Install
$ agentstack add skill-nacholafuente-5050-gtm-attio-deepline-enrich ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Attio + Deepline Enrich
Enrich records that already live in Attio, without exporting to a spreadsheet, hand-mapping columns, or guessing what it'll cost. You pick the object, the size, and the fields. The skill prices the run to the credit, asks once, then does it and writes the answers back into Attio.
Attio is both the source and the sink, and Attio reads/writes are free, so every credit is spent inside Deepline on the actual enrichment. Nothing is charged before you say yes.
> Powered by Deepline. We're big fans, 50+ GTM data providers behind one CLI is the reason this skill is three steps instead of thirty.
The flow
Attio (read, free) -> CSV -> Deepline enrich (paid, gated) -> CSV -> Attio (write, free)
Setup (once)
ATTIO_API_KEYin.env(Attio: Settings -> Apps & integrations -> API). Needs read + write scopes on the object you're enriching.- Deepline CLI installed and authenticated:
deepline auth status. Top up at https://code.deepline.com/dashboard/billing if needed. pip install requests python-dotenv(already in this repo'srequirements.txt).
Pricing reference: 10 credits = $1, so 1 credit = $0.10.
Step 1: Which object?
Ask the user which Attio object to enrich (companies, people, or a custom object slug). Then show them what's actually on it, never guess slugs:
python skills/attio-deepline-enrich/enrich.py attrs --object companies
This prints every attribute as slug | type | title (free, read-only). Use it to confirm the input slugs (what Deepline needs to work from, e.g. domains, name, email_addresses) and the target slugs (what you'll fill).
Step 2: How many, and which properties?
Ask three things and don't pick defaults silently:
- Which attributes to fill? (the targets) Map each one to how Deepline will source it. Common ones:
| Target in Attio | Deepline source | Typical tool family | |---|---|---| | work email | email waterfall | bettercontact, findymail, prospeo, dropleads | | direct/mobile phone | phone waterfall | bettercontact, datagma, trestle | | firmographics (employees, industry, revenue) | company enrich | peopledatalabs, crustdata | | LinkedIn URL | profile resolve | linkedin_scraper, crustdata (or the linkedin-url-lookup skill) | | job title / seniority | person enrich | peopledatalabs, apollo | | ICP score / segment / custom flag | classify | aiinference over the row |
If unsure which provider, read ~/.agents/skills/deepline-gtm/provider-playbooks/.md for cost and quality notes before committing.
- How many records? This is the cost driver. Pull them, keeping only rows that still need the work so you never pay to re-enrich:
``bash python skills/attio-deepline-enrich/enrich.py pull \ --object companies \ --inputs domains,name \ --target estimated_arr,employee_count \ --only-missing \ --limit 50 \ --output /tmp/enrich_in.csv ``
The script prints the real row count. That count, not the user's round number, is N for costing. If the user wants a hard cap of complete rows, over-provision ~1.4x at pull time and filter to the best N at the end (provider coverage has natural falloff; chasing the last few rows wastes credits).
Step 3: Pilot one row to learn the true cost
Never estimate from a price list. Run the real enrichment on exactly one row and read what it actually cost:
deepline billing balance --json # note the starting balance
deepline enrich --input /tmp/enrich_in.csv --output /tmp/enrich_out.csv --rows 0:1 \
--with '{"alias":"work_email","tool":"bettercontact_enrich","payload":{ ...from provider playbook... }}'
deepline session usage --json # credits the pilot consumed
Build the per-row cost from the pilot. Per-row credits x N = the estimate. If you're chaining several attributes (a waterfall), the pilot must include every step so the per-row number is real.
Step 4: The cost gate (mandatory, blocking)
Before the full run, alert the Session UI, then show the gate verbatim and stop:
deepline session alert --message "Approval needed: enrich N rows in Attio (~X credits)"
Present exactly these four sections:
Assumptions
-
-
CSV Preview (ASCII)
Credits + Scope + Cap
- Providers:
- Per-row cost (from pilot):
- Full-run scope:
- Estimated credits: (~$)
- Spend cap:
Approval Question
About to spend ~ credits (~$ at $0.10/credit) enriching in Attio.
Reply "YES I AGREE" to proceed.
Wait for the exact string YES I AGREE (case-insensitive). "yes", "ok", "go", "sure" are not consent, re-ask. One approval covers this exact run only; a new object, new attributes, or more rows needs a fresh gate.
Step 5: Full run
Only after YES I AGREE. Enrich every row (omit --rows), writing to the output CSV:
deepline enrich --input /tmp/enrich_in.csv --output /tmp/enrich_out.csv \
--with '{"alias":"work_email","tool":"...","payload":{ ... }}'
For multi-attribute or waterfall runs, use the deepline enrich --with-waterfall ... --end-waterfall form (see deepline --help and the deepline-gtm skill). Iterate with --in-place on /tmp/enrich_out.csv only, never on the source CSV.
Step 6: Write back into Attio
Dry-run first so the user sees exactly what lands where, then write for real:
# preview
python skills/attio-deepline-enrich/enrich.py push \
--object companies \
--input /tmp/enrich_out.csv \
--map work_email=email_addresses,arr=estimated_arr \
--mode append \
--dry-run
# write
python skills/attio-deepline-enrich/enrich.py push \
--object companies \
--input /tmp/enrich_out.csv \
--map work_email=email_addresses,arr=estimated_arr \
--mode append
--map is csv_column=attio_slug. Write modes:
append(default) -> PATCH. Adds values, preserves existing ones. Right for multi-value fields (emails, phones, multi-select).replace-> PUT. Overwrites the attribute's values. Use only when the user wants the old value gone (removing/replacing a single-value field).
Writes are paced under Attio's 25/sec limit and auto-retry on 429. Report back: rows written, skipped (came back empty from the provider), and failed.
Guardrails
- Read first, query attributes before writing so slugs and types are real.
- Pilot before pricing. The pilot is the only honest cost source.
- Gate before spending. No paid enrichment runs without
YES I AGREE. - Dry-run the write-back before mutating Attio.
append/PATCH by default. Reach forreplace/PUT only on explicit "overwrite it".- Don't chase incomplete rows. Over-provision, then deliver the complete ones.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: NachoLafuente
- Source: NachoLafuente/5050-gtm
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.