{"slug":"johnnyq-commits-aqueduct","name":"Aqueduct","description":"LLM-driven data engineering agent that automates SQL generation and ETL workflows from natural language requirements.","long_description":"# Aqueduct\n\n**Data Engineering Automation Agent Framework**\n\n[![CI](https://github.com/JohnnyQ-commits/aqueduct/actions/workflows/ci.yml/badge.svg)](https://github.com/JohnnyQ-commits/aqueduct/actions/workflows/ci.yml)\n![Python](https://img.shields.io/badge/python-3.10%2B-blue)\n![License](https://img.shields.io/badge/license-MIT-green)\n![Status](https://img.shields.io/badge/status-alpha-orange)\n\n[English](#overview) | [中文](#overview-zh)\n\n---\n\n## Overview\n\n**The problem**: Data engineers spend 60-70% of their time on repetitive work — understanding requirements, writing boilerplate SQL, creating DDL, writing DQC tests, generating documentation. The creative part (business logic) is small; the mechanical part is huge.\n\n**Aqueduct automates the mechanical part.**\n\nGive it a requirement document. Get back 11 standardized deliverables: DDL, ETL SQL, DQC test cases, field-level lineage, design documents, and a comprehensive report.\n\n```text\nRequirement (.md) --> Design (.md) --> DDL (.sql) --> SQL (.sql) --> Review --> DQC (.sql) --> Report (.md) --> Deliverables (11 files)\n```\n\n### What makes it different\n\n| Principle | What it means |\n|-----------|---------------|\n| **Framework, not tool** | Embed into Claude Code, LangChain, or your own app via `from aqueduct import Aqueduct` |\n| **7-layer architecture** | Clean separation: MCP / LLM / Tools / Skills / Engine / Memory / Config |\n| **Platform agnostic** | Connects to any data platform via standard MCP protocol (`.mcp.json`) |\n| **Ontology knowledge** | Business domains modeled as typed JSON — entities, relationships, metrics, axioms |\n| **DAG orchestration** | StateGraph-based workflow with interactive checkpoints and error recovery |\n| **Review-fix loop** | Code review finds bugs → auto-fix SQL → re-review → production-ready quality |\n| **Full observability** | Per-task log files, phase timing, LLM call tracing, tool execution audit |\n| **Prompt-code decoupled** | Prompts as `.tpl.md` files — edit without touching code","tagline":"LLM-powered data engineering agent: converts requirement docs and natural language into production-ready SQL, automating data pipeline workflows for ETL and analytics tasks. Open-source, MIT licensed.可嵌入的 LLM 驱动数据开发 Agent：将需求文档和自然语言转换为生产级 SQL，自动化 ETL 和分析任务的数据流水线工作流。开源项目，采用 MIT 协议。","category":"data","tags":["data-engineering","sql-generation","etl","data-pipeline","llm-agent"],"author":"JohnnyQ-commits","verified":false,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"OpenAgentSkill auto-indexer","sourceDetail":"JohnnyQ-commits/Aqueduct","creatorName":"JohnnyQ-commits","creatorUrl":"https://github.com/JohnnyQ-commits","sourceUrl":"https://github.com/JohnnyQ-commits/Aqueduct","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/johnnyq-commits-aqueduct#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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