[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CRRRT84TY3FF6CVVZWHK48":3,"skill-same:01M6CRRRT84TY3FF6CVVZWHK48":81},{"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":31,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":6,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6CRRRT84TY3FF6CVVZWHK48","analytics-product",null,"Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fanalytics-product\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"analytics","product","analytics product","cli","gemini","antigravity","codex","code","metrics","posthog","dashboards","cursor","mixpanel","community","claude-code","2026-11-10 10:12:30",false,"---\nname: analytics-product\ndescription: \"Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.\"\nrisk: none\nsource: community\ndate_added: '2026-03-06'\nauthor: renat\ntags:\n- analytics\n- product\n- metrics\n- posthog\n- mixpanel\ntools:\n- claude-code\n- antigravity\n- cursor\n- gemini-cli\n- codex-cli\n---\n\n# ANALYTICS-PRODUCT — Decida com Dados\n\n## Overview\n\nAnalytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU\u002FMAU, feature flags, A\u002FB testing, north star metric, OKRs, dashboard de produto.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to analytics product\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\n```\n[objeto]_[verbo_passado]\n\nCorreto:   user_signed_up, conversation_started, upgrade_completed\nErrado:    signup, click, conversion\n```\n\n## Analytics-Product — Decida Com Dados\n\n> \"In God we trust. All others must bring data.\" — W. Edwards Deming\n\n---\n\n## Eventos Essenciais Da Auri\n\n```python\nAURI_EVENTS = {\n    # Aquisicao\n    \"user_signed_up\":        {\"props\": [\"source\", \"medium\", \"campaign\"]},\n    \"onboarding_started\":    {\"props\": [\"step_count\"]},\n    \"onboarding_completed\":  {\"props\": [\"time_to_complete\", \"steps_skipped\"]},\n\n    # Ativacao\n    \"first_conversation\":    {\"props\": [\"intent\", \"response_time\"]},\n    \"aha_moment_reached\":    {\"props\": [\"trigger\", \"session_number\"]},\n    \"feature_discovered\":    {\"props\": [\"feature_name\", \"discovery_method\"]},\n\n    # Retencao\n    \"conversation_started\":  {\"props\": [\"intent\", \"user_tier\", \"device\"]},\n    \"conversation_completed\":{\"props\": [\"messages_count\", \"duration\", \"rating\"]},\n    \"session_started\":       {\"props\": [\"days_since_last\", \"platform\"]},\n\n    # Receita\n    \"upgrade_viewed\":        {\"props\": [\"trigger\", \"current_tier\"]},\n    \"upgrade_started\":       {\"props\": [\"target_tier\", \"trigger\"]},\n    \"upgrade_completed\":     {\"props\": [\"tier\", \"plan\", \"revenue\"]},\n    \"subscription_canceled\": {\"props\": [\"reason\", \"tier\", \"tenure_days\"]},\n    \"payment_failed\":        {\"props\": [\"attempt_count\", \"error_code\"]},\n}\n```\n\n## Implementacao Posthog (Python)\n\n```python\nfrom posthog import Posthog\nimport os\n\nposthog = Posthog(\n    project_api_key=os.environ[\"POSTHOG_API_KEY\"],\n    host=os.environ.get(\"POSTHOG_HOST\", \"https:\u002F\u002Fapp.posthog.com\")\n)\n\ndef track(user_id: str, event: str, properties: dict = None):\n    posthog.capture(\n        distinct_id=user_id,\n        event=event,\n        properties=properties or {}\n    )\n\ndef identify(user_id: str, traits: dict):\n    posthog.identify(\n        distinct_id=user_id,\n        properties=traits\n    )\n\n## Uso:\n\ntrack(\"user_123\", \"conversation_started\", {\n    \"intent\": \"business_advice\",\n    \"device\": \"alexa\",\n    \"user_tier\": \"pro\"\n})\n```\n\n---\n\n## Funil De Ativacao Auri\n\n```\nVisita landing page          (100%)\n    | [meta: 40%]\nClicou \"Experimentar\"         (40%)\n    | [meta: 70%]\nCompletou cadastro            (28%)\n    | [meta: 60%]\nFez primeira conversa         (17%)  \u003C- AHA MOMENT\n    | [meta: 50%]\nVoltou no dia seguinte        (8.5%)\n    | [meta: 40%]\nUsou 3+ dias na semana        (3.4%)\n    | [meta: 20%]\nConverteu para Pro            (0.7%)\n```\n\n## Otimizando O Funil\n\n```\nPara cada drop-off > benchmark:\n1. Identificar: onde exatamente o usuario sai?