[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7501P93WPSZPK21DBBACRMM":3,"skill-same:01M7501P93WPSZPK21DBBACRMM":49},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":31,"ownerHtmlUrl":32,"ownerBlogUrl":31,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M7501P93WPSZPK21DBBACRMM","churn-reduction",null,"Diagnoses SaaS churn root causes through cohort analysis and a seven-category taxonomy, then builds segmented intervention playbooks ranked by frequency, revenue at stake, and addressability - including save-offer economics. Use when someone asks \"why is our churn so high\", \"build a churn reduction plan\", \"should we offer discounts to cancelling customers\", or when NRR\u002FGRR is slipping and the team needs a diagnosis before prescriptions. Do NOT use for gym or fitness-studio member retention - use retention-and-churn-killer instead. Do NOT use for re-acquiring customers who already left - use win-back-campaign instead. Do NOT use for growing existing accounts - use expansion-revenue instead.","SkillMedev\u002Fskills","SkillMedev","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F298078254?v=4","01M74ZSGTQ918CB0VXW4EG3RMX","SkillMe","01M74ZSGVBT497NA95CBT8JTY4",25,"MIT","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fchurn-reduction\u002FSKILL.md",[],[],"2026-11-11 17:59:50",false,"---\nname: churn-reduction\ndescription: Diagnoses SaaS churn root causes through cohort analysis and a seven-category taxonomy, then builds segmented intervention playbooks ranked by frequency, revenue at stake, and addressability - including save-offer economics. Use when someone asks \"why is our churn so high\", \"build a churn reduction plan\", \"should we offer discounts to cancelling customers\", or when NRR\u002FGRR is slipping and the team needs a diagnosis before prescriptions. Do NOT use for gym or fitness-studio member retention - use retention-and-churn-killer instead. Do NOT use for re-acquiring customers who already left - use win-back-campaign instead. Do NOT use for growing existing accounts - use expansion-revenue instead.\nmetadata:\n  title: \"Churn Reduction\"\n---\n\n# Churn Reduction\n\nChurn is the silent killer of SaaS - 5% monthly churn caps the business no matter how fast acquisition runs, because it compounds into LTV, NRR, and valuation. The costly mistake this skill prevents is prescribing before diagnosing: launching a save-offer program or a CS hiring spree against a blended churn number, when the real leak is bad-fit acquisition or failed onboarding that no discount can fix.\n\n## Operating procedure\n\nDiagnosis strictly precedes intervention. Steps 1-3 establish what is actually leaking; only then do playbooks get built.\n\n### Step 1: Gather inputs\n\n1. Monthly logo churn and revenue churn for the trailing 12 months. Track both - a small customer leaving and a whale leaving are different problems.\n2. GRR and NRR. Gross revenue retention (churn + contraction only) isolates the leak; net revenue retention (includes expansion) can hide it. An NRR of 105% with GRR of 82% is a leaky bucket propped up by a few expanding whales.\n3. Customer list with segment, plan, signup month, acquisition channel, ACV.\n4. Cancellation reasons, exit interviews, and support history where they exist. Default: sparse - plan interviews in Step 3.\n5. Payment-failure data (involuntary churn is often invisible in \"reasons\").\n\nLabel any estimated figure as an estimate.\n\n### Step 2: Cohort before concluding\n\nBlended churn averages away the insight. Cut retention three ways minimum: by signup month (is the product getting stickier?), by segment (SMB vs mid-market behave differently), and by acquisition channel (a cheap channel with 2x churn is not cheap). Benchmarks to read against: SMB-serving SaaS commonly runs 3-7% monthly logo churn; mid-market\u002Fenterprise should be at or under 1-2% monthly (10-15% annually); GRR of 90%+ is healthy for SMB, 95%+ for enterprise. A cohort more than double its peer cohorts is where the diagnosis starts.\n\n### Step 3: Categorize every churn - the seven-category taxonomy\n\nInterview churned customers and read cancellation reasons; the categories shift the roadmap, not just CS scripts. Assign every churned account to exactly one primary category:\n\n1. Bad fit - never the ICP. Fix: tighten qualification, not retention. Cheapest churn to eliminate (stop acquiring them).\n2. Poor onboarding - never reached first value. Fix: time-to-value and activation. Often the single biggest lever.\n3. Low adoption - bought it, never embedded it in workflow.\n4. Missing value - used it, did not get the outcome. Fix: product or success motion.\n5. Champion left - the sponsor changed jobs. Fix: multi-thread accounts.