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Data Enrichment

hubspot/agent-cli-skills
1k installs18 stars
Summary

You've got a CSV of leads or company data and need to push it into HubSpot without writing a script that searches, checks for existence, then creates or updates. This wraps the `hubspot objects upsert` CLI around a jq pipeline so you can match contacts by email or companies by domain in one pass per record. The dry run workflow is solid, the per-record JSON output lets you split successes from failures and retry just what broke, and it actually tells you about the five filter limit on OR searches instead of letting you discover it at 2am. If you're doing any kind of bulk CRM sync from external data, this is the pattern. Just lowercase your keys first.

Install to Claude Code

npx -y skills add hubspot/agent-cli-skills --skill data-enrichment --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md

Prereq: read bulk-operations/SKILL.md first — JSONL piping, dry-run/digest, history, and rate-limit hygiene live there. This skill is the upsert-by-natural-key workflow on top.

The core move: upsert, not search-then-create

hubspot objects upsert --type X --id-property <natural-key> reads JSONL on stdin and creates-or-updates each row in one CLI call (the CLI batches 100 rows per API request), keyed by a property (email for contacts, domain for companies). No race window, no branching. Do not loop search → empty? → create.

Per line in: {"id":"jane@example.com","properties":{"firstname":"Jane","jobtitle":"VP"}} Per line out: {"id":"123","ok":true,"data":{...}} or {"ok":false,"error":{...}}. Order matches input. The CLI adds no fields of its own — data is the raw batch-upsert API result row.

CSV/JSONL → upsert stream

Reshape with jq, preview with --dry-run, then execute. upsert is irreversible, so the execute step re-pipes the SAME inputs plus the --digest/--confirm lifted from the preview line (upsert confirm = the row count, always). Always lowercase the natural key — CRM match is exact. Confirm available property names with hubspot properties list --type contacts; never hard-code a list. See bulk-operations/resources/json-patterns.md for reshape idioms.

# CSV → JSONL (any tool); example using csvkit
csvjson external.csv | jq -c '.[]' > external.jsonl

# Preview
cat external.jsonl \
| jq -c '{id:(.email|ascii_downcase), properties:{firstname:.first, lastname:.last, jobtitle:.title, company:.company}}' \
| hubspot objects upsert --type contacts --id-property email --dry-run \
| tee /tmp/upsert.preview.jsonl

# Lift the digest + confirm (present at every row count; upsert confirm = the row count)
digest=$(jq -r 'select(.digest != null) | .digest' /tmp/upsert.preview.jsonl)
confirm=$(jq -r 'select(.digest != null) | .target.id' /tmp/upsert.preview.jsonl)

# Execute — same pipeline, plus --digest/--confirm, capture results
cat external.jsonl \
| jq -c '{id:(.email|ascii_downcase), properties:{firstname:.first, lastname:.last, jobtitle:.title, company:.company}}' \
| hubspot objects upsert --type contacts --id-property email --digest "$digest" --confirm "$confirm" \
| tee /tmp/upsert.results.jsonl

Companies: swap --type companies --id-property domain and reshape with .domain|ascii_downcase as id.

Handle per-record OK / error output

Split with jq, inspect failure modes, retry just the failures after fixing the inputs:

jq -c 'select(.ok==true)'  /tmp/upsert.results.jsonl > /tmp/upsert.ok.jsonl
jq -c 'select(.ok==false)' /tmp/upsert.results.jsonl > /tmp/upsert.failed.jsonl
jq -r '.error.status' /tmp/upsert.failed.jsonl | sort | uniq -c   # status → count

The CLI does not tag rows as created-vs-updated. If the batch-upsert API result row carries a new boolean, jq -r '.data.new' /tmp/upsert.ok.jsonl | sort | uniq -c splits them; otherwise compare .data.createdAt against .data.updatedAt.

429s: split the input and rerun smaller chunks (see bulk-operations rate-limit notes). 400s usually mean a bad property name or invalid enum value — fix the reshape, rerun the failed inputs.

Destructive-op safety

upsert itself is non-destructive, but write-back can clobber populated fields. Always --dry-run first and spot-check. For bulk delete or overwrite of existing data, follow the dry-run → digest → confirm flow in bulk-operations/SKILL.md. Recovery: hubspot history --since 1h.

Match without upsert: OR-search → update

When you only want to read matches (no write-back), or the natural key isn't a CRM property, use repeated --filter flags — each flag is one OR group.

Verified cap: 5 OR groups per call. 6+ returns 400 too many filterGroups (count: N, max allowed: 5). Chunk 5 at a time:

# emails.txt: one lowercased email per line
xargs -n5 < emails.txt | while read -r e1 e2 e3 e4 e5; do
  args=()
  for e in "$e1" "$e2" "$e3" "$e4" "$e5"; do [ -n "$e" ] && args+=(--filter "email=$e"); done
  hubspot objects search --type contacts "${args[@]}" --properties email,firstname,company
done > /tmp/matches.jsonl

jq -c '{id, properties:{lifecyclestage:"marketingqualifiedlead"}}' /tmp/matches.jsonl \
| hubspot objects update --type contacts --dry-run

For larger keyed enrichments, prefer upsert — one pipeline, no chunking math.

Featured
CodeRabbit
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AI writes the code. CodeRabbit catches the slop.
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Categories
AI & Agent BuildingSales & MarketingCLI & Terminal
First SeenJul 14, 2026
View on GitHub

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