\n2. Entender: por que? (session recordings, surveys)\n3. Hipotese: qual mudanca poderia melhorar?\n4. Testar: A\u002FB test com amostra estatisticamente significante\n5. Medir: 2 semanas minimo, p-value \u003C 0.05\n6. Aprender: mesmo se falhar, entende-se o usuario melhor\n```\n\n---\n\n## Analise De Cohort (Retencao Semanal)\n\n```python\ndef calculate_cohort_retention(events_df):\n    \"\"\"\n    events_df: DataFrame com colunas [user_id, event_date, event_name]\n    Retorna: matriz de retencao [cohort_week x week_number]\n    \"\"\"\n    import pandas as pd\n\n    first_session = events_df[events_df.event_name == \"session_started\"] \\\n        .groupby(\"user_id\")[\"event_date\"].min() \\\n        .dt.to_period(\"W\")\n\n    sessions = events_df[events_df.event_name == \"session_started\"].copy()\n    sessions[\"cohort\"] = sessions[\"user_id\"].map(first_session)\n    sessions[\"weeks_since\"] = (\n        sessions[\"event_date\"].dt.to_period(\"W\") - sessions[\"cohort\"]\n    ).apply(lambda x: x.n)\n\n    cohort_data = sessions.groupby([\"cohort\", \"weeks_since\"])[\"user_id\"].nunique()\n    cohort_sizes = cohort_data.unstack().iloc[:, 0]\n    retention = cohort_data.unstack().divide(cohort_sizes, axis=0) * 100\n\n    return retention\n```\n\n## Benchmarks De Retencao (Assistentes De Voz)\n\n| Semana | Pessimo | Ok | Bom | Excelente |\n|--------|---------|-----|-----|-----------|\n| W1 | \u003C20% | 20-35% | 35-50% | >50% |\n| W4 | \u003C10% | 10-20% | 20-30% | >30% |\n| W8 | \u003C5% | 5-12% | 12-20% | >20% |\n\n---\n\n## Definindo A North Star Da Auri\n\n```\nFramework:\n1. O que cria valor real para o usuario? -> Conversas que geram insight\u002Facao\n2. O que prediz crescimento de longo prazo? -> Usuarios com 3+ conv\u002Fsemana\n3. Como medir? -> \"Weekly Active Conversationalists\" (WAC)\n\nNorth Star: WAC (Weekly Active Conversationalists)\nDefinicao: Usuarios com >= 3 conversas na semana que duraram >= 2 minutos\n\nMeta Ano 1: 10.000 WAC\nMeta Ano 2: 100.000 WAC\n```\n\n## Dashboard North Star\n\n```python\ndef calculate_north_star(db):\n    wac = db.query(\"\"\"\n        SELECT COUNT(DISTINCT user_id) as wac\n        FROM conversations\n        WHERE\n            created_at >= NOW() - INTERVAL '7 days'\n            AND duration_seconds >= 120\n        GROUP BY user_id\n        HAVING COUNT(*) >= 3\n    \"\"\").scalar()\n\n    return {\n        \"wac\": wac,\n        \"wow_growth\": calculate_wow_growth(db, \"wac\"),\n        \"target\": 10000,\n        \"progress\": f\"{wac\u002F10000*100:.1f}%\"\n    }\n```\n\n---\n\n## Feature Flags Com Posthog\n\n```python\ndef is_feature_enabled(user_id: str, feature: str) -> bool:\n    return posthog.feature_enabled(feature, user_id)\n\nif is_feature_enabled(user_id, \"new-onboarding-v2\"):\n    show_new_onboarding()\nelse:\n    show_old_onboarding()\n```\n\n## Calculadora De Significancia Estatistica\n\n```python\nfrom scipy import stats\nimport numpy as np\n\ndef ab_test_significance(\n    control_conversions: int,\n    control_visitors: int,\n    variant_conversions: int,\n    variant_visitors: