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Customer Success Management

CommunityPopular
cbrock84
customer-success-management

Runs the ongoing relationship with accounts after the sale — segmenting coverage against account value, building a health score that predicts rather than describes, running reviews customers find worth attending, forecasting renewals honestly, and finding expansion that follows usage instead of quota. Use this to design a customer success motion, decide who gets a named contact, work out why renewals surprise you, or fix a health score everyone ignores.

Overview

Publishercbrock84
Repositoryheadcount
Skill namecustomer-success-management
Stars
1.6K
Forks
237
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Customer Success Management 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/customer-experience/skills/customer-success-management .claude/skills/customer-success-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Success Management 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 Customer Success Management 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 Customer Success Management 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.

Customer success management

This is the motion that keeps accounts, as distinct from diagnosing why they leave — for the churn analysis itself, see revenue:retention. The failure this discipline exists to prevent is finding out at renewal.

Segment coverage before hiring anyone

Coverage is a cost decision and it should be made explicitly rather than by whoever shouts.

  • Named coverage for accounts where the revenue justifies a person and the relationship is genuinely complex. Fewer accounts per person than instinct suggests — a manager with sixty accounts is running a queue, not a relationship.
  • Pooled coverage for the middle: a team owning a segment, working from signals rather than from a calendar.
  • Programmatic coverage for the long tail: in-product guidance, lifecycle messaging, and self-service. This is not a lesser tier, it is the only one that scales, and it usually deserves more investment than it gets.

Assign by what the account needs, not only by what it pays. A large account that is live, stable and happy needs less than a small one mid-implementation.

Build a health score that predicts something

Most health scores are a weighted average of whatever was available, colored red to green, and trusted by nobody. A useful one is built backwards: take accounts that churned and accounts that renewed, and find what actually differed six months out.

  • Usage depth and breadth — how many people, how often, how many of the things they bought.
  • Trajectory over level. An account at 60% of expected usage and rising is healthier than one at 90% and falling. Level tells you where they are; direction tells you where they are going.
  • Relationship coverage — how many people you know, and whether your only contact is the person who bought.
  • Support and escalation history, weighted by severity rather than volume.

Validate it against outcomes and recalibrate. A score that did not predict last year's churn should not be steering this year's attention.

The single-threaded account is the most common avoidable loss

When one person is your entire relationship, their departure is your renewal risk, and it arrives with no warning. Track how many contacts each account has and treat single-threading as an actionable condition rather than a fact of life.

Make reviews worth the customer's hour

A business review that presents usage statistics back to the customer wastes both parties' time. The ones people attend cover what they set out to achieve, where they actually are against it, what is in the way, and what changes next — with the customer talking for at least half of it.

Frequency should follow value and risk, not a uniform quarterly cadence applied to everyone.

Forecast renewals like a pipeline, because that is what it is

Renewal forecasting is more predictable than new business and is often done worse, because everything is assumed to renew until it does not.

Start the renewal conversation far enough ahead that a problem is still fixable — for an annual contract that is months, not weeks. Track renewals in stages with entry criteria, and separate gross retention from net so expansion cannot mask a leak underneath it.

Auto-renewal is a billing mechanism, not a relationship. An account that auto-renewed while disengaged is next year's churn with a delay.

Expansion follows usage, not quota

The credible expansion conversation comes from something observable: they hit a limit, adopted the thing that leads to the next thing, added a team. Expansion pushed on a quota calendar into an account that has not realized its original purchase is how a renewal gets lost while chasing a smaller number.

Tooling

Customer success platforms: Gainsight, Totango, ChurnZero, Vitally, Planhat, and similar. They earn their cost once you have more accounts than a person can hold in their head and product usage data worth joining to the account record — before that, the CRM plus a usage query does the job.

Renewal and expansion tracking belongs in the CRM alongside new business, not in a separate system, or the forecast will exist twice and disagree.

Never

  • Run a health score nobody has validated against actual outcomes.
  • Let an account stay single-threaded without naming it as a risk.
  • Open the renewal conversation inside the notice period.
  • Report net retention without gross retention beside it.

Frequently asked questions

What does the Customer Success Management AI skill do?

Runs the ongoing relationship with accounts after the sale — segmenting coverage against account value, building a health score that predicts rather than describes, running reviews customers find worth attending, forecasting renewals honestly, and finding expansion that follows usage instead of quota. Use this to design a customer success motion, decide who gets a named contact, work out why renewals surprise you, or fix a health score everyone ignores.

Why use Customer Success Management on TypingMind?

Because you install it once and use it with any model. Customer Success Management 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 Customer Success Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/customer-experience/skills/customer-success-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Customer Success Management?

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 Customer Success Management?

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

Is the Customer Success Management 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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