\n6. Price\u002Fbudget - economic, often macro-driven.\n7. Competitor - lost head-to-head. Fix: product or positioning.\n\n### Diagnosis decision tree\n\n```\nChurned account\n├─ Did payment fail (card decline, expiry)?\n│    → INVOLUNTARY: dunning\u002Fcard-update\u002Fretry fix. Check share first -\n│      often 20-40% of all churn and the easiest to recover.\n├─ Did they ever hit the activation milestone?\n│    NO → Did they match the ICP at signup?\n│           NO  → (1) BAD FIT - fix qualification upstream\n│           YES → (2) POOR ONBOARDING - fix time-to-value\n│    YES → Was usage healthy in the last 90 days?\n│           NO → Did the champion\u002Fsponsor leave or disengage?\n│                  YES → (5) CHAMPION LEFT - multi-thread\n│                  NO  → (3) LOW ADOPTION - never embedded in workflow\n│           YES → Did they cite outcome shortfall?\n│                  YES → (4) MISSING VALUE - product\u002Fsuccess motion\n│                  NO  → Did they move to a rival?\n│                         YES → (7) COMPETITOR - product\u002Fpositioning\n│                         NO  → (6) PRICE\u002FBUDGET - economic\n```\n\n### Step 4: Build the early-warning system\n\nChurn is decided months before it happens. Score accounts on leading indicators and trigger intervention before renewal, not at it:\n\n- Declining usage or login frequency\n- Drop in active seats\n- Champion disengagement (no QBR attendance, slow email replies)\n- Support escalations or unresolved tickets\n- Failure to hit the activation milestone in onboarding\n\nAn account flagging two or more indicators enters the at-risk queue.\n\n### Step 5: Build segmented intervention playbooks\n\n- Onboarding churn: structured first-30-day activation program with a clear \"aha\" milestone.\n- Adoption churn: usage nudges, training, executive business reviews to re-anchor value (pair with customer-success-qbr).\n- At-risk accounts: a save play - executive outreach, success plan reset, targeted offer if the driver is economic.\n- Involuntary churn: dunning, card-update flows, retry logic. Often 20-40% of churn and the easiest to recover - fix this before anything heroic.\n\nSave-offer economics - run the math before offering discounts. A save offer pays only when: (expected additional lifetime margin retained × save rate) > (discount cost + CS time). Rules of thumb: cap discounts at 20-30% for a maximum of 3 months; a healthy save play converts 15-30% of at-risk accounts; a \"saved\" account that churns within 90 days anyway was a deferral, not a save - track 90-day post-save retention separately. Never train customers that threatening to cancel earns a discount: gate offers to genuinely economic-category churn, never volunteer them in the cancel flow by default.\n\n### Step 6: Prioritize the fix\n\nRank causes by frequency × revenue at stake × addressability. Fixing onboarding usually beats heroic save plays because it prevents churn at scale rather than rescuing one account at a time. Bad-fit churn is fixed in sales and marketing, not CS - route it upstream.\n\n## Deliverable\n\nProduce a churn analysis containing: retention by cohort (signup month, segment, channel), GRR\u002FNRR with the gap explained, a root-cause breakdown across the seven categories with involuntary churn quantified separately, a leading-indicator early-warning model with trigger thresholds, save-offer economics for the economic segment, and intervention playbooks ranked by frequency × revenue × addressability.\n\n## Metrics to track ongoing\n\nGross and net revenue retention by cohort; churn by root-cause category; activation rate and time-to-value; save rate on at-risk interventions plus 90-day post-save retention.\n\n## Do NOT\n\n- Do not prescribe from blended churn - averages hide the cohort where the problem lives.\n- Do not read NRR alone; expansion from a few accounts can mask a gross-retention leak that GRR exposes.\n- Do not treat bad-fit churn as a CS problem - the fix is qualification, and every dollar spent \"saving\" a bad-fit account is wasted twice.\n- Do not offer discounts reflexively in the cancel flow; it trains the base to threaten cancellation and erodes price integrity.\n- Do not skip the involuntary-churn check - recovering failed payments is the cheapest 20-40% of the problem.\n- Do not count a save as a save until the account clears 90 days post-intervention.\n\n## Quality bar\n\nShip only when: every churned account in the analysis window has one primary category; cohort cuts exist on all three axes; the early-warning model names concrete trigger thresholds; every recommended save offer has its economics shown; and the top-priority fix is justified by the frequency × revenue × addressability ranking, not by which intervention is most familiar.