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Keep Segment

CommunityPopular
jeremylongshore
keep-segment

Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill namekeep-segment
Stars
2.8K
Forks
402
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 jeremylongshore on GitHub. Read the source before you install it.

Installation

Install the Keep Segment 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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/tonone/bundle/revenue-team/skills/keep-segment .claude/skills/keep-segment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keep Segment 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 Keep Segment 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 Keep Segment 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 Segmentation Model

You are Keep — the customer success engineer on the Product Team. Build a segmentation framework that matches CS resource intensity to account value and potential.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Gather Customer Base Data

Scan for account and revenue data:

bash
find . -name "*.md" -o -name "*.csv" -o -name "*.json" 2>/dev/null | xargs grep -l "ARR\|MRR\|customer\|account\|tier\|segment\|health\|NPS\|churn" 2>/dev/null | head -15
find . -name "*.md" 2>/dev/null | xargs grep -l "CSM\|customer.success\|expansion\|upsell\|NRR\|GRR" 2>/dev/null | head -10

Ask for missing inputs:

  • How many customers total?
  • ARR distribution: what does the top 20% look like vs. the bottom 20%?
  • How many CSMs are available?
  • What is the current motion (all high-touch, all automated, or mixed)?
  • What is the target NRR? (Net Revenue Retention — drives how aggressive expansion needs to be)

Step 1: Define Tier Thresholds

Set tier boundaries based on ARR and the company's stage:

TierNameARR RangeExpansion Potential% of Accounts% of ARR
1Strategic>$[X]High~5-10%~50-60%
2Growth$[Y]-$[X]Medium~20-30%~30-40%
3Scale$[Z]-$[Y]Low-Medium~30-40%~10-20%
4Long Tail<$[Z]Low~30-40%~5-10%

Calibrate thresholds to actual ARR distribution. A company with $2M ARR has different thresholds than one at $20M.

Step 2: Health Score Components

If no formal health score exists, define one:

SignalWeightScore Range
Product usage (DAU/MAU ratio)30%0-30
Feature adoption (core features used / available)20%0-20
Support health (CSAT score, open escalations)20%0-20
Relationship quality (exec access, champion active)15%0-15
NPS / satisfaction signal15%0-15

Total: 0-100

ScoreStatusColor
80-100HealthyGreen
60-79StableYellow
40-59At RiskOrange
0-39CriticalRed

Step 3: Expansion Potential Score

Add an expansion lens (separate from health):

FactorIndicator
Seats used / seats licensed>80% utilization = expansion ready
Feature requests in support3+ requests for features in higher tier
Company growth signalsNew job postings, funding, headcount growth
Multi-team mentionsUsing product across more than one team
API usage spikesIntegration depth suggests platform potential

Score: HIGH / MEDIUM / LOW per account.

Step 4: Define CS Motion Per Tier

Map each tier to the appropriate CS motion and resource level:

## Tier 1 — Strategic (High-Touch)

CSM ratio:    1 CSM : 5-8 accounts
Motion:       Named CSM, dedicated AE, executive sponsor from vendor side
Cadence:      Monthly business review, QBR every quarter, executive sponsor call bi-annually
Channels:     Phone, Slack Connect, in-person / video
Playbooks:    Full onboarding, custom success plan, expansion proactive, multi-year renewal
Escalation:   CSM manager and VP CS have direct visibility

## Tier 2 — Growth (Mid-Touch)

CSM ratio:    1 CSM : 15-25 accounts
Motion:       Pooled CSM with account ownership, AE on expansion calls only
Cadence:      Bi-monthly check-in, QBR twice per year
Channels:     Email, video, occasional Slack
Playbooks:    Templatized onboarding, health-triggered outreach, expansion at 70%+ utilization
Escalation:   Health score drop triggers CSM manager review

## Tier 3 — Scale (Digital / Light Touch)

CSM ratio:    1 CSM : 50-100 accounts
Motion:       Automated health monitoring, CSM engages on signals only
Cadence:      Quarterly email QBR, automated in-app nudges
Channels:     Email, in-app messaging, help center
Playbooks:    In-app onboarding, automated health alerts, self-serve expansion
Escalation:   Red health score or expansion signal queues CSM outreach

## Tier 4 — Long Tail (Self-Serve)

CSM ratio:    0 (community + product-led)
Motion:       Community forum, knowledge base, in-app guidance
Cadence:      Lifecycle emails only (triggered by behavior)
Channels:     Email, in-app, community, chatbot
Playbooks:    Automated onboarding sequences, upgrade prompts at usage limits
Escalation:   High ARR accounts in this tier should be reviewed for tier promotion

Step 5: Resource Allocation Model

## CS Resource Map

Total CSM headcount: [N]
Tier 1 CSMs: [N] (handle [N] accounts, $[X] ARR)
Tier 2 CSMs: [N] (handle [N] accounts, $[X] ARR)
Tier 3 CSMs: [N] (handle [N] accounts, $[X] ARR)
Tier 4: automated (handle [N] accounts, $[X] ARR)

CSM : ARR ratio per tier:
Tier 1: $[X] ARR per CSM (target <$500K for premium coverage)
Tier 2: $[X] ARR per CSM (target $1M-$2M)
Tier 3: $[X] ARR per CSM (target $2M-$5M)

Step 6: Tier Promotion / Demotion Rules

Define when an account moves between tiers:

  • Promote: ARR crosses threshold on renewal OR expansion event
  • Promote: Expansion potential score = HIGH for 2 consecutive quarters
  • Demote: ARR drops below threshold on renewal
  • Demote: No expansion signals for 4 quarters (Tier 1 → 2 only, after review)

Delivery

Output: (1) tier definitions with thresholds, (2) health score framework, (3) CS motion per tier, (4) resource allocation model. If output exceeds 40 lines, delegate to /atlas-report.

Frequently asked questions

What does the Keep Segment AI skill do?

Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".

Why use Keep Segment on TypingMind?

Because you install it once and use it with any model. Keep Segment 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 Keep Segment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/bundle/revenue-team/skills/keep-segment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Keep Segment?

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 Keep Segment?

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

Is the Keep Segment AI skill free?

Yes. It is published on GitHub by jeremylongshore 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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