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attio-deepline-enrich

attio-deepline-enrich is a skill for Claude Code, Codex from NachoLafuente/5050-gtm. It costs 155 tokens per session (1,973 once invoked), scanned A, original, MIT.

A tool for adding information to records in Attio, a customer and company relationship database, using Deepline data providers.

In plain words
What is it for?
It helps fill company, person, or custom-object records with details such as emails, phone numbers, company facts, LinkedIn data, and sales-fit fields after you approve the price.
Why use it?
It removes the need to export records to a spreadsheet, match columns by hand, or spend enrichment credits without knowing the cost.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

not rated 3repo 4mo ago A scan 155 tokens original MIT

Good fit It helps fill company, person, or custom-object records with details such as emails, phone numbers, company facts, LinkedIn data, and sales-fit fields after you approve the price.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nacholafuente/5050-gtm/attio-deepline-enrich
View source ↗ NachoLafuente/5050-gtm
Install

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.

Any agent
npx skills add NachoLafuente/5050-gtm --skill attio-deepline-enrich
Clone the repo
git clone --depth 1 https://github.com/NachoLafuente/5050-gtm

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for attio-deepline-enrich

README.md
[![agentmods](https://agentmods.dev/badge/skills/nacholafuente/5050-gtm/attio-deepline-enrich/github.svg)](https://agentmods.dev/skills/nacholafuente/5050-gtm/attio-deepline-enrich)
Your own site
<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.

agentmods 80×15 button for attio-deepline-enrich

Your own site · 80×15
<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>
Per session 155 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,973 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00155 $0.01973
Opus 5.5 $0.00062 $0.00789
Sonnet 5.5 $0.00031 $0.00395
Haiku 4.5 $0.00015 $0.00197

Measured 2mo ago against content hash 4ea39fa3a99e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (enrich.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/attio-deepline-enrich/SKILL.md · 163 lines

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_KEY in .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's requirements.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:

  1. 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

Read the full file on GitHub · 163 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 163 lines · 155 tokens per session scan A 4ea39fa3a99e

Subscribe to this mod's changes

attio-deepline-enrich is a skill published in the GitHub repository NachoLafuente/5050-gtm (3 stars, last pushed 4mo ago), licensed MIT. It adds 155 tokens to every session and 1,973 once invoked, about $0.0006 per session on Opus 5.5. 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.

Claude CodeMarketing and SEOagent-skillsattioclaude-skillscohort-analysiscrm
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