data-enrichment
Skill28 starsChanged 43 days agoApache-2.0View on GitHub
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`.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- pjmuller/skillstheir own version, first seen Oct 23, 2026
- NEON-Rutger/B2B-revops-skillstheir own version, first seen Oct 5, 2026
Other versions2 projects use a version of this.
Word for word · 0
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Edited · 0
None found.
Wrote their own version · 2
- NEON-Rutger/B2B-revops-skills★ 58same name
- pjmuller/skills★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- audience-targetingSkill
- bulk-operationsSkill
- communication-historySkill
- conversation-history-briefSkill
- conversation-inbox-triageSkill
- crm-data-qualitySkill
- crm-lookupSkill
- customer-retentionSkill
- custom-object-managementSkill
- deal-managementSkill
- quote-to-cashSkill
- sales-executionSkill
- sales-reportingSkill
- team-ownershipSkill
- ticket-resolutionSkill
- visitor-identificationSkill
- workflow-automationSkill
- workSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 69
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 …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.jsonlCompanies: 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 → countThe 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-runFor larger keyed enrichments, prefer upsert — one pipeline, no chunking math.
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