aiagentwithdhruv

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casualize-names

Convert formal names to casual versions for cold email personalization - first names, company names, and city names. Use when user asks to casualize names, make names friendly, or prepare lead data for emails.

Examiner la sourceVoir sur GitHub
Prix non confirmé★ 16 Stars GitHubRegistre mis à jour · 1 sept. 2026agent-skill

Vue d’ensemble

Convert formal names to casual versions for cold email personalization - first names, company names, and city names. Use when user asks to casualize names, make names friendly, or prepare lead data for emails.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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

Métadonnées du fichier
name: casualize-names
description: Convert formal names to casual versions for cold email personalization - first names, company names, and city names. Use when user asks to casualize names, make names friendly, or prepare lead data for emails.
Voir le texte original
---
name: casualize-names
description: Convert formal names to casual versions for cold email personalization - first names, company names, and city names. Use when user asks to casualize names, make names friendly, or prepare lead data for emails.
---

# 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

```bash
# 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
| Name | Type | Required | Description |
|------|------|----------|-------------|
| `sheet_url` | string | Yes | Google Sheet URL with lead data |
| `overwrite` | boolean | No | Re-process existing casualized names |

### Outputs
| Name | Type | Description |
|------|------|-------------|
| `sheet_url` | string | Same sheet with casual_first_name, casual_company_name, casual_city_name columns added |

### Credentials
| Name | Source |
|------|--------|
| `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

Examiner la source

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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • SKILL.md contains a minor typo: 'overly generic name' should likely be 'overly generic names'.
  • The scripts are not fully shown in the excerpt, but the provided code appears functional and follows standard practices.
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 16 GitHub stars
  • Stars/forks activity: 16 stars, 6 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Ouvrir l’audit complet

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Commencer par une petite tâche

  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
aiagentwithdhruv/skills
Licence
MIT
Version
1.0.0
Dernier push GitHub
27 août 2026
Registre mis à jour
1 sept. 2026
Chemin des instructions
casualize-names/SKILL.md

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

56/100

Prometteur

Confiance

51/100

Do not auto-install

Audit

68/100

Revue nécessaire

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • SKILL.md contains a minor typo: 'overly generic name' should likely be 'overly generic names'.
  • The scripts are not fully shown in the excerpt, but the provided code appears functional and follows standard practices.
  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 16 GitHub stars
  • Stars/forks activity: 16 stars, 6 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Plus de détails
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