[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DXFF79SHDPJT53BZ2WZB8N":3,"skill-same:01M6DXFF79SHDPJT53BZ2WZB8N":139},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":42,"ownerDescription":43,"updatedAt":44,"install":45,"resources":58},"01M6DXFF79SHDPJT53BZ2WZB8N","motherduck-explore",null,"Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.","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-explore\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30],"explore","motherduck","motherduck explore","data catalog","search","data","table","catalog","use","databases","preview","discover","2026-11-10 10:12:30",false,"---\nname: motherduck-explore\ndescription: Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.\nlicense: MIT\n---\n\n# Explore MotherDuck Data\n\nUse this skill when you need to discover what databases, tables, and columns exist in a MotherDuck account; preview and sample data; understand schemas and data types; find shared databases; or search the data catalog.\n\n## Prerequisites\n\n- An established MotherDuck connection (or an active MotherDuck MCP server)\n\n## Default Posture\n\n- Explore top-down: databases, then tables\u002Fviews, then columns, then statistics, then sample rows.\n- Use fully qualified table names once more than one database is attached.\n- Check shared databases before concluding that data is unavailable.\n- Use the MotherDuck MCP tools (`list_databases`, `list_tables`, `list_columns`, `search_catalog`) when available because they return structured results faster than ad hoc SQL.\n- Return a concise schema map with table grain, join keys, date columns, and likely measures before moving into modeling or dashboard work.\n\n## Workflow\n\n1. List databases in scope.\n2. List tables and views in the target database.\n3. Inspect columns, types, nullability, and comments before writing queries.\n4. Run `SUMMARIZE` on important tables to understand ranges, cardinality, and null rates.\n5. Preview rows, capture grain and join assumptions, and only then move into analytical SQL or modeling work.\n\n## Open Next\n\n- Read `references\u002FEXPLORATION_PLAYBOOK.md` for the full SQL workflow, share discovery patterns, MCP tool guidance, and common exploration mistakes\n\n## Related Skills\n\n- `motherduck-connect` for session setup and authentication\n- `motherduck-query` for analytical SQL after the schema is understood\n- `motherduck-duckdb-sql` for DuckDB syntax patterns during exploration\n- `motherduck-share-data` for creating and consuming shares once shared datasets become part of the workflow\n","# Explore MotherDuck Data\n\nUse this skill when you need to discover what databases, tables, and columns exist in a MotherDuck account; preview and sample data; understand schemas and data types; find shared databases; or search the data catalog.\n\n## Prerequisites\n\n- An established MotherDuck connection (or an active MotherDuck MCP server)\n\n## Default Posture\n\n- Explore top-down: databases, then tables\u002Fviews, then columns, then statistics, then sample rows.\n- Use fully qualified table names once more than one database is attached.\n- Check shared databases before concluding that data is unavailable.\n- Use the MotherDuck MCP tools (`list_databases`, `list_tables`, `list_columns`, `search_catalog`) when available because they return structured results faster than ad hoc SQL.\n- Return a concise schema map with table grain, join keys, date columns, and likely measures before moving into modeling or dashboard work.\n\n## Workflow\n\n1. List databases in scope.\n2. List tables and views in the target database.\n3. Inspect columns, types, nullability, and comments before writing queries.\n4. Run `SUMMARIZE` on important tables to understand ranges, cardinality, and null rates.\n5. 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Create a dagster project, uv virtual environment, and everything needed for a user to run dg dev or dg check defs successfully. 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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",[167],{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[173,159,148,174,175,160,176,177,27,178],"component","create custom dagster component","support","demo","structure","integration",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":185,"publishedAt":31},"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",[],[186,187,188,189,150,190,191,27,154,192,193,194,195,196,197,198,199],"Pydantic","harness","pydantic ai","pydantic ai harness","batteries","pydantic-ai","tool","sandboxing","execution","capabilities","code","ai agents","python","ai agent",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":31},"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. 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