motherduckdb/motherduck-ducklake
plugins/motherduck-skills-claude/skills/motherduck-ducklake/SKILL.md
Decide when DuckLake is the right MotherDuck storage pattern versus native MotherDuck storage (the default). Use when evaluating lakehouse or open table format storage, Iceberg-style requirements, fully managed DuckLake, BYOB buckets, own-compute DuckLake access, data inlining, time travel, object-storage layout, or file-aware compaction and maintenance.
$ install --global
skillsauthnpx skillsauth add motherduckdb/agent-skills motherduck-ducklakeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- motherduck-ducklake
- description:
- Decide when DuckLake is the right MotherDuck storage pattern versus native MotherDuck storage (the default). Use when evaluating lakehouse or open table format storage, Iceberg-style requirements, fully managed DuckLake, BYOB buckets, own-compute DuckLake access, data inlining, time travel, object-storage layout, or file-aware compaction and maintenance.
- argument-hint:
- [storage-scenario]
- license:
- MIT
Use DuckLake on MotherDuck
Use this skill when the storage decision is genuinely about open table format and object-store behavior, not just about where to put another analytical table.
Source Of Truth
- Prefer current MotherDuck DuckLake docs first.
- Use the upstream DuckLake and DuckDB extension docs only to clarify extension-level behavior that MotherDuck docs reference.
- Keep the guidance aligned with the documented product posture:
- native MotherDuck first
- upstream DuckLake v1.0 is production-ready, but MotherDuck's DuckLake docs define the MotherDuck product surface and preview/compatibility limits; verify the current DuckDB/DuckLake version matrix instead of assuming a pinned version
- fully managed, BYOB, and own-compute paths are distinct
- maintenance and compaction are explicit operations, not background magic
Default Posture
- Start with native MotherDuck storage unless there is a concrete DuckLake requirement.
- Reach for DuckLake when you need open-table-format semantics, object storage as the source of truth, BYOB, or file-aware maintenance.
- Do not recommend DuckLake just because a workload is "large"; MotherDuck's docs explicitly note native storage is often faster for reads.
- Choose the operating mode deliberately: fully managed for easiest evaluation, BYOB for customer bucket ownership, own compute only when the compute boundary matters too.
- Document the fallback to native MotherDuck storage if the DuckLake requirement is weak, unverified, or only about future portability.
- For DuckLake v1.0, data inlining, sorted tables, bucket partitioning, deletion vectors, or extension behavior, verify the current MotherDuck DuckLake docs and DuckDB/DuckLake version matrix before giving syntax guarantees.
- Do not infer MotherDuck client/runtime support from upstream DuckDB release notes alone; check the MotherDuck lifecycle docs when the exact DuckDB version matters.
- Keep the MotherDuck product surface separate from raw DuckLake-extension assumptions.
Workflow
- Confirm why native MotherDuck storage is insufficient.
- Pick the operating mode: fully managed, BYOB with MotherDuck compute, or BYOB with own compute.
- Verify regional and bucket constraints before proposing BYOB.
- Define the ingestion and maintenance posture up front, including data inlining, file compaction, and cleanup expectations.
- Validate who will query the data and from which compute surface before finalizing the architecture.
Open Next
- Read
references/DUCKLAKE_PLAYBOOK.mdfor the mode decision matrix, MotherDuck-specific SQL patterns, BYOB constraints, data-inlining behavior, maintenance functions, and common DuckLake mistakes
Related Skills
motherduck-connectfor choosing native DuckDB versus Postgres-endpoint access pathsmotherduck-load-datawhen the real issue is ingestion rather than storage formatmotherduck-model-datawhen the user still needs analytical table design after the storage decisionmotherduck-build-data-pipelinewhen DuckLake is just one part of a broader ingestion-to-serving workflow
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