[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DS232VB8EBZ3FAX4S42Q6A":3,"skill-same:01M6DS232VB8EBZ3FAX4S42Q6A":72},{"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":17,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M6DS232VB8EBZ3FAX4S42Q6A","gtm-churn-risk-detector",null,"Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed\u002FSeries A teams where the founder or a single CSM manages all accounts manually.","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-churn-risk-detector\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"risk","gtm","gtm churn risk detector","slack","research","support","teams","severity","indicators","CSM","save","patterns","scan","seed","series","founder","2026-11-10 10:12:30",false,"---\nname: gtm-churn-risk-detector\ndescription: >\n  Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts\n  showing early churn indicators. Produces a weekly risk scorecard with severity tiers,\n  root cause hypotheses, and suggested save plays per account. Designed for seed\u002FSeries A\n  teams where the founder or a single CSM manages all accounts manually.\ntags: [research]\n---\n\n# Churn Risk Detector\n\nSurface accounts at risk of churning before it's too late. Aggregates signals from support, communication, and usage patterns into a scored risk report with specific save actions.\n\n**Built for:** Early-stage teams with no CS platform (no Gainsight, no ChurnZero). You have a spreadsheet of customers, a Slack channel, and a support inbox. This skill turns those raw signals into an actionable churn risk list.\n\n## When to Use\n\n- \"Which customers are at risk of churning?\"\n- \"Run the weekly churn risk scan\"\n- \"Flag accounts I should worry about\"\n- \"Who haven't we heard from in a while?\"\n- \"Produce a customer health report\"\n\n## Phase 0: Intake\n\n### Account Data\n1. **Customer list** — CSV or sheet with: company name, primary contact email, contract value (MRR\u002FARR), contract start date, renewal date (if known)\n2. **Product\u002Fservice type** — What are they paying for? (Helps calibrate expected engagement)\n\n### Signal Sources (provide what you have)\n3. **Support tickets** — Export from Intercom, Zendesk, or email (CSV with: customer, date, subject, status, resolution time)\n4. **Slack channel history** — Customer Slack channel or shared channel messages\n5. **NPS\u002FCSAT scores** — Recent survey results with scores and comments\n6. **Usage data** — Any metrics you track: logins, API calls, features used, active users (CSV export)\n7. **Email\u002Fcommunication log** — Last touchpoints per account (dates + context)\n8. **Billing data** — Payment failures, downgrades, discount requests\n\n### Calibration\n9. **What does \"healthy\" look like?** — Describe a healthy customer (e.g., \"logs in daily, uses 3+ features, responds to emails within 24h\")\n10. **Known churn reasons** — Why have customers churned in the past? (helps weight signals)\n\n## Phase 1: Signal Extraction\n\n### 1A: Support Signal Analysis\n\nFrom support ticket data, calculate per account:\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Ticket volume spike** | >2x their average in last 30 days | High |\n| **Unresolved tickets** | Open tickets older than 7 days | High |\n| **Escalation language** | Keywords: \"cancel\", \"frustrated\", \"alternative\", \"not working\", \"disappointed\" | Critical |\n| **Response time degradation** | Your avg response time to this customer trending up | Medium |\n| **Repeat issues** | Same problem reported 2+ times | High |\n\n### 1B: Communication Signal Analysis\n\nFrom Slack\u002Femail history:\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Gone silent** | No messages in 30+ days (was previously active) | High |\n| **Decreasing frequency** | Message frequency dropped >50% vs prior 90 days | Medium |\n| **Negative sentiment shift** | Tone changed from positive to neutral\u002Fnegative | Medium |\n| **Champion disengagement** | Primary contact stopped responding | Critical |\n| **New stakeholder questions** | New person asking basic \"what does this do?