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 LaGrowthMachine/gtm-system --skill outbound-nurture-enginegit clone --depth 1 https://github.com/LaGrowthMachine/gtm-systemWrote 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/lagrowthmachine/gtm-system/outbound-nurture-engine)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/outbound-nurture-engine"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/outbound-nurture-engine/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/lagrowthmachine/gtm-system/outbound-nurture-engine"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/outbound-nurture-engine.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 | $0.00221 | $0.04985 |
| Opus 5.5 | $0.00088 | $0.01994 |
| Sonnet 5.5 | $0.00044 | $0.00997 |
| Haiku 4.5 | $0.00022 | $0.00498 |
Grade A, and why
outbound-nurture-engine scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Apply `references/nurture-situations.md` to each thread. Label `situation` as `not_now`, `vague` or `in_nurture`; leave `ghosted` to the engine (it decides from dates). Extract the **nurture profile**: `pains[]` (the wor How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outbound Nurture Engine
Turns the replies your outbound did not convert yet into a running nurture wave: each lead gets the content that fits them, written onto the lead in La Growth Machine, and a duplicated campaign carries the touches on a 6-week cadence.
Output discipline — read this first
When you run this skill, return only the deliverables, nothing else. No preamble, no narration of the steps, no restating these instructions. Each step is one framing line plus its table, code block or widget. If something essential is missing (where the content lives, which template campaign, which conversations), ask one short, specific question and stop. Nothing is written into La Growth Machine before the user has approved the wave.
Authority — read this first
- The engine (
scripts/build.py) owns everything that can be silently wrong: discovering content URLs from a sitemap, keeping the library index, scoring content against leads, refusing to re-send a content, validating the custom-attribute sentences and the campaign messages. Never do these by hand. Run the engine; reason over its JSON.python3 scripts/build.py --testis the self-test. - Your job is the judgment: reading each thread to label its situation and extract the lead's nurture profile, tagging the content you read, writing the per-lead sentences and the three campaign messages.
references/nurture-situations.md: the four situations, the triage tree, what is not nurture. Read at Step 2.references/library-schema.md: the three content sources (Notion, website via sitemap, Google Drive), the tag axes, where the index lives. Read at Step 3.references/touch-rules.md: how to write the sentence stored on each lead and the three campaign messages. Read at Step 5.examples/: a fictional library and a fictional lead set for a worked run.
What it does
- Find the leads worth nurturing in the replies you already got: not now, vague, ghosted after an exchange.
- Profile each one from the full thread (pain, persona, industry, size, language, return condition).
- Index your content library and match 1 to 3 contents per lead, never one already sent, with a generic fallback and a content-gap report.
- Write one ready-to-insert sentence per content onto the lead (custom attributes) and add the lead to the wave's audience.
- Duplicate the nurture template campaign, fill its three messages with the lead variables, assign the audience. The user launches it in La Growth Machine.
What ships with it
7 files 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.
- 1mo ago First seen · 204 lines · 221 tokens per session scan A a86faa3376fb
outbound-nurture-engine is a skill published in the GitHub repository LaGrowthMachine/gtm-system (45 stars, last pushed 9d ago), licensed MIT. It adds 221 tokens to every session and 4,985 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
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