int,\n    confidence: float = 0.95\n) -> dict:\n    control_rate = control_conversions \u002F control_visitors\n    variant_rate = variant_conversions \u002F variant_visitors\n    lift = (variant_rate - control_rate) \u002F control_rate * 100\n\n    _, p_value = stats.chi2_contingency([\n        [control_conversions, control_visitors - control_conversions],\n        [variant_conversions, variant_visitors - variant_conversions]\n    ])[:2]\n\n    significant = p_value \u003C (1 - confidence)\n\n    return {\n        \"control_rate\": f\"{control_rate*100:.2f}%\",\n        \"variant_rate\": f\"{variant_rate*100:.2f}%\",\n        \"lift\": f\"{lift:+.1f}%\",\n        \"p_value\": round(p_value, 4),\n        \"significant\": significant,\n        \"recommendation\": \"Deploy variant\" if significant and lift > 0 else \"Keep control\"\n    }\n```\n\n---\n\n## 6. Comandos\n\n| Comando | Acao |\n|---------|------|\n| `\u002Fevent-taxonomy` | Define taxonomia de eventos |\n| `\u002Ffunnel-analysis` | Analisa funil de conversao |\n| `\u002Fcohort-retention` | Calcula retencao por cohort |\n| `\u002Fnorth-star` | Define ou revisa North Star Metric |\n| `\u002Fab-test` | Calcula significancia de A\u002FB test |\n| `\u002Fdashboard-setup` | Cria dashboard de produto |\n| `\u002Fokr-template` | Template de OKRs para produto |\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n\n## Related Skills\n\n- `growth-engine` - Complementary skill for enhanced analysis\n- `monetization` - Complementary skill for enhanced analysis\n- `product-design` - Complementary skill for enhanced analysis\n- `product-inventor` - Complementary skill for enhanced analysis\n","# ANALYTICS-PRODUCT — Decida com Dados\n\n## Overview\n\nAnalytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU\u002FMAU, feature flags, A\u002FB testing, north star metric, OKRs, dashboard de produto.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to analytics product\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\n```\n[objeto]_[verbo_passado]\n\nCorreto:   user_signed_up, conversation_started, upgrade_completed\nErrado:    signup, click, conversion\n```\n\n## Analytics-Product — Decida Com Dados\n\n> \"In God we trust. All others must bring data.\" — W. Edwards Deming\n\n---\n\n## Eventos Essenciais Da Auri\n\n```python\nAURI_EVENTS = {\n    # Aquisicao\n    \"user_signed_up\":        {\"props\": [\"source\", \"medium\", \"campaign\"]},\n    \"onboarding_started\":    {\"props\": [\"step_count\"]},\n    \"onboarding_completed\":  {\"props\": [\"time_to_complete\", \"steps_skipped\"]},\n\n    # Ativacao\n    \"first_conversation\":    {\"props\": [\"intent\", \"response_time\"]},\n    \"aha_moment_reached\":    {\"props\": [\"trigger\", \"session_number\"]},\n    \"feature_discovered\":    {\"props\": [\"feature_name\", \"discovery_method\"]},\n\n    # Retencao\n    \"conversation_started\":  {\"props\": [\"intent\", \"user_tier\", \"device\"]},\n    \"conversation_completed\":{\"props\": [\"messages_count\", \"duration\", \"rating\"]},\n    \"session_started\":       {\"props\": [\"days_since_last\", \"platform\"]},\n\n    # Receita\n    \"upgrade_viewed\":        {\"props\": [\"trigger\", \"current_tier\"]},\n    \"upgrade_started\":       {\"props\": [\"target_tier\", \"trigger\"]},\n    \"upgrade_completed\":     {\"props\": [\"tier\", \"plan\", \"revenue\"]},\n    \"subscription_canceled\": {\"props\": [\"reason\", \"tier\", \"tenure_days\"]},\n    \"payment_failed\":        {\"props\": [\"attempt_count\", \"error_code\"]},\n}\n```\n\n## Implementacao Posthog (Python)\n\n```python\nfrom posthog import