\" questions | Medium (potential reorg) |\n\n### 1C: Usage Signal Analysis (if data available)\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Login drop** | Active users down >30% vs prior month | High |\n| **Feature abandonment** | Stopped using a key feature they previously used regularly | High |\n| **Shallow usage** | Only using 1 feature when they're paying for many | Medium |\n| **No growth** | Same number of seats\u002Fusers for 6+ months | Low |\n| **Export spike** | Sudden increase in data exports | Critical (may be migrating) |\n\n### 1D: Commercial Signal Analysis\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Discount request** | Asked for pricing reduction | High |\n| **Downgrade inquiry** | Asked about lower tier | Critical |\n| **Payment failure** | Failed payment not resolved in 7+ days | High |\n| **Contract approaching renewal** | \u003C60 days to renewal with no renewal discussion | Medium |\n| **Competitor mention** | Mentioned a competitor in any channel | High |\n\n## Phase 2: Risk Scoring\n\n### Scoring Model\n\nEach account gets a composite risk score (0-100):\n\n```\nRisk Score = Σ (signal_weight × signal_present)\n\nWeights:\n  Critical signal = 25 points each\n  High signal     = 15 points each\n  Medium signal   = 8 points each\n  Low signal      = 3 points each\n\nScore cap: 100\n```\n\n### Risk Tiers\n\n| Tier | Score | Label | Action Urgency |\n|------|-------|-------|---------------|\n| **Red** | 70-100 | Critical risk — likely to churn | This week |\n| **Orange** | 40-69 | Elevated risk — needs attention | Within 2 weeks |\n| **Yellow** | 20-39 | Early warning — monitor closely | Within 30 days |\n| **Green** | 0-19 | Healthy — no action needed | Routine check-in |\n\n## Phase 3: Save Play Generation\n\nFor each Red and Orange account, generate a specific save play:\n\n### Save Play Template\n\n```\nACCOUNT: [Company Name]\nRISK TIER: [Red\u002FOrange]\nRISK SCORE: [X\u002F100]\nMRR\u002FARR: $[X]\n\nSIGNALS DETECTED:\n- [Signal 1] — [Evidence: specific data point]\n- [Signal 2] — [Evidence]\n- [Signal 3] — [Evidence]\n\nROOT CAUSE HYPOTHESIS:\n[1-2 sentences: What do you think is actually going wrong?\n E.g., \"Champion left the company and new stakeholder hasn't been onboarded\"\n or \"They hit a technical limitation with [feature] that's blocking their primary use case\"]\n\nRECOMMENDED SAVE PLAY:\n1. [Immediate action — e.g., \"Schedule a call with [contact] this week\"]\n2. [Follow-up — e.g., \"Send a personalized Loom showing how to solve [specific issue]\"]\n3. [Structural fix — e.g., \"Assign a dedicated onboarding session for new stakeholder\"]\n\nTALK TRACK:\n\"[2-3 sentences the CSM\u002Ffounder can use to open the conversation naturally,\n without saying 'we noticed you might be churning']\"\n\nESCALATION TRIGGER:\nIf [specific condition] by [date], escalate to [founder\u002FCEO call].\n```\n\n## Phase 4: Output Format\n\n```markdown\n# Churn Risk Report — Week of [DATE]\nTotal accounts scanned: [N]\nData sources: [list what was available]\n\n---\n\n## Risk Summary\n\n| Tier | Count | Total MRR at Risk |\n|------|-------|-------------------|\n| ? Red (Critical) | [N] | $[X] |\n| ? Orange (Elevated) | [N] | $[X] |\n| ? Yellow (Early Warning) | [N] | $[X] |\n| ? Green (Healthy) | [N] | $[X] |\n\n**Total MRR at risk (Red + Orange):** $[X] ([Y]% of total MRR)\n\n---\n\n## ? Critical Risk Accounts\n\n### [Company Name 1] — Score: [X]\u002F100 | MRR: $[X]\n**Signals:** [bullet list]\n**Root cause:** [hypothesis]\n**Save play:** [specific actions]\n**Owner:** [who should act]\n**Deadline:** [date]\n\n### [Company Name 2] — ...\n\n---\n\n## ? Elevated Risk Accounts\n\n### [Company Name] — Score: [X]\u002F100 | MRR: $[X]\n**Signals:** [bullet list]\n**Recommended action:** [1-2 sentences]\n\n---\n\n## ? Early Warning Accounts\n\n| Account | Score | Key Signal | Suggested Action |\n|---------|-------|------------|-----------------|\n| [Name] | [X] | [Signal] | [Action] |\n| [Name] | [X] | [Signal] | [Action] |\n\n---\n\n## Trends vs Last Week\n\n- Accounts moved Red → Green: [list — wins!]