[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CT6NZ9HM8SATDDGS2F4FYB":3,"skill-same:01M6CT6NZ9HM8SATDDGS2F4FYB":60},{"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":23,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":42,"repositoryHtmlUrl":43,"repositoryHomepageUrl":6,"ownerHtmlUrl":44,"ownerBlogUrl":6,"ownerDescription":45,"updatedAt":46,"install":47,"resources":59},"01M6CT6NZ9HM8SATDDGS2F4FYB","activation-onboarding",null,"Designs the first-run flow and experiments that get new users to their first moment of value. Use when sign-ups are not turning into active users.","vibbs\u002Fcompany-os","vibbs","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F7379149?v=4","01M6CT6M03CAS1H7RA89F3965B","Vibbs Dod","01M6CT6M4MY60C9FFQ3AAR2RSK",6,"MIT","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Factivation-onboarding\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32],"onboarding","activation","activation onboarding","experiments","run","flow","first","use","active","2026-11-10 10:12:30",false,"---\nname: activation-onboarding\ndescription: Designs the first-run flow and experiments that get new users to their first moment of value. Use when sign-ups are not turning into active users.\nuser-invocable: true\n---\n\n# Activation & Onboarding\n\n## Reference\n- **ID**: S-GRO-05\n- **Category**: Growth\n- **Inputs**: user journey data, sign-up funnel metrics, product analytics, ICP document\n- **Outputs**: activation playbook → artifacts\u002Fgrowth\u002F\n- **Used by**: Growth Agent\n- **Tool scripts**: .\u002Ftools\u002Fartifact\u002Fvalidate.sh\n\n## Purpose\nDesigns the onboarding experience that guides new users to their \"aha moment\" as quickly as possible, and defines activation experiments to systematically improve conversion from sign-up to active user.\n\n## Procedure\n\n### Step 0: Pre-Data Mode Check\n\nRead `company.stage` from `company.config.yaml`. If `idea`\u002F`mvp` with fewer than 100 users:\n\n**Pre-Data Mode** — pivot to hypothesis-driven activation:\n1. Define \"aha moment\" hypothesis from ICP document and customer conversations (not data)\n2. Instrument the hypothesized aha moment immediately (use instrumentation skill)\n3. Define \"enough data\" threshold before running experiments\n4. Replace A\u002FB test recommendations with manual interview protocol for 5 users\n5. Use discovery-validation and customer-conversations skills to validate hypotheses\n\nProceed to full data-driven activation design once threshold is met.\n\n1. Define the \"aha moment\": the specific action that correlates with long-term retention.\n2. Map the current onboarding flow from sign-up to aha moment, step by step.\n3. Identify drop-off points in the funnel using product analytics data.\n4. For each drop-off point, hypothesize the friction cause.\n5. Design interventions: simplify steps, add guidance, remove unnecessary steps, add progress indicators.\n6. Define activation metrics: time-to-value, activation rate, Day 1\u002F7\u002F30 retention.\n7. Design 2-3 activation experiments with clear hypotheses and success criteria.\n8. Define the experiment execution plan: A\u002FB test setup, sample size, duration.\n9. Outline the post-experiment analysis framework.\n10. Save the activation playbook to `artifacts\u002Fgrowth\u002F`.\n11. Validate the artifact using `.\u002Ftools\u002Fartifact\u002Fvalidate.sh`.\n\n## Progressive Discovery Integration\n\nWhen the `feature_flags.strategy` in `company.config.yaml` includes progressive discovery (`progressive-discovery` or `full`), coordinate with the Feature Flags skill:\n\n- **Map aha moments to discovery levels**: The aha moment from Step 1 should be the trigger that unlocks Level 1 (Foundations) features\n- **Align activation experiments with feature reveals**: A\u002FB tests can test different discovery level thresholds\n- **Time-to-value acceleration**: Onboarding interventions should guide users toward the triggers that unlock the next discovery level\n- **Feature reveal coordination**: When a user unlocks a new discovery level, trigger the corresponding guided tour (from the User Docs skill)\n\nDiscovery levels (from feature-flags skill):\n- **Level 0 (Core)**: Always visible — essential product value\n- **Level 1 (Foundations)**: Unlocked after aha moment \u002F core flow completion\n- **Level 2 (Power)**: Unlocked after proficiency demonstrated\n- **Level 3 (Expert)**: Unlocked on request or sustained engagement\n\n## Quality Checklist\n- [ ] \"Aha moment\" is data-backed or has a clear hypothesis\n- [ ] Current onboarding flow is mapped end-to-end\n- [ ] Drop-off points are identified with data\n- [ ] Interventions are specific and actionable\n- [ ] Activation metrics are defined with baseline values\n- [ ] Experiments have clear hypotheses and success criteria\n- [ ] Sample size and duration are calculated\n- [ ] Artifact passes validation\n","# Activation & Onboarding\n\n## Reference\n- **ID**: S-GRO-05\n- **Category**: Growth\n- **Inputs**: user journey data, sign-up funnel metrics, product analytics, ICP document\n- **Outputs**: activation playbook → artifacts\u002Fgrowth\u002F\n- **Used by**: Growth Agent\n- **Tool scripts**: .\u002Ftools\u002Fartifact\u002Fvalidate.sh\n\n## Purpose\nDesigns the onboarding experience that guides new users to their \"aha moment\" as quickly as possible, and defines activation experiments to systematically improve conversion from sign-up to active user.\n\n## Procedure\n\n### Step 0: Pre-Data Mode Check\n\nRead `company.stage` from `company.config.yaml`. If `idea`\u002F`mvp` with fewer than 100 users:\n\n**Pre-Data Mode** — pivot to hypothesis-driven activation:\n1. Define \"aha moment\" hypothesis from ICP document and customer conversations (not data)\n2. Instrument the hypothesized aha moment immediately (use instrumentation skill)\n3. Define \"enough data\" threshold before running experiments\n4. Replace A\u002FB test recommendations with manual interview protocol for 5 users\n5. Use discovery-validation and customer-conversations skills to validate hypotheses\n\nProceed to full data-driven activation design once threshold is met.