crm-data-quality
Find incomplete records, normalize field values in bulk, dedupe with hubspot objects merge, and audit custom properties. Builds on bulk-operations for JSONL piping and dry-run/digest/confirm.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/hubspot/agent-cli-skills --skill crm-data-quality- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads crm-data-quality automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Read bulk-operations/SKILL.md first — JSONL piping, batch read, pagination, and dry-run/digest/confirm gating apply to every command below.
Property discovery
Don't guess property names. List them:
hubspot properties list --type contacts --format table
hubspot properties list --type contacts | jq -c 'select(.type=="enumeration") | {name, label}'
Same for --type companies, deals, or any custom type (hubspot objects types).
1. Find incomplete records
!name = NOT_HAS_PROPERTY (missing or empty). Bare name = HAS_PROPERTY. Within one --filter, chain with AND; multiple --filter flags are OR'd.
hubspot objects search --type contacts --filter "!email" --properties firstname,lastname,company
hubspot objects search --type contacts --filter "!phone AND !mobilephone" --properties email
hubspot objects search --type contacts --filter "!hubspot_owner_id" --properties email,lifecyclestage
For >100 results, use the pagination loop from bulk-operations.
2. Normalize field values
Search → reshape with jq → pipe into update. objects update is irreversible, so --dry-run first — the dry-run → digest → confirm flow is required at every row count, not just >100 (bulk-operations covers it end to end). Reshape patterns: bulk-operations/resources/json-patterns.md.
# Collapse spellings into one canonical value — 1. preview
hubspot objects search --type contacts --filter "company~acme" \
| jq -c '{id, properties:{company:"Acme Corporation"}}' \
| hubspot objects update --type contacts --dry-run \
| tee /tmp/normalize.preview.jsonl
# 2. lift the digest + confirm (present at every row count)
digest=$(jq -r 'select(.digest != null) | .digest' /tmp/normalize.preview.jsonl)
confirm=$(jq -r 'select(.digest != null) | .target.id' /tmp/normalize.preview.jsonl) # single: record ID; batch of 2+: row count
# 3. execute — re-pipe the SAME inputs plus --digest/--confirm
hubspot objects search --type contacts --filter "company~acme" \
| jq -c '{id, properties:{company:"Acme Corporation"}}' \
| hubspot objects update --type contacts --digest "$digest" --confirm "$confirm"
# Lowercase emails (read, reshape, write) — 1. preview
hubspot objects search --type contacts --filter "email" --properties email \
| jq -c '{id, properties:{email: (.properties.email | ascii_downcase)}}' \
| hubspot objects update --type contacts --dry-run \
| tee /tmp/lower.preview.jsonl
# 2. lift the digest + confirm, then 3. re-pipe the SAME inputs to execute
digest=$(jq -r 'select(.digest != null) | .digest' /tmp/lower.preview.jsonl)
confirm=$(jq -r 'select(.digest != null) | .target.id' /tmp/lower.preview.jsonl)
hubspot objects search --type contacts --filter "email" --properties email \
| jq -c '{id, properties:{email: (.properties.email | ascii_downcase)}}' \
| hubspot objects update --type contacts --digest "$digest" --confirm "$confirm"
3. Dedupe with hubspot objects merge
Secondary is folded into primary and deleted. Irreversible. Dry-run/digest/confirm gating applies.
# Single pair — dry-run first, then execute with the digest + confirm (confirm = the secondary ID for one pair)
hubspot objects merge --type contacts --primary 149 --secondary 425 --dry-run
hubspot objects merge --type contacts --primary 149 --secondary 425 --digest <hash> --confirm 425
Bulk: pipe JSONL {"primary":"...","secondary":"..."} on stdin (omit --primary/--secondary).
Pagination required. objects search caps at 100 rows per call and jq -s slurps a single stream into memory — running the snippet below against a raw search will silently miss every duplicate that crosses a page boundary. Collect the full set first with the pagination loop from bulk-operations/SKILL.md (write to /tmp/contacts.jsonl), then dedupe from the file:
# /tmp/contacts.jsonl produced by the pagination loop (bulk-operations/SKILL.md)
jq -s -c '
group_by(.properties.email)[]
| select(length > 1)
| sort_by(.id | tonumber)
| .[0].id as $p | .[1:][] | {primary: $p, secondary: .id}
' /tmp/contacts.jsonl \
| hubspot objects merge --type contacts --dry-run | tee /tmp/merge-preview.jsonl
Lift the digest/confirm from the preview line at any size — select(.digest != null); for a batch the confirm is the pair count (.target.id), and mutation_kind is BulkData only above the 100-row threshold (RecordMutation at or below it, but the digest is present either way). Re-pipe the same producer with --digest/--confirm (see bulk-operations).
4. Audit properties
hubspot properties list (and get, batch-read) emits {name, label, type, fieldType, groupName, optionsDisplay} per row. Enum option values are exposed via properties options-list — no need to read them off a live record or the UI.
# Count properties per group (HubSpot groups standard fields; custom groups stand out)
hubspot properties list --type contacts | jq -rs 'group_by(.groupName) | map({group: .[0].groupName, count: length}) | .[]'
# All enumeration properties
hubspot properties list --type contacts | jq -c 'select(.type=="enumeration") | {name, label, fieldType}'
# Create a DQ flag property, then set it via the normalize pattern in section 2
hubspot properties create --type contacts --name dq_missing_phone --label "DQ: Missing Phone" --prop-type string --field-type text
Enum options — list and manage (properties options-*, v0.11.0)
# List the allowed options on an enumeration property
hubspot properties options-list --type contacts hs_buying_role
# each row: {"value":"EVALUATOR","label":"Evaluator","displayOrder":9,"hidden":false}
# Add / rename / remove an option (options-delete is irreversible — dry-run → digest → confirm, confirm = the option value)
hubspot properties options-create --type contacts hs_buying_role --label "Evaluator" --value EVALUATOR --display-order 9
hubspot properties options-update --type contacts hs_buying_role --value EVALUATOR --label "New Label"
hubspot properties options-delete --type contacts hs_buying_role --value EVALUATOR --dry-run
hubspot properties options-delete --type contacts hs_buying_role --value EVALUATOR --digest <hash> --confirm EVALUATOR
HubSpot-defined properties with read-only options reject options-create/update/delete before the API is called.
Recovery
Merge is irreversible. After any merge, hubspot history --since 1h captures the audit trail. If wrong direction, restore the secondary from the UI's recycle bin.
FAQ
What does the crm-data-quality skill do?
Find incomplete records, normalize field values in bulk, dedupe with hubspot objects merge, and audit custom properties. Builds on bulk-operations for JSONL piping and dry-run/digest/confirm.
How do I install crm-data-quality?
Run npx skills add https://github.com/hubspot/agent-cli-skills --skill crm-data-quality in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/crm-data-quality/ (personal) or .claude/skills/crm-data-quality/ (project).
Which AI agents can use crm-data-quality?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is crm-data-quality safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains crm-data-quality?
It is published by hubspot in hubspot/agent-cli-skills. It has 1,784 recorded installs.
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