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 swan-gtm/gtm-skills --skill ads-outbound-signaling-guidegit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/ads-outbound-signaling-guide)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/ads-outbound-signaling-guide"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ads-outbound-signaling-guide/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/ads-outbound-signaling-guide"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ads-outbound-signaling-guide.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 | $0.00040 | $0.02687 |
| Opus 5.5 | $0.00016 | $0.01075 |
| Sonnet 5.5 | $0.00008 | $0.00537 |
| Haiku 4.5 | $0.00004 | $0.00269 |
Grade A, and why
ads-outbound-signaling-guide 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Ads + Outbound Signaling - Guide
How LinkedIn ABM ads generate intent signals that trigger and personalize BDR outbound. The bridge between paid campaigns and sales outreach.
The Core Idea
ABM ads aren't just demand generation - they're a signal detection layer. By running targeted ads to known accounts and tracking which ones engage, you identify intent before anyone fills out a form.
Ads create awareness → Engagement reveals intent → Intent triggers outbound → Outbound is personalized by what they engaged with
This is "inbound-led outbound" powered by ad engagement data.
Cold outreach reply rates are low. Signal-based outreach - reaching out only to accounts that already engaged with your ads - replies at a meaningfully higher rate, and stacking multiple engagement signals lifts it further. Track the actual lift against your own cold baseline.
How Intent Signals Flow
The Signal Pipeline
LinkedIn Campaign Manager (ads running)
↓
Fibbler (captures engagement per account per campaign)
↓
HubSpot Company Properties (quantitative + qualitative intent)
↓
Active Lists (accounts segmented by stage + intent)
↓
BDR gets Slack alert + lead with intent tags
↓
Personalized outreach based on WHAT they engaged with
Two Types of Engagement Data
| Type | What It Captures | How It's Used |
|---|---|---|
| Quantitative | Impressions, engagements, clicks (counts) | Stage progression (Identified → Aware → Interested) |
| Qualitative | WHICH campaigns they engaged with | Intent detection + outreach personalization |
Qualitative is the gold. Knowing that an account clicked on your "Analytics vs Competitor X" campaign tells BDRs exactly what pain point to lead with.
Intent Detection via Campaign Structure
How Campaign Names Encode Intent
Structure your LinkedIn campaigns so engagement data reveals intent:
Campaign Group: [Intent/JTBD]
├── Campaign: [Intent] - [Ad Type] - [Stage]
│ e.g., "Analytics-Competitor-Switch - Image - Awareness"
│ e.g., "Onboarding-Automation - Video - Interested"
│ e.g., "Session-Replay - TLA - Awareness"
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 · 291 lines · 40 tokens per session scan A 9f6c988bd6ff
ads-outbound-signaling-guide is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 40 tokens to every session and 2,687 once invoked, about $0.0002 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 2026-09-03.
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