data-enrichment

Matches your spreadsheet data to customers in your CRM and adds new information automatically.

Installation
Run `npx skills add "https://github.com/hubspot/agent-cli-skills" --skill "data-enrichment"` to install this skill, then follow its SKILL.md instructions for my next request.

Paste this into Claude Code, Cursor, or any agent that can run commands.

SKILL.mdShow the author's original SKILL.md
---
name: data-enrichment
description: Match external CSV/JSONL records to CRM contacts (by email) or companies (by domain) and write enriched data back in one pass using `hubspot objects upsert`.
triggers:
  - "spreadsheet to CRM"
  - "match contacts by email"
  - "match companies by domain"
  - "enrich CRM from CSV"
  - "CRM write-back"
  - "create or update by email"
---

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.

```bash
# 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:

```bash
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:

```bash
# 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.

Mirrored from the author's public source. Install counts from the open skills registry.

The systems behind these skills get built for partners every week.

Partner with us