Abm Strategy logo

Abm Strategy

Organization
LeadMagic
abm-strategy

Designs an account-based marketing program: tier selection, account scoring, channel orchestration, BDR alignment (account-based sales development), and a measurement framework. Use when the user is launching or redesigning ABM, choosing between 1-to-1, 1-to-few, and 1-to-many, or building a target account program. Triggers on: "ABM strategy", "account based marketing", "ABM playbook", "design ABM", "target accounts", "ABSD", "account based sales development", "Lars Nilsson".

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill nameabm-strategy
Stars
72
Forks
18
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeadMagic on GitHub. Read the source before you install it.

Installation

Install the Abm Strategy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeadMagic/gtm-skills/tree/main/skills/abm/abm-strategy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeadMagic/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/abm/abm-strategy .claude/skills/abm-strategy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Abm Strategy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Abm Strategy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Abm Strategy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ABM Strategy

Overview

ABM is not "send a gift and call it ABM." It's a systematic approach to treating accounts as markets of one — aligned sales and marketing motions against a curated set of target accounts. This skill designs the full ABM program: tier selection, account intelligence, channel orchestration, and measurement.

When to Use

  • "Design our ABM strategy"
  • "Set up account-based marketing"
  • "Build a target account program"
  • "How do we run ABM?"
  • "Account selection for enterprise"

Authoritative Foundations

  • ITSMA ABM Framework — Three tiers of ABM: Strategic (1-to-1), Scale (1-to-few), Programmatic (1-to-many). Each tier has different investment, account count, and personalization depth.
  • TOPO Account-Based Framework — Account selection, insights, plays, metrics. The six roles in an ABM team: ABM lead, account exec, SDR, field marketing, content marketing, marketing ops.
  • Winning by Design Bowtie — ABM lives between marketing (left side) and sales (center), bridging awareness through close and into post-sale expansion.
  • Lars Nilsson — Account-Based Sales Development (ABSD). The SDR execution layer of ABM, created at Cloudera and scaled across 100+ SDRs at Snowflake. Account selection on buying signals (intent surge, job posts, funding) not hunches; SDR + AE + vertical SME co-author 3-email sequences from a use-case library; multi-persona outreach in 50–250 contact chunks; quality of engagement over volume sent (public first-campaign results: ~70% open / ~30% reply vs 5–8% / 2–3% for nurture blasts). Canonical → references/lars-nilsson-absd.md.
  • Sangram Vajre — TEAM and MOVE. Treat account-based work as one GTM motion, not a marketing campaign. TEAM aligns Target, Engage, Activate, and Measure; MOVE diagnoses maturity across Market, Operations, Velocity, and Expansion. Use TEAM to design the account journey and MOVE to identify the operating constraint before adding channels or tools.

Step-by-Step Process

Phase 1: Account Selection

Define the 3-tier account model:

  • Tier 1 (1-to-1): 5-15 accounts. Strategic, $100K+ ACV. Full account intelligence, custom content, executive engagement. C-suite mapping required.
  • Tier 2 (1-to-few): 15-50 accounts. Clustered by industry/use case. Semi-custom campaigns, persona-specific outreach, industry-specific proof points.
  • Tier 3 (1-to-many): 50-200+ accounts. Programmatic. Lookalike from Tier 1-2 wins, automated personalization, scaled outbound.

Selection criteria: ICP fit score (0-100), intent signals (hiring, funding, tech stack), engagement history (website visits, content downloads, event attendance), deal size potential.

Phase 2: Account Intelligence

For each Tier 1/2 account, build an account brief:

  • Company snapshot (revenue, employees, growth, funding)
  • Strategic priorities (earnings calls, annual reports, press)
  • Org chart (decision-maker, champion, economic buyer, influencers, blockers)
  • Tech stack (current tools, gaps, integration points)
  • Relationship map (who knows who, existing connections)
  • Trigger events (new hire, funding, M&A, product launch, earnings miss)

Phase 3: Channel Orchestration

Map channels by tier:

  • Tier 1: Executive mailers, Giftology gifts (strategic-gifting), custom microsites, direct mail, in-person events, executive-to-executive calls
  • Tier 2: Personalized email + LinkedIn, industry roundtables, webinar invites, direct mail, SDR outbound
  • Tier 3: Automated email sequences, LinkedIn ads, content syndication, webinar series, SDR cadences

Phase 4: BDR Alignment

ABM fails without tight sales alignment:

  • BDRs assigned to specific accounts (not territories)
  • Shared account briefs between marketing and BDRs
  • Weekly ABM standups: marketing + BDRs + AEs on target accounts
  • SLAs: contact attempts within 24h of marketing touch

For the full SDR execution layer — signal-based account prioritization, SDR + AE + SME co-authored sequences, multi-persona chunking, and public response benchmarks — run the ABSD playbook: references/lars-nilsson-absd.md.

