Delete No Email Contacts

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

Scripts

before.py counts contacts with no email and exports the CSV baseline. execute.py collects IDs, exports the audit CSV, and batch-archives (typed confirmation + safety threshold). after.py verifies zero remain.

Execution Pattern

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

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

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

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

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.

Rollback

• Deleted contacts are recoverable for 90 days via HubSpot Settings > Data Management > Deleted Objects.
• The execute script's CSV audit trail lists every deleted contact ID for selective restoration.

Setup

Create a .env file in the repo root:

HUBSPOT_ACCESS_TOKEN=pat-na1-xxxxxxxx

No package install is needed — scripts carry PEP 723 inline metadata and run with uv, which resolves dependencies automatically:

uv run skills/delete-no-email-contacts/scripts/before.py