\n\n## Escalation\n\nGym and fitness-studio retention has its own economics and playbook - use retention-and-churn-killer. Customers already churned belong to win-back-campaign; account growth belongs to expansion-revenue; recurring QBR motion belongs to customer-success-qbr.\n","# Churn Reduction\n\nChurn is the silent killer of SaaS - 5% monthly churn caps the business no matter how fast acquisition runs, because it compounds into LTV, NRR, and valuation. The costly mistake this skill prevents is prescribing before diagnosing: launching a save-offer program or a CS hiring spree against a blended churn number, when the real leak is bad-fit acquisition or failed onboarding that no discount can fix.\n\n## Operating procedure\n\nDiagnosis strictly precedes intervention. Steps 1-3 establish what is actually leaking; only then do playbooks get built.\n\n### Step 1: Gather inputs\n\n1. Monthly logo churn and revenue churn for the trailing 12 months. Track both - a small customer leaving and a whale leaving are different problems.\n2. GRR and NRR. Gross revenue retention (churn + contraction only) isolates the leak; net revenue retention (includes expansion) can hide it. An NRR of 105% with GRR of 82% is a leaky bucket propped up by a few expanding whales.\n3. Customer list with segment, plan, signup month, acquisition channel, ACV.\n4. Cancellation reasons, exit interviews, and support history where they exist. Default: sparse - plan interviews in Step 3.\n5. Payment-failure data (involuntary churn is often invisible in \"reasons\").\n\nLabel any estimated figure as an estimate.\n\n### Step 2: Cohort before concluding\n\nBlended churn averages away the insight. Cut retention three ways minimum: by signup month (is the product getting stickier?), by segment (SMB vs mid-market behave differently), and by acquisition channel (a cheap channel with 2x churn is not cheap). Benchmarks to read against: SMB-serving SaaS commonly runs 3-7% monthly logo churn; mid-market\u002Fenterprise should be at or under 1-2% monthly (10-15% annually); GRR of 90%+ is healthy for SMB, 95%+ for enterprise. A cohort more than double its peer cohorts is where the diagnosis starts.\n\n### Step 3: Categorize every churn - the seven-category taxonomy\n\nInterview churned customers and read cancellation reasons; the categories shift the roadmap, not just CS scripts. Assign every churned account to exactly one primary category:\n\n1. Bad fit - never the ICP. Fix: tighten qualification, not retention. Cheapest churn to eliminate (stop acquiring them).\n2. Poor onboarding - never reached first value. Fix: time-to-value and activation. Often the single biggest lever.\n3. Low adoption - bought it, never embedded it in workflow.\n4. Missing value - used it, did not get the outcome. Fix: product or success motion.\n5. Champion left - the sponsor changed jobs. Fix: multi-thread accounts.\n6. Price\u002Fbudget - economic, often macro-driven.\n7. Competitor - lost head-to-head. Fix: product or positioning.\n\n### Diagnosis decision tree\n\n```\nChurned account\n├─ Did payment fail (card decline, expiry)?\n│    → INVOLUNTARY: dunning\u002Fcard-update\u002Fretry fix. Check share first -\n│      often 20-40% of all churn and the easiest to recover.\n├─ Did they ever hit the activation milestone?\n│    NO → Did they match the ICP at signup?\n│           NO  → (1) BAD FIT - fix qualification upstream\n│           YES → (2) POOR ONBOARDING - fix time-to-value\n│    YES → Was usage healthy in the last 90 days?\n│           NO → Did the champion\u002Fsponsor leave or disengage?\n│                  YES → (5) CHAMPION LEFT - multi-thread\n│                  NO  → (3) LOW ADOPTION - never embedded in workflow\n│           YES → Did they cite outcome shortfall?\n│                  YES → (4) MISSING VALUE - product\u002Fsuccess motion\n│                  NO  → Did they move to a rival?\n│                         YES → (7) COMPETITOR - product\u002Fpositioning\n│                         NO  → (6) PRICE\u002FBUDGET - economic\n```\n\n### Step 4: Build the early-warning system\n\nChurn is decided months before it happens. Score accounts on leading indicators and trigger intervention before renewal, not at it:\n\n- Declining usage or login frequency\n- Drop in active seats\n- Champion disengagement (no QBR attendance, slow email replies)\n- Support escalations or unresolved tickets\n- Failure to hit the activation milestone in onboarding\n\nAn account flagging two or more indicators enters the at-risk queue.\n\n### Step 5: Build segmented intervention playbooks\n\n- Onboarding churn: structured first-30-day activation program with a clear \"aha\" milestone.