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 skills add NachoLafuente/5050-gtm --skill attio-deepline-enrichgit clone --depth 1 https://github.com/NachoLafuente/5050-gtmWrote 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/nacholafuente/5050-gtm/attio-deepline-enrich)<a href="https://agentmods.dev/skills/nacholafuente/5050-gtm/attio-deepline-enrich"><img src="https://agentmods.dev/badge/skills/nacholafuente/5050-gtm/attio-deepline-enrich/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nacholafuente/5050-gtm/attio-deepline-enrich"><img src="https://agentmods.dev/badge/skills/nacholafuente/5050-gtm/attio-deepline-enrich.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00155 | $0.01973 |
| Opus 5 | $0.00077 | $0.00986 |
| Sonnet 5 | $0.00031 | $0.00395 |
| Haiku 4.5 | $0.00015 | $0.00197 |
Grade A, and why
attio-deepline-enrich 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 12d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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,dropleadsdirect/mobile phone phone waterfall bettercontact,datagma,trestlefirmographics (employees, industry, revenue) company enrich peopledatalabs,crustdataLinkedIn URL profile resolve linkedin_scraper,crustdata(or thelinkedin-url-lookupskill)job title / seniority person enrich peopledatalabs,apolloICP score / segment / custom flag classify aiinferenceover the row
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 163 lines · 155 tokens per session scan A 4ea39fa3a99e
attio-deepline-enrich is a skill published in the GitHub repository NachoLafuente/5050-gtm (3 stars, last pushed 2mo ago), licensed MIT. It adds 155 tokens to every session and 1,973 once invoked, about $0.0008 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 2026-08-31.
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