\n- Accounts moved Green → Yellow\u002FOrange: [list — new risks]\n- Accounts churned since last report: [list]\n\n---\n\n## Signal Distribution\n\n| Signal Type | Accounts Affected |\n|------------|-------------------|\n| Support ticket spike | [N] |\n| Gone silent | [N] |\n| Usage decline | [N] |\n| Competitor mention | [N] |\n| Payment issue | [N] |\n| Champion disengagement | [N] |\n\n---\n\n## Recommended Focus This Week\n\n1. **[Account]** — [Why + what to do]\n2. **[Account]** — [Why + what to do]\n3. **[Account]** — [Why + what to do]\n```\n\nSave to `clients\u002F\u003Cclient-name>\u002Fcustomer-success\u002Fchurn-risk\u002Frisk-report-[YYYY-MM-DD].md`.\n\n## Scheduling\n\nRun weekly:\n\n```bash\n0 8 * * 1 python3 run_skill.py churn-risk-detector --client \u003Cclient-name>\n```\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| All signal analysis | Free (LLM reasoning) |\n| Slack\u002Femail parsing | Free |\n| **Total** | **Free** |\n\n## Tools Required\n\n- Input data from CSV\u002Fsheets (support tickets, usage, NPS)\n- **Optional:** Slack channel reading for communication signals\n- No external API costs — pure analysis\n\n## Trigger Phrases\n\n- \"Which customers are at risk?\"\n- \"Run the churn risk scan\"\n- \"Weekly customer health report\"\n- \"Flag at-risk accounts\"\n","# Churn Risk Detector\n\nSurface accounts at risk of churning before it's too late. Aggregates signals from support, communication, and usage patterns into a scored risk report with specific save actions.\n\n**Built for:** Early-stage teams with no CS platform (no Gainsight, no ChurnZero). You have a spreadsheet of customers, a Slack channel, and a support inbox. This skill turns those raw signals into an actionable churn risk list.\n\n## When to Use\n\n- \"Which customers are at risk of churning?\"\n- \"Run the weekly churn risk scan\"\n- \"Flag accounts I should worry about\"\n- \"Who haven't we heard from in a while?\"\n- \"Produce a customer health report\"\n\n## Phase 0: Intake\n\n### Account Data\n1. **Customer list** — CSV or sheet with: company name, primary contact email, contract value (MRR\u002FARR), contract start date, renewal date (if known)\n2. **Product\u002Fservice type** — What are they paying for? (Helps calibrate expected engagement)\n\n### Signal Sources (provide what you have)\n3. **Support tickets** — Export from Intercom, Zendesk, or email (CSV with: customer, date, subject, status, resolution time)\n4. **Slack channel history** — Customer Slack channel or shared channel messages\n5. **NPS\u002FCSAT scores** — Recent survey results with scores and comments\n6. **Usage data** — Any metrics you track: logins, API calls, features used, active users (CSV export)\n7. **Email\u002Fcommunication log** — Last touchpoints per account (dates + context)\n8. **Billing data** — Payment failures, downgrades, discount requests\n\n### Calibration\n9. **What does \"healthy\" look like?** — Describe a healthy customer (e.g., \"logs in daily, uses 3+ features, responds to emails within 24h\")\n10. **Known churn reasons** — Why have customers churned in the past? (helps weight signals)\n\n## Phase 1: Signal Extraction\n\n### 1A: Support Signal Analysis\n\nFrom support ticket data, calculate per account:\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Ticket volume spike** | >2x their average in last 30 days | High |\n| **Unresolved tickets** | Open tickets older than 7 days | High |\n| **Escalation language** | Keywords: \"cancel\", \"frustrated\", \"alternative\", \"not working\", \"disappointed\" | Critical |\n| **Response time degradation** | Your avg response time to this customer trending up | Medium |\n| **Repeat issues** | Same problem reported 2+ times | High |\n\n### 1B: Communication Signal Analysis\n\nFrom Slack\u002Femail history:\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Gone silent** | No messages in 30+ days (was previously active) | High |\n| **Decreasing frequency** | Message frequency dropped >50% vs prior 90 days | Medium |\n| **Negative sentiment shift** | Tone changed from positive to neutral\u002Fnegative | Medium |\n| **Champion disengagement** | Primary contact stopped responding | Critical |\n| **New stakeholder questions** | New person asking basic \"what does this do?