Posthog\nimport os\n\nposthog = Posthog(\n    project_api_key=os.environ[\"POSTHOG_API_KEY\"],\n    host=os.environ.get(\"POSTHOG_HOST\", \"https:\u002F\u002Fapp.posthog.com\")\n)\n\ndef track(user_id: str, event: str, properties: dict = None):\n    posthog.capture(\n        distinct_id=user_id,\n        event=event,\n        properties=properties or {}\n    )\n\ndef identify(user_id: str, traits: dict):\n    posthog.identify(\n        distinct_id=user_id,\n        properties=traits\n    )\n\n## Uso:\n\ntrack(\"user_123\", \"conversation_started\", {\n    \"intent\": \"business_advice\",\n    \"device\": \"alexa\",\n    \"user_tier\": \"pro\"\n})\n```\n\n---\n\n## Funil De Ativacao Auri\n\n```\nVisita landing page          (100%)\n    | [meta: 40%]\nClicou \"Experimentar\"         (40%)\n    | [meta: 70%]\nCompletou cadastro            (28%)\n    | [meta: 60%]\nFez primeira conversa         (17%)  \u003C- AHA MOMENT\n    | [meta: 50%]\nVoltou no dia seguinte        (8.5%)\n    | [meta: 40%]\nUsou 3+ dias na semana        (3.4%)\n    | [meta: 20%]\nConverteu para Pro            (0.7%)\n```\n\n## Otimizando O Funil\n\n```\nPara cada drop-off > benchmark:\n1. Identificar: onde exatamente o usuario sai?\n2. Entender: por que? (session recordings, surveys)\n3. Hipotese: qual mudanca poderia melhorar?\n4. Testar: A\u002FB test com amostra estatisticamente significante\n5. Medir: 2 semanas minimo, p-value \u003C 0.05\n6. Aprender: mesmo se falhar, entende-se o usuario melhor\n```\n\n---\n\n## Analise De Cohort (Retencao Semanal)\n\n```python\ndef calculate_cohort_retention(events_df):\n    \"\"\"\n    events_df: DataFrame com colunas [user_id, event_date, event_name]\n    Retorna: matriz de retencao [cohort_week x week_number]\n    \"\"\"\n    import pandas as pd\n\n    first_session = events_df[events_df.event_name == \"session_started\"] \\\n        .groupby(\"user_id\")[\"event_date\"].min() \\\n        .dt.to_period(\"W\")\n\n    sessions = events_df[events_df.event_name == \"session_started\"].copy()\n    sessions[\"cohort\"] = sessions[\"user_id\"].map(first_session)\n    sessions[\"weeks_since\"] = (\n        sessions[\"event_date\"].dt.to_period(\"W\") - sessions[\"cohort\"]\n    ).apply(lambda x: x.n)\n\n    cohort_data = sessions.groupby([\"cohort\", \"weeks_since\"])[\"user_id\"].nunique()\n    cohort_sizes = cohort_data.unstack().iloc[:, 0]\n    retention = cohort_data.unstack().divide(cohort_sizes, axis=0) * 100\n\n    return retention\n```\n\n## Benchmarks De Retencao (Assistentes De Voz)\n\n| Semana | Pessimo | Ok | Bom | Excelente |\n|--------|---------|-----|-----|-----------|\n| W1 | \u003C20% | 20-35% | 35-50% | >50% |\n| W4 | \u003C10% | 10-20% | 20-30% | >30% |\n| W8 | \u003C5% | 5-12% | 12-20% | >20% |\n\n---\n\n## Definindo A North Star Da Auri\n\n```\nFramework:\n1. O que cria valor real para o usuario? -> Conversas que geram insight\u002Facao\n2. O que prediz crescimento de longo prazo? -> Usuarios com 3+ conv\u002Fsemana\n3. Como medir? -> \"Weekly Active Conversationalists\" (WAC)\n\nNorth Star: WAC (Weekly Active Conversationalists)\nDefinicao: Usuarios com >= 3 conversas na semana que duraram >= 2 minutos\n\nMeta Ano 1: 10.000 WAC\nMeta Ano 2: 100.000 WAC\n```\n\n## Dashboard North Star\n\n```python\ndef calculate_north_star(db):\n    wac = db.query(\"\"\"\n        SELECT COUNT(DISTINCT user_id) as wac\n        FROM conversations\n        WHERE\n            created_at >= NOW() - INTERVAL '7 days'\n            AND duration_seconds >= 120\n        GROUP BY user_id\n        HAVING COUNT(*) >= 3\n    \"\"\").scalar()\n\n    return {\n        \"wac\": wac,\n        \"wow_growth\": calculate_wow_growth(db, \"wac\"),\n        \"target\": 10000,\n        \"progress\": f\"{wac\u002F10000*100:.1f}%\"\n    }\n```\n\n---\n\n## Feature Flags Com Posthog\n\n```python\ndef is_feature_enabled(user_id: str, feature: str) -> bool:\n    return posthog.feature_enabled(feature, user_id)\n\nif is_feature_enabled(user_id, \"new-onboarding-v2\"):\n    show_new_onboarding()\nelse:\n    show_old_onboarding()\n```\n\n## Calculadora De Significancia Estatistica\n\n```python\nfrom scipy import stats\nimport numpy as np\n\ndef ab_test_significance(\n    control_conversions: int,\n    control_visitors: int,\n    variant_conversions: int,\n    variant_visitors: int,\n    confidence: float = 0.95\n) -> dict:\n    control_rate = control_conversions \u002F control_visitors\n    variant_rate = variant_conversions \u002F variant_visitors\n    lift = (variant_rate - control_rate) \u002F control_rate * 100\n\n    _, p_value = stats.chi2_contingency([\n        [control_conversions, control_visitors - control_conversions],\n        [variant_conversions, variant_visitors - variant_conversions]\n    ])[:2]\n\n    significant = p_value \u003C (1 - confidence)\n\n    return {\n        \"control_rate\": f\"{control_rate*100:.2f}%\",\n        \"variant_rate\": f\"{variant_rate*100:.2f}%\",\n        \"lift\": f\"{lift:+.1f}%\",\n        \"p_value\": round(p_value, 4),\n        \"significant\": significant,\n        \"recommendation\": \"Deploy variant\" if significant and lift > 0 else \"Keep control\"\n    }\n```\n\n---\n\n## 6. Comandos\n\n| Comando | Acao |\n|---------|------|\n| `\u002Fevent-taxonomy` | Define taxonomia de eventos |\n| `\u002Ffunnel-analysis` | Analisa funil de conversao |\n| `\u002Fcohort-retention` | Calcula retencao por cohort |\n| `\u002Fnorth-star` | Define ou revisa North Star Metric |\n| `\u002Fab-test` | Calcula significancia de A\u002FB test |\n| `\u002Fdashboard-setup` | Cria dashboard de produto |\n| `\u002Fokr-template` | Template de OKRs para produto |\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n\n## Related Skills\n\n- `growth-engine` - Complementary skill for enhanced analysis\n- `monetization` - Complementary skill for enhanced analysis\n- `product-design` - Complementary skill for enhanced analysis\n- `product-inventor` - Complementary skill for enhanced analysis\n",{"name":5,"risk":52,"tags":53,"tools":54,"author":57,"source":45,"date_added":58,"description":7},"none",[32,33,40,41,44],[46,37,43,55,56],"gemini-cli","codex-cli","renat","2026-03-06","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fanalytics-product",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill analytics-product",{"tool":73,"command":74},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill analytics-product",{"tool":76,"command":77},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill analytics-product","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fanalytics-product\u002FSKILL.md",true,[],[82,93,109,136,155,182],{"skillNo":83,"name":84,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":90,"publishedAt":47},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[87],{"categoryNo":88,"categoryName":89,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[91,92],"Test Skill","test",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":47},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[100,101,102,103,104,105,106,45,107,108],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":118,"publishedAt":47},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). 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