\n- Adoption churn: usage nudges, training, executive business reviews to re-anchor value (pair with customer-success-qbr).\n- At-risk accounts: a save play - executive outreach, success plan reset, targeted offer if the driver is economic.\n- Involuntary churn: dunning, card-update flows, retry logic. Often 20-40% of churn and the easiest to recover - fix this before anything heroic.\n\nSave-offer economics - run the math before offering discounts. A save offer pays only when: (expected additional lifetime margin retained × save rate) > (discount cost + CS time). Rules of thumb: cap discounts at 20-30% for a maximum of 3 months; a healthy save play converts 15-30% of at-risk accounts; a \"saved\" account that churns within 90 days anyway was a deferral, not a save - track 90-day post-save retention separately. Never train customers that threatening to cancel earns a discount: gate offers to genuinely economic-category churn, never volunteer them in the cancel flow by default.\n\n### Step 6: Prioritize the fix\n\nRank causes by frequency × revenue at stake × addressability. Fixing onboarding usually beats heroic save plays because it prevents churn at scale rather than rescuing one account at a time. Bad-fit churn is fixed in sales and marketing, not CS - route it upstream.\n\n## Deliverable\n\nProduce a churn analysis containing: retention by cohort (signup month, segment, channel), GRR\u002FNRR with the gap explained, a root-cause breakdown across the seven categories with involuntary churn quantified separately, a leading-indicator early-warning model with trigger thresholds, save-offer economics for the economic segment, and intervention playbooks ranked by frequency × revenue × addressability.\n\n## Metrics to track ongoing\n\nGross and net revenue retention by cohort; churn by root-cause category; activation rate and time-to-value; save rate on at-risk interventions plus 90-day post-save retention.\n\n## Do NOT\n\n- Do not prescribe from blended churn - averages hide the cohort where the problem lives.\n- Do not read NRR alone; expansion from a few accounts can mask a gross-retention leak that GRR exposes.\n- Do not treat bad-fit churn as a CS problem - the fix is qualification, and every dollar spent \"saving\" a bad-fit account is wasted twice.\n- Do not offer discounts reflexively in the cancel flow; it trains the base to threaten cancellation and erodes price integrity.\n- Do not skip the involuntary-churn check - recovering failed payments is the cheapest 20-40% of the problem.\n- Do not count a save as a save until the account clears 90 days post-intervention.\n\n## Quality bar\n\nShip only when: every churned account in the analysis window has one primary category; cohort cuts exist on all three axes; the early-warning model names concrete trigger thresholds; every recommended save offer has its economics shown; and the top-priority fix is justified by the frequency × revenue × addressability ranking, not by which intervention is most familiar.\n\n## Escalation\n\nGym and fitness-studio retention has its own economics and playbook - use retention-and-churn-killer. Customers already churned belong to win-back-campaign; account growth belongs to expansion-revenue; recurring QBR motion belongs to customer-success-qbr.\n",{"name":5,"metadata":24,"description":7},{"title":25},"Churn Reduction","main","97a13607d669157a4a16d5ba79b77f208319f7b9","skills\u002Fchurn-reduction",3,"https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills","https:\u002F\u002Fskillme.dev","https:\u002F\u002Fgithub.com\u002FSkillMedev","MCP-native catalog of Claude Agent Skills. Browse, recommend, install, and auto-apply skills in chat. Open source, every skill free and clonable on GitHub.","2026-10-14 03:19:31",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add SkillMedev\u002Fskills --skill churn-reduction",{"tool":41,"command":42},"bunx","bunx skills add SkillMedev\u002Fskills --skill churn-reduction",{"tool":44,"command":45},"pnpm","pnpm dlx skills add SkillMedev\u002Fskills --skill churn-reduction","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fchurn-reduction\u002FSKILL.md",true,[],[50,58,65,72,79,86],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":54,"categories":55,"keywords":56,"publishedAt":57},"01M75271B256DF3FX2VG5VRYEE","zone-2-cardio-plan","Use when someone asks to build a steady-state cardio plan, train an aerobic base or Zone 2, structure easy or endurance weekly cardio, train by heart-rate zones, or improve resting heart rate, VO2, or endurance. Builds a heart-rate-zone aerobic training block with session structure, modality choice, and progression. General fitness