\" questions | Medium (potential reorg) |\n\n### 1C: Usage Signal Analysis (if data available)\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Login drop** | Active users down >30% vs prior month | High |\n| **Feature abandonment** | Stopped using a key feature they previously used regularly | High |\n| **Shallow usage** | Only using 1 feature when they're paying for many | Medium |\n| **No growth** | Same number of seats\u002Fusers for 6+ months | Low |\n| **Export spike** | Sudden increase in data exports | Critical (may be migrating) |\n\n### 1D: Commercial Signal Analysis\n\n| Signal | Calculation | Risk Weight |\n|--------|-------------|-------------|\n| **Discount request** | Asked for pricing reduction | High |\n| **Downgrade inquiry** | Asked about lower tier | Critical |\n| **Payment failure** | Failed payment not resolved in 7+ days | High |\n| **Contract approaching renewal** | \u003C60 days to renewal with no renewal discussion | Medium |\n| **Competitor mention** | Mentioned a competitor in any channel | High |\n\n## Phase 2: Risk Scoring\n\n### Scoring Model\n\nEach account gets a composite risk score (0-100):\n\n```\nRisk Score = Σ (signal_weight × signal_present)\n\nWeights:\n  Critical signal = 25 points each\n  High signal     = 15 points each\n  Medium signal   = 8 points each\n  Low signal      = 3 points each\n\nScore cap: 100\n```\n\n### Risk Tiers\n\n| Tier | Score | Label | Action Urgency |\n|------|-------|-------|---------------|\n| **Red** | 70-100 | Critical risk — likely to churn | This week |\n| **Orange** | 40-69 | Elevated risk — needs attention | Within 2 weeks |\n| **Yellow** | 20-39 | Early warning — monitor closely | Within 30 days |\n| **Green** | 0-19 | Healthy — no action needed | Routine check-in |\n\n## Phase 3: Save Play Generation\n\nFor each Red and Orange account, generate a specific save play:\n\n### Save Play Template\n\n```\nACCOUNT: [Company Name]\nRISK TIER: [Red\u002FOrange]\nRISK SCORE: [X\u002F100]\nMRR\u002FARR: $[X]\n\nSIGNALS DETECTED:\n- [Signal 1] — [Evidence: specific data point]\n- [Signal 2] — [Evidence]\n- [Signal 3] — [Evidence]\n\nROOT CAUSE HYPOTHESIS:\n[1-2 sentences: What do you think is actually going wrong?\n E.g., \"Champion left the company and new stakeholder hasn't been onboarded\"\n or \"They hit a technical limitation with [feature] that's blocking their primary use case\"]\n\nRECOMMENDED SAVE PLAY:\n1. [Immediate action — e.g., \"Schedule a call with [contact] this week\"]\n2. [Follow-up — e.g., \"Send a personalized Loom showing how to solve [specific issue]\"]\n3. [Structural fix — e.g., \"Assign a dedicated onboarding session for new stakeholder\"]\n\nTALK TRACK:\n\"[2-3 sentences the CSM\u002Ffounder can use to open the conversation naturally,\n without saying 'we noticed you might be churning']\"\n\nESCALATION TRIGGER:\nIf [specific condition] by [date], escalate to [founder\u002FCEO call].\n```\n\n## Phase 4: Output Format\n\n```markdown\n# Churn Risk Report — Week of [DATE]\nTotal accounts scanned: [N]\nData sources: [list what was available]\n\n---\n\n## Risk Summary\n\n| Tier | Count | Total MRR at Risk |\n|------|-------|-------------------|\n| ? Red (Critical) | [N] | $[X] |\n| ? Orange (Elevated) | [N] | $[X] |\n| ? Yellow (Early Warning) | [N] | $[X] |\n| ? Green (Healthy) | [N] | $[X] |\n\n**Total MRR at risk (Red + Orange):** $[X] ([Y]% of total MRR)\n\n---\n\n## ? Critical Risk Accounts\n\n### [Company Name 1] — Score: [X]\u002F100 | MRR: $[X]\n**Signals:** [bullet list]\n**Root cause:** [hypothesis]\n**Save play:** [specific actions]\n**Owner:** [who should act]\n**Deadline:** [date]\n\n### [Company Name 2] — ...\n\n---\n\n## ? Elevated Risk Accounts\n\n### [Company Name] — Score: [X]\u002F100 | MRR: $[X]\n**Signals:** [bullet list]\n**Recommended action:** [1-2 sentences]\n\n---\n\n## ? Early Warning Accounts\n\n| Account | Score | Key Signal | Suggested Action |\n|---------|-------|------------|-----------------|\n| [Name] | [X] | [Signal] | [Action] |\n| [Name] | [X] | [Signal] | [Action] |\n\n---\n\n## Trends vs Last Week\n\n- Accounts moved Red → Green: [list — wins!]