GoldenFlow

Standardize, reshape, and normalize messy data — CSV, Excel, Parquet, S3, databases.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "goldenflow": {
      "command": "npx",
      "args": [
        "-y",
        "goldenflow"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Moved. This repo has moved into the benzsevern/goldenmatch monorepo at packages/python/goldenflow (and packages/typescript/goldenflow)/. This repo is archived; new development happens in the monorepo.

GoldenFlow

Data transformation toolkit — standardize, reshape, and normalize messy data before it hits your pipeline. Built by Ben Severn.

Works on files (CSV, Excel, Parquet), cloud storage (S3, GCS), or live databases. Zero-config mode auto-detects what needs fixing. One command to clean what GoldenCheck found and prep what GoldenMatch needs. Available in Python and TypeScript with full feature parity.

bash

# Python
pip install goldenflow
goldenflow transform data.csv

# TypeScript / Node.js
npm install goldenflow
npx goldenflow-js transform data.csv

The Problem

Your data arrives broken in predictable ways:

Phone numbers come in 15 different formats
Dates are mixed between MM/DD/YYYY and YYYY-MM-DD
Addresses have inconsistent abbreviations
Column names don't match between systems ("fname" vs "first_name" vs "given_name")
Values have leading whitespace, unicode garbage, smart quotes
Categoricals are inconsistent ("USA", "US", "United States")

Every data engineer writes throwaway scripts to fix these. Every script is slightly different. None of them are reusable.

GoldenFlow makes the transforms reusable, composable, and automatic.

Quick Start

Python

Terminal

pip install goldenflow

# Auto-transform (zero-config)
goldenflow transform messy_data.csv

# Try the demo first
goldenflow demo
goldenflow transform demo_data.csv -c demo_config.yaml

# With config
goldenflow learn messy_data.csv -o config.yaml
goldenflow transform messy_data.csv -c config.yaml

# Schema mapping
goldenflow map --source system_a.csv --target system_b.csv

# Full pipeline
goldencheck scan data.csv
goldenflow transform data.csv
goldenmatch dedupe data_transformed.csv

TypeScript / Node.js

Terminal

npm install goldenflow

# Auto-transform (zero-config)
npx goldenflow-js transform messy_data.csv

# Try the demo first
npx goldenflow-js demo
npx goldenflow-js transform demo_data.csv -c demo_config.yaml

# With config
npx goldenflow-js learn messy_data.csv -o config.yaml
npx goldenflow-js transform messy_data.csv -c config.yaml

# Schema mapping
npx goldenflow-js map -s system_a.csv -t system_b.csv

Programmatic (TypeScript)

server.ts

import { TransformEngine } from "goldenflow";

const result = new TransformEngine().transformDf([
  { name: "  JOHN  ", phone: "(555) 123-4567", email: "John@Example.COM" },
]);
console.log(result.rows[0]);
// { name: "JOHN", phone: "+15551234567", email: "john@example.com" }

Zero-Config Mode

bash

goldenflow transform customers.csv
# or just:
goldenflow customers.csv

GoldenFlow profiles every column and applies safe transforms automatically:

Strips whitespace and normalizes unicode
Standardizes phone numbers to E.164 format
Normalizes email casing
Parses and standardizes date formats to ISO 8601
Normalizes zip codes (zero-padding, strip +4)
Replaces smart/curly quotes with straight quotes
Auto-corrects categorical misspellings via fuzzy matching

Output: a clean CSV with a sidecar manifest showing every transform applied.

Code

customers.csv           -> customers_transformed.csv
                        -> customers_manifest.json

The manifest is an audit trail — what changed, why, and which rows were affected.

CLI Commands

GoldenFlow has 14 commands. The most common ones:

bash

# Core transforms
goldenflow transform data.csv                    # Zero-config: auto-detect and fix
goldenflow transform data.csv -c config.yaml     # Apply saved config
goldenflow transform data.csv --domain healthcare # Use a domain pack
goldenflow transform data.csv --strict           # Fail on any transform error
goldenflow transform data.csv --llm              # Enable LLM-enhanced corrections
goldenflow data.csv                              # Shorthand: auto-routes to transform

