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 LeadMagic/gtm-skills --skill abm-1-to-manygit clone --depth 1 https://github.com/LeadMagic/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/leadmagic/gtm-skills/abm-1-to-many)<a href="https://agentmods.dev/skills/leadmagic/gtm-skills/abm-1-to-many"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/abm-1-to-many/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/leadmagic/gtm-skills/abm-1-to-many"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/abm-1-to-many.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 | $0.00064 | $0.01481 |
| Opus 5 | $0.00032 | $0.00740 |
| Sonnet 5 | $0.00013 | $0.00296 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
abm-1-to-many 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 8d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ABM 1-to-Many (Programmatic)
Overview
Programmatic ABM for 50-200+ accounts using automation, lookalike modeling, and scaled personalization. This tier uses the same methodology as 1-to-1 and 1-to-few but replaces manual effort with AI and workflow automation.
Authoritative Foundations
- TOPO Programmatic ABM — Named methodology governing recommendations in this skill's process.
- Clay Automation Patterns — Waterfall enrichment, Claygent research, and table-based GTM automation.
- ITSMA — Account-Based Marketing — Tier-based ABM (1:1 / 1:few / 1:many); measure pipeline from target accounts, not lead volume.
When to Use
- "Scale ABM to more accounts"
- "Programmatic ABM setup"
- "Automated account-based outreach"
- "Expand ABM coverage without headcount"
Step-by-Step Process
Phase 1: Lookalike Expansion
Start from Tier 1-2 winners and expand:
- ICP lookalike: Find accounts matching your top 10% win profile
- Intent lookalike: Accounts showing similar buying signals to closed-won
- Engagement lookalike: Accounts engaging with content the way winners did pre-opportunity
- Trigger lookalike: Accounts with same triggers (funding, hiring, tech change)
Phase 2: Automated Account Intelligence
Use enrichment and AI to auto-build briefs:
- Clay workflow: pull firmographics, technographics, news, signals
- AI summarizes: company snapshot, pain hypothesis, relevant proof points
- Auto-prioritize: score accounts 0-100 and assign to SDR queues
Phase 3: Scaled Personalization
- Dynamic landing pages: URL params personalize hero/headline by industry/company
- Tokenized email sequences: Merge fields beyond first name — industry, tech stack, signal
- Automated LinkedIn: AI drafts personalized connection notes and DMs
- Retargeting: Account-based ad audiences on LinkedIn by company name or domain
Phase 4: Automated Cadence Orchestration
- SDR assigned accounts per round (rotating to prevent burnout)
- Automated task creation in CRM per account
- AI drafts first outreach; SDR reviews and sends
- AI handles replies (OOO, not interested, wrong person); SDR handles positive replies
What ships with it
3 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.
- 8d ago Changed 4207c94001c3
- 12d ago First seen · 140 lines · 64 tokens per session scan A 9b39a422ac83
abm-1-to-many is a skill published in the GitHub repository LeadMagic/gtm-skills (50 stars, last pushed 4d ago), licensed MIT. It adds 64 tokens to every session and 1,481 once invoked, about $0.0003 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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