\n- Accounts moved Green → Yellow\u002FOrange: [list — new risks]\n- Accounts churned since last report: [list]\n\n---\n\n## Signal Distribution\n\n| Signal Type | Accounts Affected |\n|------------|-------------------|\n| Support ticket spike | [N] |\n| Gone silent | [N] |\n| Usage decline | [N] |\n| Competitor mention | [N] |\n| Payment issue | [N] |\n| Champion disengagement | [N] |\n\n---\n\n## Recommended Focus This Week\n\n1. **[Account]** — [Why + what to do]\n2. **[Account]** — [Why + what to do]\n3. **[Account]** — [Why + what to do]\n```\n\nSave to `clients\u002F\u003Cclient-name>\u002Fcustomer-success\u002Fchurn-risk\u002Frisk-report-[YYYY-MM-DD].md`.\n\n## Scheduling\n\nRun weekly:\n\n```bash\n0 8 * * 1 python3 run_skill.py churn-risk-detector --client \u003Cclient-name>\n```\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| All signal analysis | Free (LLM reasoning) |\n| Slack\u002Femail parsing | Free |\n| **Total** | **Free** |\n\n## Tools Required\n\n- Input data from CSV\u002Fsheets (support tickets, usage, NPS)\n- **Optional:** Slack channel reading for communication signals\n- No external API costs — pure analysis\n\n## Trigger Phrases\n\n- \"Which customers are at risk?\"\n- \"Run the churn risk scan\"\n- \"Weekly customer health report\"\n- \"Flag at-risk accounts\"\n",{"name":5,"tags":39,"description":40},[22],"Scan support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed\u002FSeries A teams where the founder or a single CSM manages all accounts manually.\n","master","c73386b305dec167db7654e0e6feacb462d4f9dc","assets\u002Fskills\u002Fgtm\u002Fgtm-churn-risk-detector",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":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-churn-risk-detector",{"tool":54,"command":55},"bunx","bunx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-churn-risk-detector",{"tool":57,"command":58},"pnpm","pnpm dlx skills add AntifragileTech\u002Fantifragile-claude-code --skill gtm-churn-risk-detector","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fgtm\u002Fgtm-churn-risk-detector\u002FSKILL.md",true,[62,68],{"path":63,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":67},"index.json","REFERENCE","参考文档",528,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Flinux-fs-vfs-hook-detect\u002Findex.json",{"path":69,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":70,"sourceHtmlUrl":71},"skill.meta.json",262,"https:\u002F\u002Fgithub.com\u002FAntifragileTech\u002Fantifragile-claude-code\u002Fblob\u002Fmaster\u002Fassets\u002Fskills\u002Fgtm\u002Fgtm-churn-risk-detector\u002Fskill.meta.json",[73,99,114,129,142,155],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":92,"publishedAt":34},"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",[79,84,87],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[93,94,95,96,97,98],"writing skills","writing","use","editing","work","deployment",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":107,"publishedAt":34},"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",[105,106],{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[108,109,94,110,111,95,112,113],"plans","writing plans","requirements","task","code","spec",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":122,"publishedAt":34},"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",[120,121],{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[123,124,125,126,127,128,94],"docs:write concisely","write","docs","read","Rules","documentation",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":34},"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",[],[131,136,137,138,139,140,141],"why","kaizen","issue","five whys","analysis","root cause analysis",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":34},"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",[],[149,150,151,152,112,153,154],"verification loop","verification","loop","sessions","claude code sessions","system",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":161,"publishedAt":34},"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",[],[162,150,163,97,95,164,165,166],"verification before completion","commands","evidence","claim","complete"]