# Schema & profiling
goldenflow map -s a.csv -t b.csv                 # Auto-map schemas between files
goldenflow profile data.csv                      # Show column profiles
goldenflow learn data.csv -o config.yaml         # Generate config from data patterns
goldenflow validate data.csv                     # Dry-run: show what would change
goldenflow diff before.csv after.csv             # Compare pre/post transform

# Continuous & scheduled
goldenflow watch ./data/                         # Auto-transform new/changed files
goldenflow schedule data.csv --every 1h          # Run on a schedule (5m, 1h, 30s...)
goldenflow stream large_file.csv --chunk-size 50000  # Stream-process in batches

# Discovery & history
goldenflow init data.csv                         # Interactive setup wizard
goldenflow demo                                  # Generate sample data to try
goldenflow history                               # Show recent transform runs
goldenflow history -n 50                         # Last 50 runs

# Integrations
goldenflow interactive data.csv                  # Launch TUI
goldenflow serve                                 # REST API for real-time transforms
goldenflow mcp-serve                             # MCP server for Claude Desktop

Default Routing

Running goldenflow <file> without a subcommand auto-routes to transform:

bash

goldenflow customers.csv       # equivalent to: goldenflow transform customers.csv
goldenflow -                   # read from stdin, write to stdout

Streaming

For files too large to load into memory, use StreamProcessor or the stream command:

bash

goldenflow stream large_file.csv --chunk-size 50000

server.ts

from goldenflow.streaming import StreamProcessor

processor = StreamProcessor(config=config)

# Process a single record
result = processor.transform_one({"name": "  John  ", "phone": "(555) 123-4567"})

# Process a batch
result = processor.transform_batch(df_batch)

# Stream a large file in chunks
for result in processor.stream_file("large_data.csv", chunk_size=10_000):
    write_to_output(result.df)

print(f"Processed {processor.batches_processed} batches")

Cloud Connectors

GoldenFlow reads from and writes to S3 and Google Cloud Storage transparently:

bash

# S3
goldenflow transform s3://my-bucket/raw/customers.csv -o s3://my-bucket/clean/

# GCS
goldenflow transform gs://my-bucket/data/records.csv

server.ts

from goldenflow.connectors.s3 import read_s3, write_s3
from goldenflow.connectors.gcs import read_gcs, write_gcs

df = read_s3("s3://my-bucket/raw/customers.csv")
df = read_gcs("gs://my-bucket/data/records.csv")

Cloud paths are detected automatically — no extra flags needed.

Watch Mode

Auto-transform files as they arrive in a directory:

bash

goldenflow watch ./data/
goldenflow watch ./incoming/ -c config.yaml -o ./processed/

GoldenFlow polls the directory and applies transforms to any new or changed files.

Scheduling

Run transforms on a repeating schedule:

bash

goldenflow schedule data.csv --every 1h
goldenflow schedule data.csv --every 30m -c config.yaml -o ./output/

Supported intervals: 30s, 5m, 1h, 2h, etc.

Setup Wizard

Generate a YAML config interactively:

bash

goldenflow init data.csv

The wizard profiles your data, suggests transforms, and saves a goldenflow.yaml ready to use.

History

GoldenFlow tracks every transform run in ~/.goldenflow/history/:

bash

goldenflow history         # Last 20 runs
goldenflow history -n 50   # Last 50 runs

Each run record captures: source file, row count, transforms applied, errors, and duration.

Schema Mapping

When you need to merge data from different systems:

bash

goldenflow map --source crm_export.csv --target warehouse_schema.csv

GoldenFlow auto-maps columns between schemas using name similarity and data profiling:

Code

crm_export.csv              warehouse schema
-----                       -----
email_address      ->       email (rename)
phone_number       ->       phone (rename)
fname              ->       first_name (alias match)
st                 ->       state (alias match)

Ambiguous mappings get flagged for human review. Confident mappings apply automatically.

Domain Packs

Pre-configured transform sets for common industries. All 5 are now implemented:

Read the full README →View source on GitHub →

Related MCP Servers

View all in Workplace & Productivity View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about GoldenFlow

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "goldenflow": { "command": "npx", "args": ["-y", "GoldenFlow"] } }

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Technical Specs & Signals

CategoryWorkplace & Productivity

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 🏢 Workplace & Productivity →Best MCP servers for Workplace & Productivity →Alternatives to GoldenFlow →Install in Claude Desktop Install in Cursor Install in VS Code

GoldenFlow

Standardize, reshape, and normalize messy data — CSV, Excel, Parquet, S3, databases.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "goldenflow": {
      "command": "npx",
      "args": [
        "-y",
        "goldenflow"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

Moved. This repo has moved into the benzsevern/goldenmatch monorepo at packages/python/goldenflow (and packages/typescript/goldenflow)/. This repo is archived; new development happens in the monorepo.

