[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DXFBXYXZBWRHRAYE02VEN1":3,"skill-same:01M6DXFBXYXZBWRHRAYE02VEN1":84},{"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":28,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6DXFBXYXZBWRHRAYE02VEN1","motherduck-enable-self-serve-analytics",null,"Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.","cianfhoghlaim\u002Fcianfhoghlaim","cianfhoghlaim","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F57019547?v=4","01M6DWR3GAV495PYDTZSR1FZNX","Cian Mac (an Déisigh Uí) Liatháin || Deacy-Lyons","01M6DWR3N3Z768BB64XVJN0AY2",4,"MIT","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics\u002FSKILL.md",[18,23],{"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},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39],"analytics","self","motherduck","motherduck enable self serve analytics","teams","share","adoption","first","use","rollout","dataset","2026-11-10 10:12:30",false,"---\nname: motherduck-enable-self-serve-analytics\ndescription: Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.\nlicense: MIT\n---\n\n# Enable Self-Serve Analytics\n\nUse this skill when the user wants broad internal access to analytics with clear guardrails, trusted datasets, and a practical rollout path.\n\nThis is a use-case skill. It orchestrates `motherduck-explore`, `motherduck-query`, `motherduck-model-data`, `motherduck-create-dive`, and `motherduck-share-data`.\n\n## Start Here: Is a MotherDuck Server Active?\n\nAlways determine this first.\n\n- If a **remote MotherDuck MCP server** or **local MotherDuck server** is active, use it.\n- If the user has not named the target database, ask which database or workspace will power the rollout.\n- Explore the live data model before defining the rollout:\n  - trusted source tables\n  - candidate curated views\n  - department-level dimensions\n  - core KPIs\n  - share boundaries\n\nUse the actual data model to pick the first audience and first asset.\n\nIf no server is active, ask for a table list and target audience before drafting the rollout.\n\n## Use This Skill When\n\n- The user wants internal teams to answer their own questions.\n- The user needs a first rollout plan for Dives, dashboards, or shares.\n- The user needs adoption plus governance, not just chart creation.\n- The audience is internal; for external users or embedded product analytics, use `motherduck-build-cfa-app`.\n\n## Rollout Defaults\n\n- first audience first, not company-wide exposure\n- curated dataset before broad access\n- Dive or share boundary over raw table dumping\n- standard ownership for metric changes\n- lightweight metric definitions and owners before inviting more users\n\n## Workflow\n\n1. Confirm whether live MotherDuck discovery is available.\n2. Inspect the data model that internal teams would use.\n3. Pick the first audience and first use case.\n4. Publish one trusted dataset.\n5. Document the metric owner, refresh expectation, and access boundary.\n6. Publish one Dive or one share.\n7. Expand only after the first workflow is stable.\n\nWhen this skill produces a native DuckDB (`md:`) connection, watermark it with `custom_user_agent=agent-skills\u002F2.3.0(harness-\u003Charness>;llm-\u003Cllm>)`. If metadata is missing, fall back to `harness-unknown` and `llm-unknown`.\n\n## Output\n\nThe output of this skill should be:\n\n- the first audience\n- the first asset\n- the governing dataset\n- the ownership model\n- the rollout guardrails\n\nIf the caller explicitly asks for structured JSON, return raw JSON only with no Markdown fences or prose before\u002Fafter it.\nThis is mainly for automated tests, regression checks, or downstream tooling that needs a stable machine-readable shape. Normal human-facing use of the skill can stay in prose unless JSON is explicitly requested.\n\nUse this exact top-level shape when JSON is requested:\n\n```json\n{\n  \"summary\": {},\n  \"assumptions\": [],\n  \"implementation_plan\": [],\n  \"validation_plan\": [],\n  \"risks\": []\n}\n```\n\n## References\n\nRead this as reference, not as a script to execute:\n\n- `references\u002FSELF_SERVE_ROLLOUT_GUIDE.md` -- curate-publish-expand sequence, Dive-versus-share choice, data freshness checks, scale guidance, and starter snippets\n\n## Runnable Artifact\n\n- `artifacts\u002Fself_serve_rollout_example.py` -- MotherDuck-backed Python example that publishes a curated view and produces team KPI output for a first rollout asset\n- `artifacts\u002Fself_serve_rollout_example.ts` -- TypeScript companion artifact with the same rollout output contract\n\nRun it with:\n\n```bash\nuv run --with duckdb python skills\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.py\n```\n\nRun the same artifact against a temporary MotherDuck database:\n\n```bash\nMOTHERDUCK_ARTIFACT_USE_MOTHERDUCK=1 \\\nuv run --with duckdb python skills\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.py\n```\n\nValidate the TypeScript companion artifact:\n\n```bash\nuv run scripts\u002Ftest_typescript_artifacts.py\n```\n\n## Related Skills\n\n- `motherduck-explore` -- inspect the real workspace before rollout\n- `motherduck-query` -- validate KPI definitions\n- `motherduck-model-data` -- publish curated analytical views or tables\n- `motherduck-create-dive` -- build the first shareable answer surface\n- `motherduck-share-data` -- publish governed data access when users need SQL, not just a Dive\n","# Enable Self-Serve Analytics\n\nUse this skill when the user wants broad internal access to analytics with clear guardrails, trusted datasets, and a practical rollout path.