drt-hub

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drt — data reverse tool

Reverse ETL for the code-first data stack.

drt syncs data from your data warehouse to external services — declaratively, via YAML and CLI. Think dbt run → drt run. Same developer experience, opposite data direction.

pip install drt-core          # core (DuckDB included)
drt init && drt run

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Why drt?

Problem	drt's answer
Census/Hightouch are expensive SaaS	Free, self-hosted OSS
GUI-first tools don't fit CI/CD	CLI + YAML, Git-native
dbt/dlt ecosystem has no reverse leg	Same philosophy, same DX
LLM/MCP era makes GUI SaaS overkill	LLM-native by design

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Quickstart

No cloud accounts needed — runs locally with DuckDB in about 5 minutes.

1. Install

pip install drt-core

For cloud sources: pip install drt-core[bigquery], drt-core[postgres], etc.

2. Set up a project

mkdir my-drt-project && cd my-drt-project
drt init   # select "duckdb" as source

3. Create sample data

python -c "
import duckdb
c = duckdb.connect('warehouse.duckdb')
c.execute('''CREATE TABLE IF NOT EXISTS users AS SELECT * FROM (VALUES
  (1, 'Alice', 'alice@example.com'),
  (2, 'Bob',   'bob@example.com'),
  (3, 'Carol', 'carol@example.com')
) t(id, name, email)''')
c.close()
"

4. Create a sync

# syncs/post_users.yml
name: post_users
description: "POST user records to an API"
model: ref('users')
destination:
  type: rest_api
  url: "https://httpbin.org/post"
  method: POST
  headers:
    Content-Type: "application/json"
  body_template: |
    { "id": {{ row.id }}, "name": "{{ row.name }}", "email": "{{ row.email }}" }
sync:
  mode: full
  batch_size: 1
  on_error: fail

5. Run

drt run --dry-run   # preview, no data sent
drt run             # run for real
drt status          # check results

See examples/ for more: Slack, Google Sheets, HubSpot, GitHub Actions, etc.

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CLI Reference

drt init                    # initialize project
drt list                    # list sync definitions
drt run                     # run all syncs
drt run --select <name>     # run a specific sync
drt run --dry-run           # dry run
drt run --verbose           # show row-level error details
drt validate                # validate sync YAML configs
drt status                  # show recent sync status
drt status --verbose        # show per-row error details
drt mcp run                 # start MCP server (requires drt-core[mcp])

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MCP Server

Connect drt to Claude, Cursor, or any MCP-compatible client so you can run syncs, check status, and validate configs without leaving your AI environment.

pip install drt-core[mcp]
drt mcp run

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "drt": {
      "command": "drt",
      "args": ["mcp", "run"]
    }
  }
}

Available MCP tools:

Tool	What it does
drt_list_syncs	List all sync definitions
drt_run_sync	Run a sync (supports dry_run)
drt_get_status	Get last run result(s)
drt_validate	Validate sync YAML configs
drt_get_schema	Return JSON Schema for config files

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AI Skills for Claude Code

Install the official Claude Code skills to generate YAML, debug failures, and migrate from other tools — all from the chat interface.

Install via Plugin Marketplace (recommended)

/plugin marketplace add drt-hub/drt
/plugin install drt@drt-hub

Tip: Enable auto-update so you always get the latest skills when drt is updated: /plugin → Marketplaces → drt-hub → Enable auto-update

Manual install (slash commands)

Copy the files from .claude/commands/ into your drt project's .claude/commands/ directory.