guidance, not medical advice. Do NOT use to design a strength or lifting program (use Strength Training Plan), high-intensity interval conditioning, HIIT, or circuits (use Conditioning & HIIT Program), or a mobility\u002Fflexibility routine (use Mobility Routine).","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzone-2-cardio-plan\u002FSKILL.md",[],[],"2026-11-11 18:27:18",{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":57},"01M7526VP1QBBKYBE17S1WR86Y","youtube-script-writer","Writes retention-engineered long-form YouTube scripts on a hook\u002Fbody\u002Fpayoff architecture - a first-30-seconds hook that survives the retention cliff, a beat-sheet body with mini-hooks and bridges, timed pattern interrupts, and a payoff that over-delivers - with B-roll and editing cues marked inline. Use when someone says \"script my next video\", \"my retention graph dies at the start\", \"tighten this draft\", or \"outline a video on X\". Do NOT use for the 3-second opener of TikTok, Reels, or Shorts - use video-hook-writer instead; do NOT use for spoken voiceover synced to on-screen tutorial actions - use narration-script instead; do NOT use for a sales demo built on a buyer's discovery notes - use demo-script instead; do NOT use to design the thumbnail and title - use thumbnail-concept instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-script-writer\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":57},"01M7526P2T0WKMHDXS4Q58237T","win-back-campaign","Designs segmented win-back email sequences for dormant users - dormancy tiers, a three-beat sequence with a single proportional incentive, and a suppression rule - and measures success by re-activation, not opens. Use when someone says \"our users went quiet\", \"design a win-back campaign\", \"how do I re-engage lapsed customers\", \"should we email people who haven't logged in for months\", or lifecycle messaging has stopped landing with a lapsed segment. Do NOT use for recovering an unfinished checkout - use abandoned-cart-sequence instead. Do NOT use for diagnosing and fixing why active customers cancel - use churn-reduction instead. Do NOT use for building always-on onboarding or nurture flows - use email-drip-builder instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwin-back-campaign\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":57},"01M7526HF2RCDJ50P93WRZRHVV","wikipedia-style","Writes neutral, verifiable, encyclopedic prose following Wikipedia's core content policies and manual of style - NPOV, inline citations, notability-backed claims, and a strict ban on promotional language. Use when someone asks \"write this like a Wikipedia article\", \"draft a Wikipedia page for this company or person\", \"make this neutral and encyclopedic\", or \"why does my article draft keep getting rejected\". Do NOT use for persuasive or opinion writing - use op-ed-writer instead; for thesis-driven academic papers, use academic-essay; for verifying claims in existing text, use fact-checker.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwikipedia-style\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":57},"01M7526D43CTGRKGKNQ2GB0KVX","whitepapers","Structures and writes authoritative B2B whitepapers - a standalone executive summary, a data-sized problem statement, methodology, evidence-driven findings, recommendations, and a soft call to action - flagging every claim that needs a real citation. Use when a marketer asks \"write a whitepaper on this topic\", \"turn our research into a lead-gen asset\", \"make this credible enough for executives\", or a draft reads like a product pitch instead of analysis. Do NOT use for customer-story-driven proof - use case-study-builder instead; for policy audiences, use policy-brief.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwhitepapers\u002FSKILL.md",[],[],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":57},"01M75268423Y3K50SY5WC2NX2G","white-space-analysis","Finds unmet market gaps by ranking jobs-to-be-done on importance versus satisfaction, overlaying them against current solutions and workarounds, and validating that each gap is real, winnable, and worth winning. Use when someone asks \"where are the gaps in this market\", \"what's underserved here\", \"where should we play next\", or is hunting for product, feature, or market-entry opportunities. Do NOT use for profiling competitors and building battle cards - use competitive-intelligence instead; for putting a dollar figure on an identified opportunity - use market-sizing instead; for extracting jobs-to-be-done from raw customer interviews - use jtbd-extractor instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwhite-space-analysis\u002FSKILL.md",[],[]]