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 Ad-Superpowers/ad-superpowers-plugin --skill linkedin-abm-targeting-strategygit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWrote 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/ad-superpowers/ad-superpowers-plugin/linkedin-abm-targeting-strategy)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/linkedin-abm-targeting-strategy"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-abm-targeting-strategy/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/ad-superpowers/ad-superpowers-plugin/linkedin-abm-targeting-strategy"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-abm-targeting-strategy.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 | $0.00107 | $0.03675 |
| Opus 5.5 | $0.00043 | $0.01470 |
| Sonnet 5.5 | $0.00021 | $0.00735 |
| Haiku 4.5 | $0.00011 | $0.00367 |
Grade A, and why
linkedin-abm-targeting-strategy 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 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.
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 — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn ABM Targeting Strategy
Purpose
Help advertisers build and execute account-based marketing campaigns on LinkedIn by mapping target accounts to decision-maker personas, building tiered company lists, and layering seniority/function targeting for maximum account penetration. ABM on LinkedIn consistently delivers 2-5x higher conversion rates vs broad targeting, but only when the targeting architecture is correct.
When to Use This Skill
Invoke when user mentions:
- ABM campaigns: "I want to target specific companies on LinkedIn"
- Company list targeting: "I have a list of target accounts"
- Decision makers: "How do I reach the buying committee?"
- Account penetration: "I need to reach multiple people at the same company"
- Named account strategy: "We're doing account-based marketing"
- Enterprise targeting: "How do I target Fortune 500 companies?"
Required Tools
| Tool | Purpose |
|---|---|
linkedin_query |
Pull campaign data, audience sizes, and targeting configurations |
linkedin_get_analytics |
Measure account penetration, engagement rates by company |
Part 1: ABM Tier Architecture
Three-Tier Account Model
ABM effectiveness depends on matching investment level to account value. Never treat all target accounts the same.
TIER 1: Strategic Accounts (5-25 accounts)
├── Highest deal value (>€50K ACV)
├── Named account campaigns (1 campaign per 3-5 accounts)
├── Fully custom creative per account cluster
├── Budget: 40-50% of total ABM spend
├── Goal: Multi-threaded engagement (3+ contacts per account)
└── Measurement: Account penetration rate, meetings booked
TIER 2: Target Accounts (25-200 accounts)
├── Medium deal value (€10K-50K ACV)
├── Grouped campaigns by industry/segment (10-30 accounts each)
├── Semi-custom creative per segment
├── Budget: 30-35% of total ABM spend
├── Goal: Initial engagement + MQL generation
└── Measurement: Engagement rate, lead volume, pipeline influenced
TIER 3: Market Accounts (200-1,000 accounts)
├── Lower deal value (<€10K ACV) or early-stage prospects
├── Broad campaigns by firmographic cluster
├── Template creative with dynamic elements
├── Budget: 15-25% of total ABM spend
├── Goal: Awareness + demand generation
└── Measurement: Reach, website visits, content engagement
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 · 389 lines · 107 tokens per session scan A 80f23ed4c057
linkedin-abm-targeting-strategy is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 1mo ago), licensed MIT. It adds 107 tokens to every session and 3,675 once invoked, about $0.0004 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 2026-09-11.
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