Goldenflow

> 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.

[](https://pypi.org/project/goldenflow/) [](https://www.npmjs.com/package/goldenflow) [](https://github.com/benzsevern/goldenflow/actions/workflows/test.yml) [](https://codecov.io/gh/benzsevern/goldenflow) [](https://pepy.tech/project/goldenflow) [](https://python.org) [](https://nodejs.org) [](LICENSE) [](https://benzsevern.github.io/goldenflow/) [](https://github.com/benzsevern/dqbench) [](https://colab.research.google.com/github/benzsevern/goldenflow/blob/main/scripts/goldenflow_demo.ipynb)

# 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 "firstname" vs "givenname")
• 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

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

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)

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

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.

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:

# 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 without a subcommand auto-routes to transform:

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:

goldenflow stream large_file.csv --chunk-size 50000

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:

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

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

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:

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:

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:

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/:

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:

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

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

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:

goldenflow transform patients.csv --domain healthcare
goldenflow transform employees.csv --domain people_hr
goldenflow transform transactions.csv --domain finance
goldenflow transform orders.csv --domain ecommerce
goldenflow transform listings.csv --domain real_estate

| Domain Pack | What It Covers | |-------------|---------------| | People/HR | Name parsing, SSN formatting, employment dates, gender/boolean standardization | | Healthcare | Patient IDs, diagnosis codes, clinical dates, HIPAA-sensitive field handling | | Finance | Currency normalization, account numbers, transaction dates, amount parsing | | E-commerce | SKU normalization, price parsing, order dates, address standardization | | Real Estate | Property addresses, listing dates, price normalization, geo fields |

---

Transform Library (76 transforms)

Text Transforms (18)

| Transform | What It Does | |-----------|-------------| | strip | Trim whitespace | | lowercase / uppercase | Case conversion | | title_case | Proper casing ("john smith" -> "John Smith") | | normalize_unicode | NFKD normalization, strip accents | | normalize_quotes | Smart/curly quotes -> straight quotes | | collapse_whitespace | Multiple spaces -> single space | | truncate:N | Limit to N characters | | remove_punctuation | Strip punctuation characters | | remove_html_tags | Strip HTML markup from scraped data | | remove_urls | Strip URLs from free-text fields | | remove_digits | Strip numeric characters from text | | remove_emojis | Strip emoji characters | | fix_mojibake | Fix common UTF-8/Latin-1 encoding garbling | | normalize_line_endings | Normalize \r\n and \r to \n | | extract_numbers | Pull numeric values from mixed text | | pad_left:N / pad_right:N | Pad to fixed width (account numbers, IDs) |

Phone Transforms (5)

| Transform | What It Does | |-----------|-------------| | phone_e164 | Any format -> +15550123456 | | phone_national | Any format -> (555) 012-3456 | | phone_digits | Strip to digits only | | phone_validate | Flag invalid numbers | | phone_country_code | Extract country calling code |

Name Transforms (8)

| Transform | What It Does | |-----------|-------------| | split_name | "John Smith" -> first: "John", last: "Smith" | | split_name_reverse | "Smith, John" -> first: "John", last: "Smith" | | strip_titles / strip_suffixes | Remove Mr., Mrs., Dr., MD, PhD, etc. | | name_proper | "mcdonald" -> "McDonald", "o'brien" -> "O'Brien" | | initial_expand | Flag names with initials for review | | nickname_standardize | "Bob" -> "Robert", "Bill" -> "William" | | merge_name | Combine firstname + lastname into full_name |

Address Transforms (8)

| Transform | What It Does | |-----------|-------------| | address_standardize | "Street" -> "St", "Avenue" -> "Ave" | | address_expand | "St" -> "Street", "Ave" -> "Avenue" | | state_abbreviate / state_expand | "Pennsylvania" "PA" | | zip_normalize | Zero-pad, strip +4, validate | | split_address | Single line -> street, city, state, zip | | country_standardize | "United States" / "USA" -> "US" (ISO 3166) | | unit_normalize | "Apt" / "Apartment" / "#" -> "Unit" |

