Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/hubspot/agent-cli-skills/data-enrichmentnpx skills add HubSpot/agent-cli-skills --skill data-enrichmentgit clone --depth 1 https://github.com/HubSpot/agent-cli-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hubspot/agent-cli-skills/data-enrichment)<a href="https://agentmods.dev/skills/hubspot/agent-cli-skills/data-enrichment"><img src="https://agentmods.dev/badge/skills/hubspot/agent-cli-skills/data-enrichment.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.01096 |
| Opus 5 | $0.00020 | $0.00548 |
| Sonnet 5 | $0.00008 | $0.00219 |
| Haiku 4.5 | $0.00004 | $0.00110 |
Grade A, and why
data-enrichment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 per record, 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":"[email protected]","properties":{"firstname":"Jane","jobtitle":"VP"}}
Per line out: {"id":"123","ok":true,"data":{...,"new":true|false}} or {"ok":false,"error":{...}}. Order matches input.
CSV/JSONL → upsert stream
Reshape with jq, preview with --dry-run, then execute. 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 | head
# Execute (same pipeline, drop --dry-run, 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 \
| 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
jq -r '.data.new' /tmp/upsert.ok.jsonl | sort | uniq -c # created vs updated
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 80 lines · 40 tokens per session scan A cf66001834af
data-enrichment is a skill published in the GitHub repository HubSpot/agent-cli-skills (23 stars, last pushed yesterday), licensed Apache-2.0. It adds 40 tokens to every session and 1,096 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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