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Chief Customer Officer Advisor

Advises on retention decomposition, customer segmentation, CS team coverage and CS hiring sequence

Source Download skill
Repository
alirezarezvani/claude-skills
Stars
29,149
Licence
MIT
Files
8 files

What it does

This skill gives an AI agent a strategic playbook for customer success leadership at startups. When asked about retention, segmentation, customer success team sizing or which customer success role to hire next, the agent works through a fixed framework: it checks gross retention before net retention, splits churn into root-cause categories, sorts customers into investment tiers with an ICP fit score, and compares pooled versus named account manager coverage. It can also run included Python scripts on sample or user-supplied JSON data to produce numbers instead of guesses.

Great for

  • Startup founders or early-stage leaders without a Chief Customer Officer who need a structured view of retention
  • Customer success leaders deciding how to segment accounts and how many CSMs to hire
  • Teams trying to understand whether a high net revenue retention figure hides churn

Skip it if

  • Tactical CRM, health-score tooling, NPS survey setup or onboarding automation, which the skill says belong to other skills (the agent should not rely on it for these)
  • Large enterprises with different benchmarks, since the thresholds are described as B2B SaaS startup baselines
  • Making real firing or headcount decisions from sample outputs alone, because the ratios and thresholds are generic and need validation against your own data

Try asking

  • “Our net revenue retention is 115%. Break it down into gross retention, contraction and expansion and tell me if we have a churn problem.”
  • “Here is my customer list as JSON. Segment them into tiers and flag which accounts fall below the investment floor.”
  • “We have 350 customers and 4 CSMs. Should we move to pooled coverage or named CSMs?”
  • “We are at 20 employees with $3M ARR. What customer success role should we hire next?”
Step-by-step guide: how to use Chief Customer Officer AdvisorInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 8 original files, the MIT licence and an attribution note.

    chief-customer-officer-advisor.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip chief-customer-officer-advisor.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip chief-customer-officer-advisor.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Provide your own cohort or customer JSON rather than relying on the embedded sample data, so the outputs reflect your business.
  • Ask for gross retention first; the skill is designed to surface churn hidden by expansion revenue.
  • Treat tier thresholds and coverage ratios as starting points and validate them against your own segment economics.

What's inside

  • references/cs_coverage_model.md
  • references/cs_team_org_evolution.md
  • references/customer_segmentation_strategy.md
  • references/retention_decomposition.md
  • scripts/cs_coverage_calculator.py
  • scripts/customer_segmentation_designer.py
  • scripts/retention_decomposition_analyzer.py
  • SKILL.md

Redistributed unmodified from alirezarezvani/claude-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.