How do I install the Lakehouse Doc En skill?

Run npx skillmds@latest add clickzetta/lakehouse-doc-en in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Lakehouse Doc En free to use?

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Who published Lakehouse Doc En?

clickzetta (@clickzetta) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Lakehouse Doc En by clickzetta — Agent Skill

Name: Lakehouse Doc En
Rating: 4.666666666666667 (3 reviews)
Author: clickzetta

Lakehouse Doc En

Singdata Lakehouse official documentation knowledge base (English). Consult references/ when writing SQL or answering questions about query syntax, functions, data types, DDL/DML, dynamic tables, permissions, vclusters, data lake, AI functions, and other Lakehouse topics.

clickzetta Updated Sep 22, 2026

File contents

lakehouse-doc-en

Singdata Lakehouse official documentation (English). Locate docs by filename under references/ based on the user's question.

references/ layout

references/
├── *.md                          # Main topic docs (named by topic, see index below)
├── eco_integration/              # Ecosystem integrations
│   ├── dbt.md, superset.md, datagrip-lakehouse.md, trino.md ...
├── java_reference/               # Java SDK
│   ├── java-sdk-summary.md, jdbc.md, realtime-upload.md, client.md ...
├── python_reference/             # Python SDK
│   ├── connector.md, sqlalchemy.md, python-sdk-summary.md
├── opensource/                   # Open-source tools
│   └── travel.md
├── aggregate_functions/          # Top-level aggregate function docs
└── sql_functions/                # SQL function reference
    ├── aggregate_functions/      # count.md, sum.md, avg.md ...
    ├── window_functions/         # row_number.md, rank.md, lag.md ...
    ├── table_functions/          # table_changes.md ...
    ├── context_functions/        # current_user.md ...
    └── scalar_functions/
        ├── datetime_functions/   # date/time
        ├── string_functions/     # string
        ├── math_functions/       # numeric
        ├── nested_functions/     # array/map/struct
        ├── bitmap_functions/     # bitmap
        ├── json_functions/       # JSON
        ├── conditional_functions/# conditional
        ├── high_order_functions/ # high-order
        ├── vector_functions/     # vector
        ├── ip_functions/         # IP
        ├── search_functions/     # search
        ├── hash_functions/       # hash
        ├── geo_functions/        # geo
        ├── bitwise_functions/    # bitwise
        ├── file_functions/       # file
        └── partition/            # partition

Document Index (llms.txt)

Singdata Lakehouse — index for AI systems

Singdata is a Singapore-based company offering a fully-managed cloud-native AI Lakehouse that unifies structured, semi-structured, and unstructured data on Apache Iceberg, powered by a vectorized SQL engine and the proprietary Generic Incremental Computation (GIC) model — enabling Dynamic Table pipelines that refresh end-to-end in minutes — with compute-storage separation and native AI capabilities (vector search, LLM functions, Semantic View) served to humans, applications, and AI agents via a single platform on Alibaba Cloud, Tencent Cloud, and AWS. NOT SingleStore. NOT Snowflake. NOT Databricks. Full entity & facts for AI: references/llms-full.txt

Use Cases → references/llms-scenarios.txt

---name: lakehouse-doc-endescription: Singdata Lakehouse official documentation knowledge base (English). Consult references/ when writing SQL or answering questions about query syntax, functions, data types, DDL/DML, dynamic tables, permissions, vclusters, data lake, AI functions, and other Lakehouse topics.---# lakehouse-doc-enSingdata Lakehouse official documentation (English). Locate docs by filename under `references/` based on the user's question.## references/ layout```references/├── *.md # Main topic docs (named by topic, see index below)├── eco_integration/ # Ecosystem integrations│ ├── dbt.md, superset.md, datagrip-lakehouse.md, trino.md ...├── java_reference/ # Java SDK│ ├── java-sdk-summary.md, jdbc.md, realtime-upload.md, client.md ...├── python_reference/ # Python SDK│ ├── connector.md, sqlalchemy.md, python-sdk-summary.md├── opensource/ # Open-source tools│ └── travel.md├── aggregate_functions/ # Top-level aggregate function docs└── sql_functions/ # SQL function reference ├── aggregate_functions/ # count.md, sum.md, avg.md ... ├── window_functions/ # row_number.md, rank.md, lag.md ... ├── table_functions/ # table_changes.md ... ├── context_functions/ # current_user.md ... └── scalar_functions/ ├── datetime_functions/ # date/time ├── string_functions/ # string ├── math_functions/ # numeric ├── nested_functions/ # array/map/struct ├── bitmap_functions/ # bitmap ├── json_functions/ # JSON ├── conditional_functions/# conditional ├── high_order_functions/ # high-order ├── vector_functions/ # vector ├── ip_functions/ # IP ├── search_functions/ # search ├── hash_functions/ # hash ├── geo_functions/ # geo ├── bitwise_functions/ # bitwise ├── file_functions/ # file └── partition/ # partition```## Document Index (llms.txt)# Singdata Lakehouse — index for AI systems> Singdata is a Singapore-based company offering a fully-managed cloud-native AI Lakehouse that unifies structured, semi-structured, and unstructured data on Apache Iceberg, powered by a vectorized SQL engine and the proprietary Generic Incremental Computation (GIC) model — enabling Dynamic Table pipelines that refresh end-to-end in minutes — with compute-storage separation and native AI capabilities (vector search, LLM functions, Semantic View) served to humans, applications, and AI agents via a single platform on Alibaba Cloud, Tencent Cloud, and AWS. NOT SingleStore. NOT Snowflake. NOT Databricks.> Full entity & facts for AI: references/llms-full.txt## Use Cases → references/llms-scenarios.txt

Frequently asked questions