Back to Skills

delete-no-email-contacts

TomGranot
Updated 2 days ago
6 views
33
11
33
View on GitHub
Documentationaiapiautomation

About

This skill automatically identifies and deletes HubSpot contacts that lack an email address, helping reduce billed contact counts and maintain CRM hygiene. It uses HubSpot's Search and Batch Archive APIs for full automation. Developers should implement this for regular database cleanup when non-communicable contacts accumulate.

Quick Install

Claude Code

Recommended
Primary
npx skills add TomGranot/hubspot-admin-skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/TomGranot/hubspot-admin-skills
Git CloneAlternative
git clone https://github.com/TomGranot/hubspot-admin-skills.git ~/.claude/skills/delete-no-email-contacts

Copy and paste this command in Claude Code to install this skill

Documentation

Delete Contacts With No Email Address

Purpose

Contacts without an email address serve no functional purpose in a HubSpot Marketing Hub instance. They cannot receive marketing emails, sales sequences, or transactional messages. They inflate the billed contact count. This skill identifies and deletes them via the API.

Prerequisites

  • A HubSpot private app access token with crm.objects.contacts.read and crm.objects.contacts.write scopes
  • Python 3.10+ with uv for package management
  • A .env file containing HUBSPOT_ACCESS_TOKEN

Execution Pattern

This skill follows a 4-stage execution pattern: Plan -> Before State -> Execute -> After State.

Stage 1: Plan

Before writing any code, confirm these items with the user:

  1. Root cause: Ask whether any integrations (CRM sync, form tool, import process) are intentionally creating contacts without email. If so, fix the inflow first.
  2. Threshold: The default safety abort threshold is 500 contacts. If the user expects more, adjust the threshold in the execute script.
  3. Recovery window: Confirm the user understands that deleted contacts are recoverable for 90 days via HubSpot Settings > Data Management > Deleted Objects.

Stage 2: Before State

Run a count query to establish the baseline. Save results for comparison.

"""
Before State: Count contacts with no email address.
"""
import os
import json
import requests
from dotenv import load_dotenv

load_dotenv()

TOKEN = os.environ["HUBSPOT_ACCESS_TOKEN"]
BASE = "https://api.hubapi.com"
headers = {
    "Authorization": f"Bearer {TOKEN}",
    "Content-Type": "application/json",
}

search_payload = {
    "filterGroups": [
        {
            "filters": [
                {
                    "propertyName": "email",
                    "operator": "NOT_HAS_PROPERTY",
                }
            ]
        }
    ],
    "properties": ["firstname", "lastname", "createdate", "hs_object_id"],
    "limit": 1,  # Only need the total count
}

url = f"{BASE}/crm/v3/objects/contacts/search"
response = requests.post(url, headers=headers, json=search_payload)
response.raise_for_status()

data = response.json()
total = data.get("total", 0)

print(f"BEFORE STATE: {total} contacts exist with no email address.")

if total > 0 and data.get("results"):
    sample = data["results"][0]
    props = sample.get("properties", {})
    print(f"  Sample: ID {sample['id']}, "
          f"{props.get('firstname', '(empty)')} {props.get('lastname', '(empty)')}, "
          f"created {props.get('createdate', '(unknown)')}")

Expected output: A count of contacts with no email and a sample record for sanity checking.

Present findings to the user before proceeding. Ask for explicit confirmation to continue.

Stage 3: Execute

Collect all contact IDs via paginated search, export a CSV audit trail, then batch-delete.

"""
Execute: Delete all contacts with no email address.
Steps:
  1. Paginated search to collect all contact IDs
  2. Export CSV audit log before deletion
  3. Batch archive in groups of 100
"""
import os
import csv
import time
import requests
from dotenv import load_dotenv

load_dotenv()

TOKEN = os.environ["HUBSPOT_ACCESS_TOKEN"]
BASE = "https://api.hubapi.com"
headers = {
    "Authorization": f"Bearer {TOKEN}",
    "Content-Type": "application/json",
}

# --- Step 1: Collect all contact IDs ---
all_contacts = []
after = None

search_payload = {
    "filterGroups": [
        {
            "filters": [
                {
                    "propertyName": "email",
                    "operator": "NOT_HAS_PROPERTY",
                }
            ]
        }
    ],
    "properties": ["firstname", "lastname", "createdate", "hs_object_id"],
    "limit": 100,
}

while True:
    payload = search_payload.copy()
    if after:
        payload["after"] = after

    resp = requests.post(
        f"{BASE}/crm/v3/objects/contacts/search",
        headers=headers, json=payload,
    )
    resp.raise_for_status()
    data = resp.json()

    for contact in data.get("results", []):
        props = contact.get("properties", {})
        all_contacts.append({
            "id": contact["id"],
            "firstname": props.get("firstname", ""),
            "lastname": props.get("lastname", ""),
            "createdate": props.get("createdate", ""),
        })

    paging = data.get("paging", {})
    after = paging.get("next", {}).get("after")
    if not after:
        break
    time.sleep(0.2)  # Rate limiting

print(f"Total contacts to delete: {len(all_contacts)}")

# --- Step 2: SAFETY CHECK ---
ABORT_THRESHOLD = 500
if len(all_contacts) > ABORT_THRESHOLD:
    print(f"SAFETY ABORT: Found {len(all_contacts)} contacts, "
          f"exceeds threshold of {ABORT_THRESHOLD}.")
    print("Review the data and adjust the threshold if this is expected.")
    exit(1)

