MM-DD].md`.\n\n## Scheduling\n\nRun monthly or quarterly:\n\n```bash\n0 8 1 *\u002F3 * python3 run_skill.py voice-of-customer-synthesizer --client \u003Cclient-name>\n```\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| Review scraping (via review-scraper) | ~$0.50-1.00 |\n| Web search (social mentions) | Free |\n| All analysis and synthesis | Free (LLM reasoning) |\n| **Total** | **Free — $1** |\n\n## Tools Required\n\n- **Optional:** `review-scraper` for G2\u002FCapterra\u002FTrustpilot reviews\n- **Optional:** `twitter-scraper` for social mentions\n- **Optional:** `reddit-scraper` for community feedback\n- All analysis is pure LLM reasoning on provided data\n\n## Trigger Phrases\n\n- \"What are customers saying?\"\n- \"Build a VoC report\"\n- \"Synthesize our customer feedback\"\n- \"Run voice of customer analysis\"\n- \"Customer feedback summary for [period]\"\n","# Voice of Customer Synthesizer\n\nTurn 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.\n\n**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.\n\n## When to Use\n\n- \"What are our customers saying?\"\n- \"Synthesize customer feedback from last quarter\"\n- \"Build a VoC report for the product team\"\n- \"What themes are coming up in customer feedback?\"\n- \"Aggregate feedback from all our channels\"\n\n## Phase 0: Intake\n\n### Feedback Sources (provide all you have)\n1. **Support tickets** — Export from support tool (CSV: customer, date, subject, description, tags, resolution)\n2. **NPS\u002FCSAT survey responses** — Scores + verbatim comments\n3. **Slack messages** — Customer channel messages, feedback channels\n4. **G2\u002FCapterra reviews** — Will scrape if product is listed (provide product name or URL)\n5. **Call\u002Fmeeting transcripts** — Customer call recordings or notes\n6. **Churn exit survey responses** — Why did customers leave?\n7. **Feature request log** — Internal tracker of what customers have asked for\n8. **Social mentions** — Twitter\u002FLinkedIn\u002FReddit threads mentioning your product\n9. **Email threads** — Notable customer emails (praise or complaints)\n10. **In-app feedback** — Any in-product feedback submissions\n\n### Configuration\n11. **Time period** — What window to analyze? (Last 30 days, quarter, 6 months)\n12. **Product name** — For review scraping and context\n13. **Report audience** — Who's reading this? (Product team, exec team, CS team, all)\n14. **Focus areas** — Any specific themes to pay attention to? (e.g., \"onboarding experience\", \"pricing feedback\", \"mobile app\")\n\n## Phase 1: Data Collection\n\n### 1A: Internal Data Processing\n\nFrom the provided inputs, normalize all feedback into a standard format:\n\n```\nSOURCE | DATE | CUSTOMER | SEGMENT | FEEDBACK_TEXT | SENTIMENT | CATEGORY\n```\n\nSentiment classification per item:\n- **Positive** — Praise, satisfaction, delight\n- **Neutral** — Feature request, question, observation\n- **Negative** — Complaint, frustration, disappointment\n- **Critical** — Churn threat, escalation, anger\n\n### 1B: External Review Scraping (if applicable)\n\nIf product is on review platforms:\n\n```\nChain: review-scraper for G2, Capterra, Trustpilot\nFilter: reviews from the target time period\n```\n\nExtract: rating, review text, reviewer role\u002Fcompany size, date, pros, cons.\n\n### 1C: Social Listening (if applicable)\n\n```\nSearch: \"[product name]\" feedback OR review OR \"switched to\" OR \"stopped using\"\nSearch: \"[product name]\" site:reddit.com OR site:twitter.com\n```\n\n## Phase 2: Theme Clustering\n\nGroup all feedback items into themes using a bottom-up approach:\n\n### Clustering Method\n\n1. Read all feedback items\n2. Identify recurring topics (mentioned by 3+ customers or in 3+ sources)\n3. Group into theme clusters\n4. Rank by frequency AND severity\n\n### Theme Template\n\n```\nTHEME: [Name — e.g., \"Onboarding Complexity\"]\nFREQUENCY: [N mentions across M sources]\nSENTIMENT: [Predominantly positive\u002Fneutral\u002Fnegative]\nTREND: [↑ Growing \u002F → Stable \u002F ↓ Declining vs prior period]\n\nREPRESENTATIVE QUOTES:\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n- \"[Exact quote]\" — [Source, Customer segment, Date]\n\nCUSTOMER SEGMENTS AFFECTED:\n- [Segment 1: e.g., \"New customers in first 30 days\"]\n- [Segment 2: e.g., \"Enterprise accounts\"]\n\nROOT CAUSE HYPOTHESIS:\n[1-2 sentences: Why is this coming up? What's the underlying issue?]