Account Selection logo

Account Selection

Organization
LeadMagic
account-selection

Builds and tiers a target account list: fit and intent scoring models, tier assignment, and TAM segmentation. Use when the user needs to decide which accounts to target, score or tier an account list, or prioritize a TAM for ABM or outbound. Triggers on: "account selection", "target account list", "tier accounts", "prioritize accounts", "TAM segmentation".

Overview

PublisherLeadMagic
Repositorygtm-skills
Skill nameaccount-selection
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 Account Selection 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/account-selection
  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/account-selection .claude/skills/account-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Account Selection 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 Account Selection 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 Account Selection 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.

Account Selection & Tiering

Overview

Account selection is the hardest part of ABM. Pick wrong and you waste 6 months. Pick right and you 3x your close rate. This skill builds a weighted scoring model to tier your TAM into Strategic, Scale, and Programmatic accounts.

Authoritative Foundations

  • TOPO (now Gartner) — Account selection — Score accounts on fit first, then layer intent and engagement to decide tier; fit without intent is a nurture list, not a target list.
  • Winning by Design — ICP — Define the ideal customer profile from closed-won customers who retain and expand, not from the largest logos.
  • ITSMA — Account-Based Marketing — Tier-based ABM (1:1 / 1:few / 1:many); measure pipeline from target accounts, not lead volume.
  • Lars Nilsson — Account-based sales development (ABSD) — Select and sequence accounts by buying signals, not firmographics alone; the account list is a living ranking, not an annual export.

When to Use

  • "Which accounts should we target?"
  • "Tier our target account list"
  • "Build an account scoring model"
  • "Prioritize our TAM for ABM"

Step-by-Step Process

Phase 1: Build Scoring Model

Weight each dimension (must total 100 points):

Firmographic Fit (35 pts):

  • Industry match (0-10): Direct ICP industry = 10, adjacent = 5, outside = 2
  • Employee count (0-10): In sweet spot = 10, near = 5, outside = 2
  • Revenue range (0-10): In sweet spot = 10, near = 5, outside = 2
  • Geography (0-5): Target region = 5, acceptable = 3, outside = 1

Technographic Fit (25 pts):

  • Current tech stack (0-15): Complementary tools present = 15, some = 8, none = 2
  • Integration potential (0-10): High integration value = 10, medium = 5, low = 2

Behavioral Signals (25 pts):

  • Website visits (0-10): Frequent, multi-page = 10, some = 5, none = 2
  • Content engagement (0-10): Downloads, webinar attendance, newsletter = score by recency
  • Social engagement (0-5): LinkedIn follows, comments, shares

Intent & Trigger (15 pts):

  • Hiring signals (0-5): Role related to your product = 5
  • Funding event (0-5): Recent raise = 5
  • Tech change (0-5): Evidence of switching or evaluating

Phase 2: Tier Assignment

  • Tier 1 (>80): 5-15 accounts. Immediate sales + marketing investment.
  • Tier 2 (60-79): 15-50 accounts. Scaled ABM plays.
  • Tier 3 (40-59): 50-200 accounts. Programmatic nurture.
  • Below 40: Not ABM. Generic inbound/outbound only.

Phase 3: Ongoing Refinement

  • Idle whitespace pull (ABSD): filter high-fit prospects with zero prior business and zero open pipeline — that list is the targeted-outbound campaign queue (references/lars-nilsson-absd.md)
  • Monthly: Re-score based on new signals
  • Quarterly: Review tier assignments, promote/demote accounts
  • After wins: Analyze what made winners score high — adjust weights
  • After losses: Add disqualifying signals discovered in lost deals

Output Format

Scored account list with tier assignments, scoring model documentation, and a refinement calendar.

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.

Execution Artifacts

  • references/framework-notes.md — named frameworks, citation anchors, and operating assumptions
  • templates/output-template.md — copy-paste deliverable structure for the user
  • scripts/check-output.py — local checklist validator for required sections This skill includes lightweight artifacts the agent can load on demand: Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.

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:

  • TOPO Account Selection: apply only the part that directly improves the requested deliverable.
  • WbD ICP Framework: apply only the part that directly improves the requested deliverable.
  • ITSMA — Account-Based Marketing: apply only the part that directly improves the requested deliverable.
  • Lars Nilsson ABSD signal-based selection: 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.

Related Skills

  • icp-scoring, abm-strategy, signal-scoring, lead-finding, list-building

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 Account Selection AI skill do?

Builds and tiers a target account list: fit and intent scoring models, tier assignment, and TAM segmentation. Use when the user needs to decide which accounts to target, score or tier an account list, or prioritize a TAM for ABM or outbound. Triggers on: "account selection", "target account list", "tier accounts", "prioritize accounts", "TAM segmentation".

Why use Account Selection on TypingMind?

Because you install it once and use it with any model. Account Selection 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 Account Selection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeadMagic/gtm-skills/tree/main/skills/abm/account-selection. 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 Account Selection?

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 Account Selection?

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

Is the Account Selection 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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