# --- Step 3: Export CSV audit trail ---
os.makedirs("data/audit-logs", exist_ok=True)
csv_path = "data/audit-logs/deleted-no-email-contacts.csv"

with open(csv_path, "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["id", "firstname", "lastname", "createdate"])
    writer.writeheader()
    writer.writerows(all_contacts)

print(f"Audit log saved: {csv_path} ({len(all_contacts)} records)")

# --- Step 4: Batch delete ---
all_ids = [c["id"] for c in all_contacts]
BATCH_SIZE = 100
deleted_count = 0
failed_ids = []

for i in range(0, len(all_ids), BATCH_SIZE):
    batch = all_ids[i : i + BATCH_SIZE]
    delete_payload = {"inputs": [{"id": cid} for cid in batch]}

    resp = requests.post(
        f"{BASE}/crm/v3/objects/contacts/batch/archive",
        headers=headers, json=delete_payload,
    )

    if resp.status_code == 204:
        deleted_count += len(batch)
        print(f"  Batch {i // BATCH_SIZE + 1}: deleted {len(batch)} contacts")
    else:
        failed_ids.extend(batch)
        print(f"  Batch FAILED: {resp.status_code} — {resp.text[:200]}")

    time.sleep(0.5)  # Rate limiting between batches

print(f"\nDeleted: {deleted_count}, Failed: {len(failed_ids)}")

Key API details:

  • POST /crm/v3/objects/contacts/search with NOT_HAS_PROPERTY filter on email
  • Paginate with after cursor, 100 results per page
  • POST /crm/v3/objects/contacts/batch/archive accepts up to 100 IDs per call
  • Successful archive returns HTTP 204 (no content)

Stage 4: After State

Re-run the before-state query to confirm zero contacts remain.

"""
After State: Verify no contacts with missing email remain.
"""
# (Same search payload as Before State)
response = requests.post(url, headers=headers, json=search_payload)
response.raise_for_status()
total = response.json().get("total", 0)

if total == 0:
    print("SUCCESS: 0 contacts with no email remain.")
else:
    print(f"WARNING: {total} contacts with no email still exist.")
    print("New contacts may have been created since deletion. Investigate.")

Present results to the user. If new contacts appeared, investigate the source (form submissions, integrations, imports).

Safety Mechanisms

MechanismDetail
Abort thresholdHard-coded at 500 contacts by default. If the search returns more, the script exits without deleting anything. Adjust only with explicit user confirmation.
CSV audit trailEvery contact ID, name, and create date is exported to CSV before any deletion occurs.
Confirmation promptAlways present the Before State count to the user and wait for explicit confirmation before running Execute.
90-day recoveryDeleted contacts can be restored via HubSpot Settings > Data Management > Deleted Objects for 90 days.
Archived contacts auditAfter deletion, you can retrieve deleted contacts via the standard contacts endpoint with archived=true parameter to verify what was removed.

Technical Gotchas

  1. NOT_HAS_PROPERTY vs EQ "": Use NOT_HAS_PROPERTY operator, not EQ with an empty string. HubSpot treats "property not set" differently from "property set to empty string."

  2. Search API pagination limit: The HubSpot CRM Search API has a hard cap of 10,000 results per query. For this use case (typically a few hundred contacts), this is not an issue. If you encounter it, use segmented queries (e.g., filter by create date ranges).

  3. Rate limiting: The search API allows ~4 requests/second for a private app. The batch archive API is more restrictive. Use time.sleep(0.5) between batch archive calls.

  4. Batch archive returns 204: A successful batch archive returns HTTP 204 with an empty body, not 200. Check for status_code == 204.

  5. Contacts may reappear: If an integration or form is creating contacts without email, new ones will appear after deletion. Always investigate the root cause.

Package Setup

uv init hubspot-cleanup
cd hubspot-cleanup
uv add requests python-dotenv

Create a .env file:

HUBSPOT_ACCESS_TOKEN=pat-na1-xxxxxxxx

GitHub Repository

TomGranot/hubspot-admin-skills
Path: skills/delete-no-email-contacts
0

Related Skills

railway-docs

Documentation

This skill fetches current Railway documentation to answer questions about features, functionality, or specific docs URLs. It ensures developers receive accurate, up-to-date information directly from Railway's official sources. Use it when users ask how Railway works or reference Railway documentation.

View skill

n8n-code-python

Documentation

This Claude Skill provides expert guidance for writing Python code in n8n's Code nodes, specifically for using Python's standard library and working with n8n's special syntax like `_input`, `_json`, and `_node`. It helps developers understand Python's limitations within n8n and recommends using JavaScript for most workflows while offering Python solutions for specific data transformation needs.

View skill

archon

Documentation

The Archon skill provides RAG-powered semantic search and project management through a REST API. Use it for querying documentation, managing hierarchical projects/tasks, and performing knowledge retrieval with document upload capabilities. Always prioritize Archon first when searching external documentation before using other sources.

View skill

n8n-code-javascript

Documentation

This Claude Skill provides expert guidance for writing JavaScript code in n8n's Code nodes. It covers essential n8n-specific syntax like `$input`/`$json` variables, HTTP helpers, and DateTime handling, while troubleshooting common errors. Use it when developing n8n workflows that require custom JavaScript processing in Code nodes.

View skill