agentsclimarketplace

Health drop diagnosis

Skill quivly/skills/customer-engineering/health-drop-diagnosis

Production-ready Agent Skills for Customer Engineering, Post-Sales, and Customer Success teams. Quivly Skills is a curated open-source collection of reusable skills that give AI agents deep expertise in customer engineering workflows. Every skill follows the official Agent Skills specification.

Install
npx -y skills add quivly/skills --skill health-drop-diagnosis

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Explains a health score drop — decomposes which score components moved, finds the triggering events, and recommends a proportionate response. Use when a health score fell, a risk alert fired, or someone asks "why did X's health drop".

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SKILL.md

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Health Drop Diagnosis

You explain a health score movement in plain language: which inputs moved, what real-world events drove them, and how worried to be.

Core principle: a health score is a smoke detector, not a diagnosis — never recommend action from the score alone; trace it to real events first.

Workflow

  1. Get the current health score with its component breakdown (get-health-score). Identify which components dropped and by how much — the composite hides the story.
  2. For each falling component, find the underlying event:
    • Usage component → pull usage detail (get-usage)
    • Support component → recent tickets (search-tickets)
    • Engagement component → conversation recency and tone (search-conversations)
  3. Check signals on record (get-insights) for events the score may lag: champion departure, funding news, org changes.
  4. Judge severity honestly:
    • Mechanical — one input crossed a threshold, no real-world change (e.g., a holiday week tanked logins)
    • Early warning — real deterioration, early stage, recoverable
    • Confirmed risk — multiple components falling together with corroborating events

Output Format

Health drop: {Customer} — score before → after, severity verdict

What moved — component-by-component, each mapped to its real-world cause

The story — 2-3 sentences of narrative: what is actually happening at this account

Response — proportionate to severity: nothing (mechanical), targeted play (early warning), or escalate to save-plan mode (confirmed)

Guidelines

  • A score is a smoke detector, not a diagnosis — always trace to real events before recommending action.
  • Call out mechanical drops confidently; false alarms erode trust in the whole scoring system.
  • If severity is "confirmed risk", recommend running a churn save plan rather than duplicating one here.

Related skills: usage component is the driver → usage-drop-investigation; confirmed risk → churn-save-plan; full structured review → customer-health-review.

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