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 post-to-campaigngit 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/post-to-campaign)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/post-to-campaign"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/post-to-campaign/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/post-to-campaign"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/post-to-campaign.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00220 | $0.02613 |
| Opus 5 | $0.00110 | $0.01307 |
| Sonnet 5 | $0.00044 | $0.00523 |
| Haiku 4.5 | $0.00022 | $0.00261 |
Grade A, and why
post-to-campaign 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 10d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post to Campaign
Turns a single LinkedIn post into a ready-to-launch La Growth Machine campaign: it reads the post, scrapes the people who liked and commented into one audience, and fills a draft campaign whose copy is calibrated to the post and your objective. You attach the audience and launch.
Authority — read this first
Everything to run the common path is inlined below. The three reference files hold the detail for one step each; open a file only when you reach that step.
- Scraping the engagers into an audience (identities, async import, polling, the audience naming rule) →
references/audience-from-post.md. Read it at Step 3. - Creating the draft campaign in LGM (
newHtmlformat, find-or-duplicate a structure, reconcile steps, fill each message) →references/lgm-campaign-create.md. Read it at Step 5. - Copywriting rules for the sequence (structure, banned phrases, anti-AI tells, per-channel length) →
references/copywriting-rules.md. Read it at Step 4. This is the base rule set. If themultichannel-campaign-builderskill is installed, defer copy generation to it for a deeper result (see Step 4).
Do not re-derive these from scratch when the file already covers them. Ask the user's input questions in English, and never use an em-dash in anything you write to the user. The campaign copy itself is written in the language of the post (confirmed with the user).
What it does
Two layers, so it is useful with or without La Growth Machine:
- Standalone (no LGM needed): from the post's content and your objective, it writes the full outreach sequence copy, ready to paste into any tool.
- With the LGM MCP connected: it also scrapes the post's likers and commenters into a single audience and fills a draft campaign with that copy, so the only thing left to do is attach the audience and hit launch.
Workflow
Step 1 — Gather the brief (ask in English, no em-dash)
Ask only for what is missing, in one message, then proceed:
What ships with it
4 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.
- 10d ago First seen · 139 lines · 220 tokens per session scan A 4b7079c45f9e
post-to-campaign is a skill published in the GitHub repository LaGrowthMachine/gtm-system (36 stars, last pushed 2d ago), licensed MIT. It adds 220 tokens to every session and 2,613 once invoked, about $0.0011 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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