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Account Based Marketing

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cbrock84
account-based-marketing

Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.

Overview

Publishercbrock84
Repositoryheadcount
Skill nameaccount-based-marketing
Stars
1.6K
Forks
237
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by cbrock84 on GitHub. Read the source before you install it.

Installation

Install the Account Based Marketing 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.
  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/cbrock84/headcount.git /tmp/headcount
mkdir -p .claude/skills
cp -r /tmp/headcount/plugins/demand-generation/skills/account-based-marketing .claude/skills/account-based-marketing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Account Based Marketing 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 Based Marketing 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 Based Marketing 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-based marketing

Account-based marketing inverts the usual model: instead of generating leads and finding out which accounts they came from, you choose the accounts and work them. It is a good fit for a narrow set of businesses and an expensive mistake for the rest.

Qualify the model before adopting it

It works when deal sizes are large enough to justify per-account effort, the addressable market is small enough to enumerate, buying groups have several people, and sales cycles are long enough for sustained effort to compound.

It does not work when the deal size cannot carry the cost, when the market is too large to name, or when marketing and sales will not actually coordinate. That last one is the usual failure: the tooling gets bought, the account list gets built, and the program becomes a more expensive way to run the same campaigns.

Run the arithmetic first. Total program cost divided by the number of accounts, against expected deal value and a realistic win rate, tells you the tier structure you can afford — or that you cannot afford this at all.

Build the list from fit and evidence, then hold it still

Start from the accounts that already look like your best customers — not by revenue but by why they bought and whether they stayed. Add observable signals: hiring, technology in use, funding, regulatory pressure, a change in leadership.

Then commit. A list that churns quarterly cannot compound, and compounding is the only reason this model beats broad demand generation. Agree the list with sales and get their explicit acceptance, because a list sales does not believe in is a list sales will not work.

Map the buying group, not the contact

Purchases at this size are made by several people with different concerns: the person with the problem, the person with the budget, the person who will operate it, and whoever can veto on security, legal or procurement grounds.

Reaching one champion and mistaking that for account coverage is the most common structural error. Track how many roles you have reached within each account, and treat single-threading as a status that needs fixing rather than a warning to note.

Tier the effort, because one-to-one does not scale

  • One-to-one for a small number of the highest-value accounts: genuinely bespoke research and content, executive engagement.
  • One-to-few for clusters that share an industry or a problem: shared narrative, light customization per account.
  • One-to-many for the rest of the named list: programmatic personalization at segment level.

Being honest about which tier an account is in prevents the common outcome where everything is nominally one-to-one and nothing is actually customized.

Coordinate, or the account experiences three campaigns

The account should see one coherent effort. That requires marketing and sales working the same plan, with agreed timing, agreed messaging, and an agreed sequence of who reaches out when.

Set up a shared cadence between the two teams for the tiered accounts and hold it. Without that meeting the program degrades into marketing sending things and sales prospecting separately, which is the status quo with extra software.

Measure account progression, not lead volume

Lead counts are the wrong instrument. What matters is how accounts move: coverage of the buying group, engagement across it rather than by one person, movement into and through pipeline, and eventually win rate and deal size against non-target accounts.

Expect the timeline to be long. Judging an account-based program on a quarter is judging it before any of its mechanism has had time to work, and canceling it there is the most common way the investment is wasted entirely.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Adopt the model without checking whether the deal size carries the per-account cost.
  • Run a target list sales has not explicitly accepted.
  • Treat one engaged champion as account coverage.
  • Judge the program on lead volume, or on a single quarter.

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 Based Marketing AI skill do?

Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.

Why use Account Based Marketing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/demand-generation/skills/account-based-marketing. 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 Based Marketing?

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 Based Marketing?

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

Is the Account Based Marketing AI skill free?

Yes. It is published on GitHub by cbrock84 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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