\n\nIMPACT:\n- On retention: [High\u002FMedium\u002FLow]\n- On expansion: [High\u002FMedium\u002FLow]\n- On acquisition: [High\u002FMedium\u002FLow]\n```\n\n## Phase 3: Analysis\n\n### 3A: Sentiment Overview\n\n```\nOverall Sentiment Distribution:\n  Positive:  [N] items ([X%])  ████████░░\n  Neutral:   [N] items ([X%])  ████░░░░░░\n  Negative:  [N] items ([X%])  ██░░░░░░░░\n  Critical:  [N] items ([X%])  █░░░░░░░░░\n```\n\n### 3B: Source Comparison\n\n| Source | Volume | Avg Sentiment | Top Theme |\n|--------|--------|---------------|-----------|\n| Support tickets | [N] | [Pos\u002FNeg score] | [Theme] |\n| NPS comments | [N] | [Score] | [Theme] |\n| G2 reviews | [N] | [Score] | [Theme] |\n| Slack | [N] | [Score] | [Theme] |\n| Calls | [N] | [Score] | [Theme] |\n\n**Insight:** Different sources often reveal different stories. Support tickets skew negative (problems). Reviews skew bipolar (love\u002Fhate). Calls reveal nuance. Note where themes appear across sources for highest confidence.\n\n### 3C: Segment Analysis\n\n| Customer Segment | Dominant Sentiment | Top Request | Key Pain |\n|-----------------|-------------------|-------------|----------|\n| [New customers] | [Sentiment] | [Request] | [Pain] |\n| [Power users] | [Sentiment] | [Request] | [Pain] |\n| [Enterprise] | [Sentiment] | [Request] | [Pain] |\n| [Churned] | [Sentiment] | [Request] | [Pain] |\n\n### 3D: Trend Detection\n\nCompare against prior period (if available):\n\n| Theme | Prior Period | This Period | Trend | Alert |\n|-------|-------------|-------------|-------|-------|\n| [Theme 1] | [N mentions] | [N mentions] | [↑X%] | [New\u002FGrowing\u002FStable\u002FDeclining] |\n| [Theme 2] | ... | ... | ... | ... |\n\n**New themes this period:** [Themes that weren't present before]\n**Resolved themes:** [Themes that decreased significantly — things you fixed]\n\n## Phase 4: Recommendations\n\n### For Product Team\n\n| Priority | Theme | Recommendation | Evidence Strength |\n|----------|-------|---------------|-------------------|\n| P0 | [Theme] | [Specific action] | [N mentions, M sources, includes churn signals] |\n| P1 | [Theme] | [Action] | [Evidence] |\n| P2 | [Theme] | [Action] | [Evidence] |\n\n### For CS\u002FSupport Team\n\n| Action | Theme | Expected Impact |\n|--------|-------|----------------|\n| [Create help article for X] | [Theme] | Deflect ~[N] tickets\u002Fmonth |\n| [Add onboarding step for Y] | [Theme] | Reduce confusion for new users |\n| [Proactive outreach to segment Z] | [Theme] | Prevent churn in at-risk segment |\n\n### For Marketing Team\n\n| Action | Theme | Opportunity |\n|--------|-------|------------|\n| [Use this proof point in messaging] | [Positive theme] | \"[Customer quote ready for marketing]\" |\n| [Address this objection on website] | [Negative theme] | Counter common concern pre-sale |\n| [Build case study around X] | [Positive theme] | [N] customers mentioned this win |\n\n## Phase 5: Output Format\n\n```markdown\n# Voice of Customer Report — [Period]\nSources analyzed: [list]\nTotal feedback items: [N]\nDate range: [start] — [end]\n\n---\n\n## Executive Summary\n\n[3-5 sentences: What are customers saying? What's the overall sentiment?\nWhat's the single most important thing to act on?]\n\n---\n\n## Sentiment Overview\n\nPositive: [X%] | Neutral: [X%] | Negative: [X%] | Critical: [X%]\n\nNet Sentiment Score: [calculated — % positive minus % negative]\nvs Prior Period: [+\u002F- X points]\n\n---\n\n## Top Themes (Ranked by Impact)\n\n### 1. [Theme Name] — [Sentiment] — [N mentions]\n**Summary:** [2-3 sentences]\n**Key quotes:**\n> \"[Quote]\" — [Source]\n> \"[Quote]\" — [Source]\n**Recommended action:** [What to do]\n**Owner:** [Product \u002F CS \u002F Marketing]\n\n### 2. [Theme Name] — ...\n\n### 3. [Theme Name] — ...\n\n[Continue for top 5-8 themes]\n\n---\n\n## What Customers Love (Preserve These)\n\n| Strength | Evidence | Marketing Opportunity |\n|----------|---------|----------------------|\n| [Feature\u002Fexperience] | \"[Quote]\" — [N mentions] | [How to use in messaging] |\n\n---\n\n## What Customers Want (Feature Requests)\n\n| Request | Frequency | Segments | Product Priority |\n|---------|-----------|----------|-----------------|\n| [Feature] | [N mentions] | [Who wants it] | [P0\u002FP1\u002FP2] |\n\n---\n\n## What Causes Pain (Fix These)\n\n| Pain Point | Severity | Churn Risk | Recommended Fix |\n|-----------|----------|------------|----------------|\n| [Issue] | [High\u002FMed\u002FLow] | [Yes\u002FNo] | [Action] |\n\n---\n\n## Trends vs Prior Period\n\n[What's getting better, what's getting worse, what's new]\n\n---\n\n## Team-Specific Action Items\n\n### Product Team\n1. [Action] — [Evidence]\n\n### CS Team\n1. [Action] — [Evidence]\n\n### Marketing Team\n1. [Action] — [Evidence]\n\n---\n\n## Appendix: All Themes Detail\n\n[Full theme cards with all quotes and analysis]\n```\n\nSave to `clients\u002F\u003Cclient-name>\u002Fcustomer-success\u002Fvoc\u002Fvoc-report-[YYYY-MM-DD].md`.