mcpField
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casualize-names

casualize-names

github.com/aiagentwithdhruvMarketing & growth2040 installsUpdated 15w ago
Repository

About casualize-names

casualize-names is a Python workflow for turning formal first, company, and city names into professional, friendly forms for cold email copy. It processes Google Sheets lead rows using Claude.

Key features

  1. 01Casualizes first names, companies, and cities
  2. 02Processes Google Sheets in batches
  3. 03Overwrites existing results on request
  4. 04Adds three output columns

Use cases

  1. 01Prepare lead names for cold email copy
  2. 02Normalize names in a Google Sheet

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/aiagentwithdhruv/skills --skill casualize-names

After installing, type/casualize-namesin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

scripts/
casualize_batch.py
casualize_city_names_batch.py
casualize_company_names_batch.py
casualize_first_names_batch.py

Details

From the project README

Casualize Names Workflow

Goal

Convert formal names (first names, company names, cities) to casual, friendly versions suitable for cold email copy.

Scripts

  • ./scripts/casualize_batch.py - Main script (all 3 fields at once)
  • ./scripts/casualize_company_names_batch.py - Company names only
  • ./scripts/casualize_first_names_batch.py - First names only
  • ./scripts/casualize_city_names_batch.py - City names only

Quick Start

# Process all three fields at once (recommended, 3x faster)
python3 -u ./scripts/casualize_batch.py "GOOGLE_SHEET_URL"

# Re-process existing (overwrite)
python3 -u ./scripts/casualize_batch.py "GOOGLE_SHEET_URL" --overwrite

How It Works

  1. Processes records in batches of 50
  2. Uses 5 parallel workers
  3. Claude converts all three fields in one API call
  4. Batch updates Google Sheet with results
  5. Only processes rows with emails

Performance: ~35 records/sec (3,000 records ≈ 90 seconds)

Casualization Rules

First Names
  • Use common nicknames: "William" → "Will", "Jennifer" → "Jen"
  • Keep original if no common nickname exists
  • Keep it professional
Company Names
  • Remove "The" at beginning
  • Remove legal suffixes (LLC, Inc, Corp, Ltd)
  • Remove generic words (Realty, Group, Solutions, Services)
  • Keep core brand name
  • Use "you guys" for overly generic names

Examples:

  • "Keller Williams Realty Inc" → "Keller Williams"
  • "The Teal Umbrella Family Dental Healthcare" → "Teal Umbrella"
City Names
  • Use local nicknames: "San Francisco" → "SF", "Philadelphia" → "Philly"
  • Keep original if no common nickname

Output

Creates three new columns:

  • casual_first_name
  • casual_company_name
  • casual_city_name

Environment

ANTHROPIC_API_KEY=your_key

Schema

Inputs
NameTypeRequiredDescription
sheet_urlstringYesGoogle Sheet URL with lead data
overwritebooleanNoRe-process existing casualized names
Outputs
NameTypeDescription
sheet_urlstringSame sheet with casual_first_name, casual_company_name, casual_city_name columns added
Credentials
NameSource
ANTHROPIC_API_KEY.env
Composable With

Skills that chain well with this one: scrape-leads, gmaps-leads, instantly-campaigns

Cost

~35 records/sec, minimal API cost

Comments

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