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ducklakeducklake 命令行

Agent Skill

ducklake 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

288

周安装

12

GitHub Stars

公开资料未说明

下载量

96
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ducklake(ducklake 命令行)
来源仓库:https://github.com/brojonat/llmsrules
仓库路径:skills/ducklake
安装命令:
npx skills add https://github.com/brojonat/llmsrules --skill ducklake
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/brojonat/llmsrules --skill ducklake

简介

实现基于 PostgreSQL、DuckDB 或 SQLite 的开放湖仓格式管理。

  • 支持不可变快照、时间旅行查询和变更溯源等高级数据治理能力。
  • 通过 SQL 命令挂载本地或云存储的数据集,适用于构建统一数据平台。
  • 需配置元数据存储后端(如 PostgreSQL)和 Parquet 文件路径,确保读写权限正确。
  • ducklake 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

DuckLake

DuckLake is an open lakehouse format with a SQL catalog database (PostgreSQL, DuckDB, SQLite) and Parquet data files on any storage backend (local, S3, R2, GCS, Azure).

Every mutation creates an immutable snapshot, enabling time travel, change feeds, and conflict resolution.

Installation & Connection

INSTALL ducklake;
LOAD ducklake;

-- Local DuckDB catalog + local files
ATTACH 'ducklake:metadata.ducklake' AS my_lake;

-- PostgreSQL catalog + S3 storage
ATTACH 'ducklake:postgres:dbname=ducklake host=myhost' AS my_lake
    (DATA_PATH 's3://my-bucket/data/');

-- Read-only / at specific snapshot
ATTACH 'ducklake:metadata.ducklake' AS my_lake (READ_ONLY);
ATTACH 'ducklake:metadata.ducklake' AS my_lake (SNAPSHOT_VERSION 3);
ATTACH 'ducklake:metadata.ducklake' AS my_lake (SNAPSHOT_TIME '2025-05-26 00:00:00');

Core Operations

-- DDL
CREATE SCHEMA my_schema;
CREATE TABLE my_schema.tbl (id INTEGER NOT NULL, name VARCHAR, ts TIMESTAMP);

-- DML
INSERT INTO tbl VALUES (1, 'alice', now());
UPDATE tbl SET name = 'bob' WHERE id = 1;
DELETE FROM tbl WHERE id = 1;

-- Upsert via MERGE INTO (no primary keys in DuckLake)
MERGE INTO target USING source
    ON target.id = source.id
    WHEN MATCHED THEN UPDATE SET name = source.name
    WHEN NOT MATCHED THEN INSERT VALUES (source.id, source.name);

Schema Evolution

All changes are metadata-only (no file rewrites):

ALTER TABLE tbl ADD COLUMN new_col INTEGER;
ALTER TABLE tbl ADD COLUMN new_col VARCHAR DEFAULT 'hello';
ALTER TABLE tbl DROP COLUMN old_col;
ALTER TABLE tbl RENAME old_col TO new_name;
ALTER TABLE tbl ALTER col SET TYPE BIGINT;  -- lossless promotions only

Valid type promotions: int8->int16/32/64, int16->int32/64, int32->int64, float32->float64.

Snapshots & Time Travel

-- List / inspect snapshots
SELECT * FROM my_lake.snapshots();
FROM my_lake.current_snapshot();

-- Add metadata to a snapshot
BEGIN;
INSERT INTO tbl VALUES (1, 'data');
CALL my_lake.set_commit_message('author', 'Description', extra_info => '{"key": "value"}');
COMMIT;

-- Time travel
SELECT * FROM tbl AT (VERSION => 3);
SELECT * FROM tbl AT (TIMESTAMP => now() - INTERVAL '1 week');

-- Change feed between snapshots
FROM my_lake.table_changes('tbl', 2, 5);
FROM my_lake.table_changes('tbl', now() - INTERVAL '1 week', now());

Partitioning

ALTER TABLE tbl SET PARTITIONED BY (region);
ALTER TABLE tbl SET PARTITIONED BY (year(ts), month(ts));
ALTER TABLE tbl RESET PARTITIONED BY;

Functions: identity, year(), month(), day(), hour(). Only affects new data.

Maintenance

-- All-in-one
CHECKPOINT;

-- Individual operations
CALL ducklake_merge_adjacent_files('my_lake');
CALL ducklake_expire_snapshots('my_lake', older_than => now() - INTERVAL '1 week');
CALL ducklake_cleanup_old_files('my_lake', older_than => now() - INTERVAL '1 week');
CALL ducklake_delete_orphaned_files('my_lake', older_than => now() - INTERVAL '1 week');
CALL ducklake_rewrite_data_files('my_lake', 'tbl', delete_threshold => 0.5);

Configuration

-- Persistent settings (stored in catalog)
CALL my_lake.set_option('parquet_compression', 'zstd');
CALL my_lake.set_option('target_file_size', '256MB', table_name => 'big_table');

Key settings: parquet_compression (snappy/zstd/gzip), target_file_size (512MB), data_inlining_row_limit (0), encrypted (false), require_commit_message (false).

Key Differences from Plain DuckDB

  • No indexes, primary keys, foreign keys, unique constraints, or check constraints
  • MERGE INTO instead of INSERT... ON CONFLICT
  • Every transaction creates a snapshot
  • Schema changes are metadata-only
  • Deletes use merge-on-read (delete files, not in-place mutation)
  • Updates = DELETE + INSERT in one transaction
  • Only NOT NULL constraint is supported

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.88%
按下载量换算37

Claude

30.39%
按下载量换算29

Cursor

18.14%
按下载量换算17

Gemini CLI

10.07%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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