\n\n## Scheduling\n\nRun monthly or quarterly:\n\n```bash\n0 8 1 *\u002F3 * python3 run_skill.py voice-of-customer-synthesizer --client \u003Cclient-name>\n```\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| Review scraping (via review-scraper) | ~$0.50-1.00 |\n| Web search (social mentions) | Free |\n| All analysis and synthesis | Free (LLM reasoning) |\n| **Total** | **Free — $1** |\n\n## Tools Required\n\n- **Optional:** `review-scraper` for G2\u002FCapterra\u002FTrustpilot reviews\n- **Optional:** `twitter-scraper` for social mentions\n- **Optional:** `reddit-scraper` for community feedback\n- All analysis is pure LLM reasoning on provided data\n\n## Trigger Phrases\n\n- \"What are customers saying?\"\n- \"Build a VoC report\"\n- \"Synthesize our customer feedback\"\n- \"Run voice of customer analysis\"\n- \"Customer feedback summary for [period]\"\n",{"name":5,"tags":53,"description":54},[45],"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-scraper for public review data.\n","master","c73386b305dec167db7654e0e6feacb462d4f9dc","assets\u002Fskills\u002Fgtm\u002Fgtm-voice-of-customer-synthesizer",1,"https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code","https:\u002F\u002Fgithub.com\u002FAntifragileTech","2026-10-02 14:22:14",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-voice-of-customer-synthesizer",{"tool":68,"command":69},"bunx","bunx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-voice-of-customer-synthesizer",{"tool":71,"command":72},"pnpm","pnpm dlx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-voice-of-customer-synthesizer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fgtm\u002Fgtm-voice-of-customer-synthesizer\u002FSKILL.md",true,[76],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"skill.meta.json","REFERENCE","参考文档",282,"https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fgtm\u002Fgtm-voice-of-customer-synthesizer\u002Fskill.meta.json",[83,105,120,135,147,160],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":98,"publishedAt":48},"01M6DT9Q6VYA615SCXR757TZZ7","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fwriting-skills\u002FSKILL.md",[89,92,95],{"categoryNo":90,"categoryName":91,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":96,"categoryName":97,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000036","内容创作者",[99,100,101,102,103,104],"writing skills","writing","use","editing","work","deployment",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":113,"publishedAt":48},"01M6DT9GFHDNY90P41WGAE1TV0","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fwriting-plans\u002FSKILL.md",[111,112],{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":96,"categoryName":97,"taxonomyType":25,"taxonomyTypeDesc":26},[114,115,100,116,117,101,118,119],"plans","writing plans","requirements","task","code","spec",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":128,"publishedAt":48},"01M6DT9C6PFGD86WS3HY7YE3XG","docs:write-concisely","Apply writing rules to any documentation that humans will read. Makes your writing clearer, stronger, and more professional.","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fwrite-concisely\u002FSKILL.md",[126,127],{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":96,"categoryName":97,"taxonomyType":25,"taxonomyTypeDesc":26},[129,130,131,132,133,134,100],"docs:write concisely","write","docs","read","Rules","documentation",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":48},"01M6DT98AWQ27P0N1CG97WAG66","kaizen:why","Iterative Five Whys root cause analysis drilling from symptoms to fundamentals","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fwhy\u002FSKILL.md",[],[137,142,143,144,145,38,146],"why","kaizen","issue","five whys","root cause analysis",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":153,"publishedAt":48},"01M6DT93EH25NAX19R3WGE1P53","verification-loop","A comprehensive verification system for Claude Code sessions.","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fverification-loop\u002FSKILL.md",[],[154,155,156,157,118,158,159],"verification loop","verification","loop","sessions","claude code sessions","system",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":48},"01M6DT90GW7Z7VCN98P9DVYSNC","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fthinking\u002Fverification-before-completion\u002FSKILL.md",[],[167,155,168,103,101,169,170,171],"verification before completion","commands","evidence","claim","complete"]