\n\n1. Define the \"aha moment\": the specific action that correlates with long-term retention.\n2. Map the current onboarding flow from sign-up to aha moment, step by step.\n3. Identify drop-off points in the funnel using product analytics data.\n4. For each drop-off point, hypothesize the friction cause.\n5. Design interventions: simplify steps, add guidance, remove unnecessary steps, add progress indicators.\n6. Define activation metrics: time-to-value, activation rate, Day 1\u002F7\u002F30 retention.\n7. Design 2-3 activation experiments with clear hypotheses and success criteria.\n8. Define the experiment execution plan: A\u002FB test setup, sample size, duration.\n9. Outline the post-experiment analysis framework.\n10. Save the activation playbook to `artifacts\u002Fgrowth\u002F`.\n11. Validate the artifact using `.\u002Ftools\u002Fartifact\u002Fvalidate.sh`.\n\n## Progressive Discovery Integration\n\nWhen the `feature_flags.strategy` in `company.config.yaml` includes progressive discovery (`progressive-discovery` or `full`), coordinate with the Feature Flags skill:\n\n- **Map aha moments to discovery levels**: The aha moment from Step 1 should be the trigger that unlocks Level 1 (Foundations) features\n- **Align activation experiments with feature reveals**: A\u002FB tests can test different discovery level thresholds\n- **Time-to-value acceleration**: Onboarding interventions should guide users toward the triggers that unlock the next discovery level\n- **Feature reveal coordination**: When a user unlocks a new discovery level, trigger the corresponding guided tour (from the User Docs skill)\n\nDiscovery levels (from feature-flags skill):\n- **Level 0 (Core)**: Always visible — essential product value\n- **Level 1 (Foundations)**: Unlocked after aha moment \u002F core flow completion\n- **Level 2 (Power)**: Unlocked after proficiency demonstrated\n- **Level 3 (Expert)**: Unlocked on request or sustained engagement\n\n## Quality Checklist\n- [ ] \"Aha moment\" is data-backed or has a clear hypothesis\n- [ ] Current onboarding flow is mapped end-to-end\n- [ ] Drop-off points are identified with data\n- [ ] Interventions are specific and actionable\n- [ ] Activation metrics are defined with baseline values\n- [ ] Experiments have clear hypotheses and success criteria\n- [ ] Sample size and duration are calculated\n- [ ] Artifact passes validation\n",{"name":5,"description":7,"user-invocable":38},true,"main","e842fb6d0e04360c47db78bc2e2e2987314d64d4",".claude\u002Fskills\u002Factivation-onboarding",2,"https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os","https:\u002F\u002Fgithub.com\u002Fvibbs","Principal Engineer · Applied AI · building ArcAI @ GrowthArc","2026-10-21 02:08:08",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":38},[49,52,55],{"tool":50,"command":51},"npx","npx skills add vibbs\u002Fcompany-os --skill activation-onboarding",{"tool":53,"command":54},"bunx","bunx skills add vibbs\u002Fcompany-os --skill activation-onboarding",{"tool":56,"command":57},"pnpm","pnpm dlx skills add vibbs\u002Fcompany-os --skill activation-onboarding","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Factivation-onboarding\u002FSKILL.md",[],[61,70,78,88,104,122],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":67,"publishedAt":33},"01M6CTHBW1E64ECYGD105N4S5E","typo-field","Fixture skill with a misspelled top-level field.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fskills\u002Ftypo-field\u002FSKILL.md",[],[68,69],"typo field","fixture skill",{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":33},"01M6CTHABTV8FKVDHFMMKPAV51","spaced-comments","Fixture skill with comments and spaces after block keys.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fskills\u002Fspaced-comments\u002FSKILL.md",[],[77,69],"spaced comments",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":33},"01M6CTH8MHC14KB65ZDHYBJW1N","crlf-comments","Fixture skill with Windows line endings.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fskills\u002Fcrlf-comments\u002FSKILL.md",[],[85,86,87,69],"crlf comments","CRLF","windows",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":33},"01M6CTH6TBKTDP9G8QB1Z2K0TY","claude-fields","Fixture skill using top-level fields Claude Code documents.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Ftests\u002Ffixtures\u002Fskills\u002Fclaude-fields\u002FSKILL.md",[],[95,96,97,98,99,100,69,101,102,103],"claude fields","explore","documents","src","grep","read","Bash","code","testing",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":115,"publishedAt":33},"01M6CTH46BGKXH80RWS876Y2CW","workflow-router","Maps an objective to the artifacts, stages and agents it needs. Use when deciding what happens next or which agents to involve.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Fos\u002Fskills\u002Fworkflow-router\u002FSKILL.md",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[116,117,118,119,120,31,121],"workflow","router","workflow router","next","maps","artifacts",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":33},"01M6CTH27653SRVP2FMEQN3J5B","weekly-review","Summarizes your week: what shipped, AI spend, support themes, security health and next priorities. Use at the end of each week.","https:\u002F\u002Fgithub.com\u002Fvibbs\u002Fcompany-os\u002Fblob\u002Fmain\u002Fos\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[129,130,131,119,31,132,133,134,135],"weekly review","review","priorities","health","save","support","security"]