motherduck-explore
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.
- License 许可
- MIT
- 最近更新
- 2026-10-12
- Stars
- ★ 4
- Forks
- 1
01 前言 Front Matter
- name
- motherduck-explore
- license
- MIT
- description
- 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.
02 资源文件 19
- 资产文件data/domain-complexity.csv7.2 KB
- 参考文档data/prd-purpose.md7.1 KB
- 资产文件data/project-types.csv3.1 KB
- 参考文档references/EXPLORATION_PLAYBOOK.md5.8 KB
- 参考文档steps-v/step-v-01-discovery.md7.0 KB
- 参考文档steps-v/step-v-02-format-detection.md6.4 KB
- 参考文档steps-v/step-v-02b-parity-check.md6.3 KB
- 参考文档steps-v/step-v-03-density-validation.md5.6 KB
- 参考文档steps-v/step-v-04-brief-coverage-validation.md7.0 KB
- 参考文档steps-v/step-v-05-measurability-validation.md6.9 KB
- 参考文档steps-v/step-v-06-traceability-validation.md7.1 KB
- 参考文档steps-v/step-v-07-implementation-leakage-validation.md6.8 KB
- 参考文档steps-v/step-v-08-domain-compliance-validation.md7.5 KB
- 参考文档steps-v/step-v-09-project-type-validation.md8.4 KB
- 参考文档steps-v/step-v-10-smart-validation.md6.6 KB
- 参考文档steps-v/step-v-11-holistic-quality-validation.md8.5 KB
- 参考文档steps-v/step-v-12-completeness-validation.md8.0 KB
- 参考文档steps-v/step-v-13-report-complete.md7.9 KB
- 参考文档workflow.md2.8 KB
03 SKILL.md 正文
Explore MotherDuck Data
Use 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.
Prerequisites
- An established MotherDuck connection (or an active MotherDuck MCP server)
Default Posture
- Explore top-down: databases, then tables/views, then columns, then statistics, then sample rows.
- Use fully qualified table names once more than one database is attached.
- Check shared databases before concluding that data is unavailable.
- 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. - Return a concise schema map with table grain, join keys, date columns, and likely measures before moving into modeling or dashboard work.
Workflow
- List databases in scope.
- List tables and views in the target database.
- Inspect columns, types, nullability, and comments before writing queries.
- Run
SUMMARIZEon important tables to understand ranges, cardinality, and null rates. - Preview rows, capture grain and join assumptions, and only then move into analytical SQL or modeling work.
Open Next
- Read
references/EXPLORATION_PLAYBOOK.mdfor the full SQL workflow, share discovery patterns, MCP tool guidance, and common exploration mistakes
Related Skills
motherduck-connectfor session setup and authenticationmotherduck-queryfor analytical SQL after the schema is understoodmotherduck-duckdb-sqlfor DuckDB syntax patterns during explorationmotherduck-share-datafor creating and consuming shares once shared datasets become part of the workflow
04 同仓库更多 Skills
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)
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.
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.
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/capabilities, defer capability loading, stream output, define agents from YAML, or test agent behavior.
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/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.
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.