GoldenFlow

Data transformation toolkit — standardize, reshape, and normalize messy data before it hits your pipeline. Built by Ben Severn.

bash

# Python
pip install goldenflow
goldenflow transform data.csv

# TypeScript / Node.js
npm install goldenflow
npx goldenflow-js transform data.csv

The Problem

Your data arrives broken in predictable ways:

Phone numbers come in 15 different formats
Dates are mixed between MM/DD/YYYY and YYYY-MM-DD
Addresses have inconsistent abbreviations
Column names don't match between systems ("fname" vs "first_name" vs "given_name")
Values have leading whitespace, unicode garbage, smart quotes
Categoricals are inconsistent ("USA", "US", "United States")

Every data engineer writes throwaway scripts to fix these. Every script is slightly different. None of them are reusable.

GoldenFlow makes the transforms reusable, composable, and automatic.

Quick Start

Python

Terminal

pip install goldenflow

# Auto-transform (zero-config)
goldenflow transform messy_data.csv

# Try the demo first
goldenflow demo
goldenflow transform demo_data.csv -c demo_config.yaml

# With config
goldenflow learn messy_data.csv -o config.yaml
goldenflow transform messy_data.csv -c config.yaml

# Schema mapping
goldenflow map --source system_a.csv --target system_b.csv

# Full pipeline
goldencheck scan data.csv
goldenflow transform data.csv
goldenmatch dedupe data_transformed.csv

TypeScript / Node.js

Terminal

npm install goldenflow

# Auto-transform (zero-config)
npx goldenflow-js transform messy_data.csv

# Try the demo first
npx goldenflow-js demo
npx goldenflow-js transform demo_data.csv -c demo_config.yaml

# With config
npx goldenflow-js learn messy_data.csv -o config.yaml
npx goldenflow-js transform messy_data.csv -c config.yaml

# Schema mapping
npx goldenflow-js map -s system_a.csv -t system_b.csv

Programmatic (TypeScript)

server.ts

import { TransformEngine } from "goldenflow";

const result = new TransformEngine().transformDf([
  { name: "  JOHN  ", phone: "(555) 123-4567", email: "John@Example.COM" },
]);
console.log(result.rows[0]);
// { name: "JOHN", phone: "+15551234567", email: "john@example.com" }

Zero-Config Mode

bash

goldenflow transform customers.csv
# or just:
goldenflow customers.csv

GoldenFlow profiles every column and applies safe transforms automatically:

Strips whitespace and normalizes unicode
Standardizes phone numbers to E.164 format
Normalizes email casing
Parses and standardizes date formats to ISO 8601
Normalizes zip codes (zero-padding, strip +4)
Replaces smart/curly quotes with straight quotes
Auto-corrects categorical misspellings via fuzzy matching

Output: a clean CSV with a sidecar manifest showing every transform applied.

Code

customers.csv           -> customers_transformed.csv
                        -> customers_manifest.json

The manifest is an audit trail — what changed, why, and which rows were affected.

CLI Commands

GoldenFlow has 14 commands. The most common ones:

bash

# Core transforms
goldenflow transform data.csv                    # Zero-config: auto-detect and fix
goldenflow transform data.csv -c config.yaml     # Apply saved config
goldenflow transform data.csv --domain healthcare # Use a domain pack
goldenflow transform data.csv --strict           # Fail on any transform error
goldenflow transform data.csv --llm              # Enable LLM-enhanced corrections
goldenflow data.csv                              # Shorthand: auto-routes to transform

# Schema & profiling
goldenflow map -s a.csv -t b.csv                 # Auto-map schemas between files
goldenflow profile data.csv                      # Show column profiles
goldenflow learn data.csv -o config.yaml         # Generate config from data patterns
goldenflow validate data.csv                     # Dry-run: show what would change
goldenflow diff before.csv after.csv             # Compare pre/post transform

