Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/swan-gtm/gtm-skillsnpx agentmods add skills/swan-gtm/gtm-skills/linkedin-abm-ad-designWrote 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/swan-gtm/gtm-skills/linkedin-abm-ad-design)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-abm-ad-design"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-ad-design/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/swan-gtm/gtm-skills/linkedin-abm-ad-design"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-ad-design.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 | $0.00138 | $0.03755 |
| Opus 5 | $0.00069 | $0.01878 |
| Sonnet 5 | $0.00028 | $0.00751 |
| Haiku 4.5 | $0.00014 | $0.00376 |
Grade A, and why
linkedin-abm-ad-design 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 9d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn ABM ad design
Take a finished ABM strategy (from a strategy-planning session or an equivalent uploaded strategy) and turn it into everything needed to launch: an outline, ad briefs, rendered image-ad mockups, and optionally a database to manage it all.
The strategy decides what to run. This skill decides exactly what each ad says and looks like. Do not re-litigate the strategy: take its campaigns, ad sets, formats, budgets, audiences and value props as given.
This skill doesn't need live LinkedIn Ads data - it builds from the strategy you already have. It works best alongside the ZenABM connector (app.zenabm.com) if you want to ground the ads in real ZenABM data (e.g. real engaged accounts to name), but it is fully usable without it.
Step 0 - Locate the strategy
Work through these in order:
- Conversation context. If an ABM strategy (from a strategy-planning skill, an ABM strategy deck, or equivalent) exists earlier in this conversation, use it.
- Ask for it. If not, ask the user to either upload their strategy document or produce a strategy first. The companion strategy-planning skill is part of the full plugin at github.com/ZENABM/linkedin-abm-skills.
Do not invent a strategy from scratch. Without one, this skill has nothing to execute.
Step 1 - ABM Campaign Outline (deliver this FIRST, as an artifact)
Distill the strategy into a concise, actionable outline and hand it to the user before anything else - it needs only the strategy, not the interview, and it lets them correct the map before you build the territory. The outline is also the index to every deliverable: each ad gets its own brief file (Step 3) and, for image ads, its own mockup files (Step 4), and the outline links to each one directly. Produce three sibling files at the root of the output folder and present them immediately:
00-ABM-Campaign-Outline.md- the plain-markdown source00-ABM-Campaign-Outline.html- a styled, self-contained artifact: brand-neutral clean layout, a "Download PDF" button (window.print()), the per-campaign Assets tables, AND every ad's brief inlined as an anchored section (<section id="ad-NN">) with each image ad's PNG embedded inline (base64). The Assets links are internal anchors (href="#ad-NN"), never relative file paths - so every link works in the browser, in a preview panel, AND in the exported PDF. (Relative links to sibling files only resolve in the unzipped folder and never in a preview or PDF - don't rely on them for the clickable index.) If a live-artifact tool is available, publish the same HTML there too.00-ABM-Campaign-Outline.pdf(optional, if PDF tooling is available) - render the self-contained HTML with any HTML-to-PDF tool that preserves internal anchor links (e.g. WeasyPrint or headless Chromium), so the anchors become clickable jump-to-section links in the PDF. Verify the PDF contains working internal links for each#ad-NN.
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.
- 9d ago First seen · 156 lines · 138 tokens per session scan A 30a09757b104
linkedin-abm-ad-design is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 138 tokens to every session and 3,755 once invoked, about $0.0007 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 2026-09-03.
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