Date Transforms (13)

| Transform | What It Does | |-----------|-------------| | date_iso8601 | Any format -> 2024-03-15 | | datetime_iso8601 | Any format -> 2024-03-15T15:30:00 | | date_us / date_eu | Regional format output | | date_parse | Auto-detect and normalize to ISO 8601 | | age_from_dob | Date of birth -> age in years | | extract_year / extract_month / extract_day | Decompose dates | | extract_quarter | Date -> Q1/Q2/Q3/Q4 | | extract_day_of_week | Date -> Monday, Tuesday, etc. | | date_shift | Add/subtract days (anonymization) | | date_validate | Flag invalid dates as boolean |

Categorical Transforms (6)

| Transform | What It Does | |-----------|-------------| | category_auto_correct | Fuzzy-match misspellings to canonical values | | category_standardize | Map variants to canonical values | | category_from_file | Load mapping from CSV/YAML file | | boolean_normalize | "Yes"/"Y"/"1"/"True" -> true | | gender_standardize | "Male"/"M"/"Female"/"F" -> M/F | | null_standardize | "N/A"/"NULL"/"none" -> null |

Numeric Transforms (9)

| Transform | What It Does | |-----------|-------------| | currency_strip | "$1,234.56" -> 1234.56 | | percentage_normalize | "85%" -> 0.85 | | round:N | Round to N decimal places | | clamp | Constrain values to a min/max range | | to_integer | Parse string to int, truncating decimals | | abs_value | Absolute value | | fill_zero | Replace nulls with 0 | | comma_decimal | European format "1.234,56" -> 1234.56 | | scientific_to_decimal | "1.5e3" -> 1500.0 |

Email Transforms (4)

| Transform | What It Does | |-----------|-------------| | email_lowercase | Normalize to lowercase | | email_normalize | Strip +tags, strip Gmail dots, lowercase | | email_extract_domain | user@example.com -> example.com | | email_validate | Flag invalid email format |

Identifier Transforms (3)

| Transform | What It Does | |-----------|-------------| | ssn_format | Normalize to XXX-XX-XXXX | | ssn_mask | Redact to \\\-\\*-1234 | | ein_format | Normalize to XX-XXXXXXX |

URL Transforms (2)

| Transform | What It Does | |-----------|-------------| | url_normalize | Lowercase domain, ensure scheme, strip trailing slash | | url_extract_domain | Extract domain from URL |

---

Special Modes

Strict Mode

Fail immediately if any transform error occurs — useful in CI or production pipelines:

goldenflow transform data.csv --strict

Exits with code 1 and prints the first 5 errors if any transform fails.

LLM Mode

Use an LLM to enhance categorical corrections and handle edge cases that fuzzy matching misses:

goldenflow transform data.csv --llm

Requires OPENAI_API_KEY or ANTHROPIC_API_KEY in your environment. Falls back to standard transforms gracefully.

Auto-Correct

category_auto_correct uses fuzzy matching to fix misspelled categorical values automatically. It is suppressed on high-cardinality columns (>10% unique values) to avoid false positives.

"actve" -> "active"
"Pennsylvnia" -> "Pennsylvania"
"Unted States" -> "United States"

---

YAML Config

For repeatable pipelines:

# goldenflow.yaml
source: customers.csv
output: customers_clean.csv

transforms:
  - column: name
    ops: [strip, title_case]
  - column: email
    ops: [lowercase, strip]
  - column: phone
    ops: [phone_e164]
  - column: state
    ops: [state_abbreviate]
  - column: signup_date
    ops: [date_iso8601]

renames:
  email_address: email
  phone_number: phone

drop: [internal_id, temp_notes]

dedup:
  columns: [email]
  keep: first

goldenflow transform customers.csv -c goldenflow.yaml

Generate

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: benseverndev-oss
• Source: benseverndev-oss/goldenflow
• License: MIT
• Homepage: https://github.com/benseverndev-oss/goldenmatch

Install and usage instructions live in the source repository linked above.