mcpField
C
customer-success-health-review

customer-success-health-review

github.com/sylphxaiMarketing & growth147 installsUpdated today
Repository

About customer-success-health-review

A skill for producing structured customer-account health reviews for product operations and customer success teams. It separates adoption, outcomes, sentiment, support burden, commercial risk, and data quality, then connects health states to actions.

Key features

  1. 01Account context checklist
  2. 02Separate health dimensions
  3. 03Action-oriented health scoring
  4. 04Account states and playbooks
  5. 05Review cadence planning

Use cases

  1. 01Preparing renewal-risk reviews
  2. 02Designing account health models
  3. 03Planning customer-success actions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sylphxai/skills --skill customer-success-health-review

After installing, type/customer-success-health-reviewin Claude Code to confirm it loaded.

Bundled filesagents/openai.yamlreferences/customer-health-patterns.md

Details

From the project README

Customer Success Health Review

Use this skill to turn a recurring product-operations problem into a concrete, reviewable artifact instead of generic advice.

Workflow

  1. Identify account segment, buyer, users, promised outcome, adoption milestones, renewal date, support history, and expansion path.
  2. Read references/customer-health-patterns.md.
  3. Separate leading indicators, lagging outcomes, sentiment, support burden, commercial risk, and data quality.
  4. Design a health score that maps to actions, not just dashboard colors.
  5. Produce account states, playbooks, events, and review cadence.

When not to use

  • Do not use for generic advice the base model already handles without this skill's specific artifact contract.

Guardrails

  • Do not treat raw usage volume as health without outcome context.
  • Do not hide missing data behind a confident score.
  • Keep renewal risk, product adoption, and expansion readiness separate dimensions.
  • Avoid manipulative save tactics; success motions should restore value.

Output format

Customer/account context:
Outcome promised:

Health model:
| Dimension | Signal | Weight | Data quality | Action |
| --- | --- | --- | --- | --- |

Account state and playbook:
- <state> -> <owner/action/message/proof>

Metrics/events:
- <event or review cadence>

Comments

Markdown supported