Phase 5: Measurement Framework

Don't measure ABM on MQLs. Measure:

  • Coverage: % of target accounts with contacts identified
  • Engagement: touches per account per week, depth (C-suite vs end user)
  • Pipeline: opportunities created from target accounts
  • Velocity: days from first touch to opportunity
  • Win rate: target accounts vs non-target
  • Deal size: target account ACV vs non-target

Output Format

ABM strategy document with: tier definitions, account selection criteria and initial list, intelligence template, channel plan by tier, BDR alignment model, measurement dashboard design.

Quality Check

Before delivering, verify:

  • All required sections are complete
  • Output matches the user's stated need
  • Named frameworks are cited for key recommendations
  • No vague claims — every recommendation has a specific action
  • Deliverable is ready for operational use, not just conceptual

Common Pitfalls

  1. Treating ABM as a marketing-only initiative. ABM requires tight sales alignment. Without BDRs assigned to specific accounts and shared account briefs, marketing produces content nobody uses. Fix: weekly ABM standups with marketing + BDRs + AEs.
  2. One-size-fits-all tiering. Applying the same playbook to Tier 1 and Tier 3 accounts. Fix: Tier 1 gets custom content and executive engagement; Tier 3 gets automated personalization.
  3. Measuring ABM on MQLs. ABM success is pipeline from target accounts, not lead volume. Fix: track coverage %, engagement depth, pipeline created, and win rate by tier.

Implementation Depth

Use this section when the user asks for a finished asset, not a high-level explanation.

Diagnostic Questions

  1. What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led?
  2. Which ICP tier is the output for: small business, mid-market, enterprise, or mixed?
  3. What proof is available today: customer stories, usage data, third-party validation, screenshots, or none?
  4. What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow?
  5. What decision will the user make from this output: launch, prioritize, route, rewrite, score, coach, or measure?

Framework Application

Map the recommendation explicitly to the named frameworks in this skill:

  • ITSMA ABM Framework: apply only the part that directly improves the requested deliverable.
  • TOPO Account-Based Framework: apply only the part that directly improves the requested deliverable.
  • WbD Bowtie: apply only the part that directly improves the requested deliverable.
  • John Ruhlin Giftology: apply only the part that directly improves the requested deliverable.
  • Lars Nilsson ABSD: apply only the part that directly improves the requested deliverable.
  • Sangram Vajre TEAM and MOVE: apply only the part that directly improves the requested deliverable.

Deliverable Standard

A strong output from this skill includes:

  • A crisp diagnosis of the current situation
  • A recommended path with tradeoffs, not a generic list
  • A concrete artifact the user can use immediately: table, script, checklist, scorecard, sequence, dashboard spec, or implementation plan
  • A measurement plan with leading and lagging indicators
  • Risks and edge cases called out before execution

Adaptation Rules

  • For small business: reduce complexity, shorten time-to-value, and prioritize owner/operator clarity.
  • For mid-market: include workflow ownership, handoffs, integrations, and enablement assets.
  • For enterprise: include governance, risk, procurement, stakeholder mapping, and proof requirements.

Execution Artifacts

  • references/framework-notes.md — Named frameworks and reference tables
  • templates/output-template.md — Deliverable shell for agent output
  • scripts/check-output.py — Lightweight deliverable validator
  • references/lars-nilsson-absd.md — Lars Nilsson (Cloudera/Snowflake) ABSD playbook: signal-based selection, SME sequence design, stack, benchmarks

Related Skills

  • abm-1-to-1, abm-1-to-few, abm-1-to-many, account-selection, multi-thread-orchestration
  • icp-scoring, sales-enablement, meeting-prep, pipeline-management, signal-scoring

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Abm Strategy AI skill do?

Designs an account-based marketing program: tier selection, account scoring, channel orchestration, BDR alignment (account-based sales development), and a measurement framework. Use when the user is launching or redesigning ABM, choosing between 1-to-1, 1-to-few, and 1-to-many, or building a target account program. Triggers on: "ABM strategy", "account based marketing", "ABM playbook", "design ABM", "target accounts", "ABSD", "account based sales development", "Lars Nilsson".

Why use Abm Strategy on TypingMind?

Because you install it once and use it with any model. Abm Strategy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Abm Strategy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/abm/abm-strategy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Abm Strategy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Abm Strategy?

As many as you like. As long as a model supports skills, you can use Abm Strategy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Abm Strategy AI skill free?

Yes. It is published on GitHub by LeadMagic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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