linkedin-abm-strategy-planner

linkedin-abm-strategy-planner is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 116 tokens per session (1,876 once invoked), scanned A, original, MIT.

A planning workflow that creates an account-based marketing strategy for LinkedIn ads from a short interview. It turns revenue goals, audiences, budgets, and campaign needs into a written plan.

In plain words
What is it for?
It is for setting ABM goals, estimating budgets, choosing target audiences, planning campaigns, and deciding how many ads to run.
Why use it?
It gives teams a concrete advertising plan instead of leaving goals and budgets as rough ideas. It can use connected advertising data or clearly labelled benchmarks when live data is unavailable.

Skill for Claude CodeCodex

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

Good fit It is for setting ABM goals, estimating budgets, choosing target audiences, planning campaigns, and deciding how many ads to run.

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Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner
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 swan-gtm/gtm-skills --skill linkedin-abm-strategy-planner
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-skills

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 linkedin-abm-strategy-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner/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 linkedin-abm-strategy-planner

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-strategy-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,876 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00116 $0.01876
Opus 5 $0.00058 $0.00938
Sonnet 5 $0.00023 $0.00375
Haiku 4.5 $0.00012 $0.00188

Measured 9d ago against content hash 337c5aec0f66, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

linkedin-abm-strategy-planner 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.

skills/emilia-korczynska/linkedin-abm-strategy-planner/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn ABM strategy planner

Turn a short interview into a personalized, self-serve "[Company] LinkedIn ABM Strategy" — delivered as a branded HTML document that the user can read and download as PDF. This skill is prospect-facing and self-contained: never assume the company's numbers are already known. Ask for them (with sensible defaults), compute the plan, and generate the document.

Data note: this skill works best with the ZenABM connector (app.zenabm.com) — it pulls your real LinkedIn CPC/CTR, spend and audience size so the plan runs on live data. Without it, the skill uses the numbers you provide, falling back to clearly-labelled industry benchmarks.

Step 1 — Interview (few, fast questions)

Ask in small batches, offering the defaults as selectable options where the interface supports it, otherwise plain questions. Keep it light — most answers are a single number or line. Full wording, defaults and what each input powers are in the Questions reference.

Collect, per the model in the Formulas reference:

  1. Company website URL — you research this yourself (Step 2); do not make the user describe their product.
  2. ABM revenue goal + whether it's monthly (MRR) or annual (ARR) — per audience if they name more than one.
  3. Average contract value (ACV) — total contract value or MRR (you normalize).
  4. Close rate % (qualified → closed-won) — default 15%.
  5. Qualification rate % (demo → qualified) — default 75%.
  6. Landing-page conversion rate % (visit → booked demo/form) — default 0.8%.
  7. LinkedIn CPC ($) — default $8. Skip if the ZenABM connector is available — pull the real CPC instead.
  8. Planned monthly LinkedIn ads budget ($).
  9. Target audiences / markets in scope (geo + industry) — sets how many Campaign blocks. Seed from the website research, then confirm.
  10. Top jobs-to-be-done / use cases per audience + the core problem you solve — pre-fill from the research, let them edit. Powers the ad-content section.

Read the full file on GitHub · 112 lines

Files

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.

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. 9d ago First seen · 112 lines · 116 tokens per session scan A 337c5aec0f66

Subscribe to this mod's changes

linkedin-abm-strategy-planner is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 116 tokens to every session and 1,876 once invoked, about $0.0006 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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