# Continuous & scheduled
goldenflow watch ./data/                         # Auto-transform new/changed files
goldenflow schedule data.csv --every 1h          # Run on a schedule (5m, 1h, 30s...)
goldenflow stream large_file.csv --chunk-size 50000  # Stream-process in batches

# Discovery & history
goldenflow init data.csv                         # Interactive setup wizard
goldenflow demo                                  # Generate sample data to try
goldenflow history                               # Show recent transform runs
goldenflow history -n 50                         # Last 50 runs

# Integrations
goldenflow interactive data.csv                  # Launch TUI
goldenflow serve                                 # REST API for real-time transforms
goldenflow mcp-serve                             # MCP server for Claude Desktop

Default Routing

Running goldenflow <file> without a subcommand auto-routes to transform:

bash

goldenflow customers.csv       # equivalent to: goldenflow transform customers.csv
goldenflow -                   # read from stdin, write to stdout

Streaming

For files too large to load into memory, use StreamProcessor or the stream command:

bash

goldenflow stream large_file.csv --chunk-size 50000

server.ts

from goldenflow.streaming import StreamProcessor

processor = StreamProcessor(config=config)

# Process a single record
result = processor.transform_one({"name": "  John  ", "phone": "(555) 123-4567"})

# Process a batch
result = processor.transform_batch(df_batch)

# Stream a large file in chunks
for result in processor.stream_file("large_data.csv", chunk_size=10_000):
    write_to_output(result.df)

print(f"Processed {processor.batches_processed} batches")

Cloud Connectors

GoldenFlow reads from and writes to S3 and Google Cloud Storage transparently:

bash

# S3
goldenflow transform s3://my-bucket/raw/customers.csv -o s3://my-bucket/clean/

# GCS
goldenflow transform gs://my-bucket/data/records.csv

server.ts

from goldenflow.connectors.s3 import read_s3, write_s3
from goldenflow.connectors.gcs import read_gcs, write_gcs

df = read_s3("s3://my-bucket/raw/customers.csv")
df = read_gcs("gs://my-bucket/data/records.csv")

Cloud paths are detected automatically — no extra flags needed.

Watch Mode

Auto-transform files as they arrive in a directory:

bash

goldenflow watch ./data/
goldenflow watch ./incoming/ -c config.yaml -o ./processed/

GoldenFlow polls the directory and applies transforms to any new or changed files.

Scheduling

Run transforms on a repeating schedule:

bash

goldenflow schedule data.csv --every 1h
goldenflow schedule data.csv --every 30m -c config.yaml -o ./output/

Supported intervals: 30s, 5m, 1h, 2h, etc.

Setup Wizard

Generate a YAML config interactively:

bash

goldenflow init data.csv

The wizard profiles your data, suggests transforms, and saves a goldenflow.yaml ready to use.

History

GoldenFlow tracks every transform run in ~/.goldenflow/history/:

bash

goldenflow history         # Last 20 runs
goldenflow history -n 50   # Last 50 runs

Each run record captures: source file, row count, transforms applied, errors, and duration.

Schema Mapping

When you need to merge data from different systems:

bash

goldenflow map --source crm_export.csv --target warehouse_schema.csv

GoldenFlow auto-maps columns between schemas using name similarity and data profiling:

Code

crm_export.csv              warehouse schema
-----                       -----
email_address      ->       email (rename)
phone_number       ->       phone (rename)
fname              ->       first_name (alias match)
st                 ->       state (alias match)

Ambiguous mappings get flagged for human review. Confident mappings apply automatically.

Domain Packs

Pre-configured transform sets for common industries. All 5 are now implemented:

Read the full README →View source on GitHub →

Related MCP Servers

View all in Workplace & Productivity View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about GoldenFlow

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "goldenflow": { "command": "npx", "args": ["-y", "GoldenFlow"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/goldenflow?style=directory)](https://allmcps.com/mcp/goldenflow)

HTML Embed

<a href="https://allmcps.com/mcp/goldenflow"><img src="https://allmcps.com/api/badge/goldenflow?style=directory" alt="GoldenFlow on AllMCPs" /></a>

Technical Specs & Signals

CategoryWorkplace & Productivity

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Featured

AllMCPs Server

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🏢 Workplace & Productivity →Best MCP servers for Workplace & Productivity →Alternatives to GoldenFlow →Install in Claude Desktop Install in Cursor Install in VS Code