\n\nThis is a use-case skill. It orchestrates `motherduck-explore`, `motherduck-query`, `motherduck-model-data`, `motherduck-create-dive`, and `motherduck-share-data`.\n\n## Start Here: Is a MotherDuck Server Active?\n\nAlways determine this first.\n\n- If a **remote MotherDuck MCP server** or **local MotherDuck server** is active, use it.\n- If the user has not named the target database, ask which database or workspace will power the rollout.\n- Explore the live data model before defining the rollout:\n  - trusted source tables\n  - candidate curated views\n  - department-level dimensions\n  - core KPIs\n  - share boundaries\n\nUse the actual data model to pick the first audience and first asset.\n\nIf no server is active, ask for a table list and target audience before drafting the rollout.\n\n## Use This Skill When\n\n- The user wants internal teams to answer their own questions.\n- The user needs a first rollout plan for Dives, dashboards, or shares.\n- The user needs adoption plus governance, not just chart creation.\n- The audience is internal; for external users or embedded product analytics, use `motherduck-build-cfa-app`.\n\n## Rollout Defaults\n\n- first audience first, not company-wide exposure\n- curated dataset before broad access\n- Dive or share boundary over raw table dumping\n- standard ownership for metric changes\n- lightweight metric definitions and owners before inviting more users\n\n## Workflow\n\n1. Confirm whether live MotherDuck discovery is available.\n2. Inspect the data model that internal teams would use.\n3. Pick the first audience and first use case.\n4. Publish one trusted dataset.\n5. Document the metric owner, refresh expectation, and access boundary.\n6. Publish one Dive or one share.\n7. Expand only after the first workflow is stable.\n\nWhen this skill produces a native DuckDB (`md:`) connection, watermark it with `custom_user_agent=agent-skills\u002F2.3.0(harness-\u003Charness>;llm-\u003Cllm>)`. If metadata is missing, fall back to `harness-unknown` and `llm-unknown`.\n\n## Output\n\nThe output of this skill should be:\n\n- the first audience\n- the first asset\n- the governing dataset\n- the ownership model\n- the rollout guardrails\n\nIf the caller explicitly asks for structured JSON, return raw JSON only with no Markdown fences or prose before\u002Fafter it.\nThis is mainly for automated tests, regression checks, or downstream tooling that needs a stable machine-readable shape. Normal human-facing use of the skill can stay in prose unless JSON is explicitly requested.\n\nUse this exact top-level shape when JSON is requested:\n\n```json\n{\n  \"summary\": {},\n  \"assumptions\": [],\n  \"implementation_plan\": [],\n  \"validation_plan\": [],\n  \"risks\": []\n}\n```\n\n## References\n\nRead this as reference, not as a script to execute:\n\n- `references\u002FSELF_SERVE_ROLLOUT_GUIDE.md` -- curate-publish-expand sequence, Dive-versus-share choice, data freshness checks, scale guidance, and starter snippets\n\n## Runnable Artifact\n\n- `artifacts\u002Fself_serve_rollout_example.py` -- MotherDuck-backed Python example that publishes a curated view and produces team KPI output for a first rollout asset\n- `artifacts\u002Fself_serve_rollout_example.ts` -- TypeScript companion artifact with the same rollout output contract\n\nRun it with:\n\n```bash\nuv run --with duckdb python skills\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.py\n```\n\nRun the same artifact against a temporary MotherDuck database:\n\n```bash\nMOTHERDUCK_ARTIFACT_USE_MOTHERDUCK=1 \\\nuv run --with duckdb python skills\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.py\n```\n\nValidate the TypeScript companion artifact:\n\n```bash\nuv run scripts\u002Ftest_typescript_artifacts.py\n```\n\n## Related Skills\n\n- `motherduck-explore` -- inspect the real workspace before rollout\n- `motherduck-query` -- validate KPI definitions\n- `motherduck-model-data` -- publish curated analytical views or tables\n- `motherduck-create-dive` -- build the first shareable answer surface\n- `motherduck-share-data` -- publish governed data access when users need SQL, not just a Dive\n",{"name":5,"license":15,"description":7},"main","5ace051462d0639f59bcc60912f4ec08c1a96772",".agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics",1,"https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim","cianfhoghlaim.ie","Irish-British teacher, mathematician, foghlaimeoir || minority-language AI researcher || Lucht Siúil ||\r\nFine Gael + Alliance Party + Liberal Democrats Member","2026-10-12 08:07:09",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add cianfhoghlaim\u002Fcianfhoghlaim --skill motherduck-enable-self-serve-analytics",{"tool":60,"command":61},"bunx","bunx skills add cianfhoghlaim\u002Fcianfhoghlaim --skill motherduck-enable-self-serve-analytics",{"tool":63,"command":64},"pnpm","pnpm dlx skills add cianfhoghlaim\u002Fcianfhoghlaim --skill motherduck-enable-self-serve-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics\u002FSKILL.md",true,[68,74,78],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"artifacts\u002Fself_serve_rollout_example.py","SCRIPT","脚本",2171,"https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.py",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"artifacts\u002Fself_serve_rollout_example.ts",1790,"https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics\u002Fartifacts\u002Fself_serve_rollout_example.ts",{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002FSELF_SERVE_ROLLOUT_GUIDE.md","REFERENCE","参考文档",9071,"https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-enable-self-serve-analytics\u002Freferences\u002FSELF_SERVE_ROLLOUT_GUIDE.md",[85,106,122,143,167,189],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":40},"01M6DXR6F7GB4JSD67XTX6WY4A","dagster-init","Initialize a dagster project using the create-dagster cli. Create a dagster project, uv virtual environment, and everything needed for a user to run dg dev or dg check defs successfully. (project)","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002Fdocs\u002Fresearch\u002Fdagster\u002Fdagster-dspy\u002F.claude\u002Fskills\u002Fdagster-init\u002FSKILL.md",[],[92,93,94,95,96,97,98,99,100,101,102,103,104,105],"init","dagster","dagster init","run","project","uv","environment","user","initialize","check","virtual","dev","create","cli",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":115,"publishedAt":40},"01M6DXQZ2HSP548JH2V3FQ7H2V","create-custom-dagster-component","Create a custom Dagster Component with demo mode support, realistic asset structure, and optional custom scaffolder using the dg CLI. Use this skill if there is no Component included in an existing integration or if Dagster does not have the integration.","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002Fdocs\u002Fresearch\u002Fdagster\u002Fdagster-dspy\u002F.claude\u002Fskills\u002Fcreate-custom-dagster-component\u002FSKILL.md",[112],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[116,104,93,117,118,105,119,120,37,121],"component","create custom dagster component","support","demo","structure","integration",{"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":40},"01M6DXHPZMQ4VAXH9PGBK2PNNP","pydantic-ai-harness","Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- currently Code Mode, which collapses many tool calls into one sandboxed Python execution. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fpydantic\u002Fpydantic-ai-harness\u002FSKILL.md",[],[129,130,131,132,95,133,134,37,99,135,136,137,138,139,140,141,142],"Pydantic","harness","pydantic ai","pydantic ai harness","batteries","pydantic-ai","tool","sandboxing","execution","capabilities","code","ai agents","python","ai agent",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":153,"publishedAt":40},"01M6DXH7JE54YEHM58JY8KY2AK","building-pydantic-ai-agents","Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools\u002Fcapabilities, defer capability loading, stream output, define agents from YAML, or test agent behavior.","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fpydantic\u002Fbuilding-pydantic-ai-agents\u002FSKILL.md",[149,152],{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},[140,129,131,154,141,155,142,156,157,158,159,160,37,161,162,163,164,165,138,166],"building pydantic ai agents","testing","structured output","define","agent patterns","patterns","build","behavior","multi-agent","Tools","yaml","streaming","test",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":40},"01M6DXGCVB0WSHBQ7BSA56A12C","motherduck-share-data","Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access\u002Fvisibility\u002Fupdate modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-share-data\u002FSKILL.md",[173],{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[34,175,31,176,37,177,178,179,180,181,182,183,184,185,186,187,188,104],"data","motherduck share data","distribution","organization","manage","only","refresh","read","public","access","team","ZeRO","database","update",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"publishedAt":40},"01M6DXG9NMFYC72KJWQVJ1D6BZ","motherduck-security-governance","Explain MotherDuck security, governance, and access-control patterns. Use for any question about SOC 2, GDPR, compliance, data residency, regions, SSO, service accounts, token handling, tenant isolation, sharing boundaries, snapshots and recovery, or governance posture — including when a security_compliance_owner, technical_owner, or application_builder is evaluating MotherDuck.","https:\u002F\u002Fgithub.com\u002Fcianfhoghlaim\u002Fcianfhoghlaim\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmotherduck\u002Fmotherduck-security-governance\u002FSKILL.md",[],[196,197,198,31,199,200,201,184,202,203,204,205,206,175,207,208,209,210,37,211],"governance","security","motherduck security governance","sso","sharing","recovery","explain","technical","compliance","soc","gdpr","snapshots","token","control","question","builder"]