cs-health-scorecard

Category: Design Risk: High risk ★ 4.6 · Rating 4.6/5 (1014) mohitagw15856/pm-claude-skills MIT

Rating is derived from the repo's GitHub stars and shown for reference.

shell_execution

name: cs-health-scorecard
description: "Build a customer health scorecard for a specific account. Use when asked to score account health, assess renewal risk, build a health dashboard, or evaluate an account's likelihood to renew or expand. Produces a structured health scorecard with a RAG status, dimension scores, key risks, and recommended actions."

Customer Health Scorecard Skill

Produce a structured, data-driven health scorecard for a customer account — giving the CSM and leadership a clear view of renewal risk, expansion potential, and the actions needed to move the account in the right direction.

Reads from / Writes to the Brain

If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:

Read first: the account's entities/ file, its stakeholders/ (champion, economic buyer, detractors), and knowledge/. Run python3 ../professional-brain/scripts/brain_query.py ./brain "<account name>" and carry each fact's provenance tag through.
📥 Propose to the Brain: after producing, propose recording the health verdict + key risks to the account entities/ file, and a renewal-risk entry to decisions/ if a call is made, each provenance-tagged. Show them, get a yes, then write with ../professional-brain/scripts/brain_write.py … --commit (append-only, dry-run by default).

Required Inputs

Ask for these if not already provided:

Account name and tier (enterprise / mid-market / SMB)
Contract value (ARR) and renewal date
Product usage data — logins, DAU/MAU ratio, key feature adoption
Support data — open tickets, CSAT or NPS score, recent escalations
Engagement data — last QBR date, executive sponsor status, champion name
Commercial data — payment history, expansion conversations, seats used vs. licensed
Any known risks or recent changes at the account

Scoring Framework

Score each dimension 1–5. Weight as shown. Calculate weighted total out of 100.

Dimension	Weight	What to Score
Product Adoption	30%	DAU/MAU ratio, breadth of features used, power users identified
Engagement	20%	QBR cadence, executive sponsor active, champion strength
Outcomes	20%	Customer hitting their stated goals / success metrics
Support Health	15%	Ticket volume trend, unresolved escalations, CSAT
Commercial	15%	On-time payments, seats utilised, expansion signals

Score → RAG conversion:

80–100: Green (healthy, renew likely)
60–79: Amber (at risk, needs attention)
0–59: Red (high churn risk, escalate)

Programmatic Helper

This skill ships with a stdlib-only Python script that applies the weights above and converts the weighted total to a RAG status — so the headline score is computed identically every time and weights always sum to 100%.

# Five scores 1-5 in order: adoption engagement outcomes support commercial
python3 scripts/health_score.py --scores 4 3 4 2 5 --account "Acme Corp"

# Or from JSON (lets you override the default weights per account/segment)
python3 scripts/health_score.py --input account.json

It returns the per-dimension weighted points, the total out of 100, and the RAG band (Green ≥80, Amber 60–79, Red <60) with a one-line next step. Run it to set the headline number, then write the dimension detail and actions below around it. Add --json for downstream tooling.

Output Format

Customer Health Scorecard: [Account Name]

CSM: [Name] | Tier: [Enterprise / Mid-Market / SMB]
ARR: £/$/€[X] | Renewal date: [Date] | Days to renewal: [N]
Overall health: [Green / Amber / Red] — [Score]/100
Last updated: [Date]

Health Score Summary

Dimension	Score (1–5)	Weight	Weighted Score	Trend
Product Adoption	[1–5]	30%	[X]	↑ / → / ↓
Engagement	[1–5]	20%	[X]	↑ / → / ↓
Outcomes	[1–5]	20%	[X]	↑ / → / ↓
Support Health	[1–5]	15%	[X]	↑ / → / ↓
Commercial	[1–5]	15%	[X]	↑ / → / ↓
Total	—	100%	[X]/100

Dimension Detail

Product Adoption — [Score]/5

DAU/MAU ratio: [X]% (benchmark: >25% = healthy)
Key features adopted: [List features in use]
Features not adopted: [List unused high-value features]
Power users identified: [Yes / No — how many]
Assessment: [1–2 sentences on adoption health]

Engagement — [Score]/5

Last QBR: [Date] — [Outcome summary]
Next QBR: [Scheduled / Overdue]
Executive sponsor: [Active / Passive / Vacant]
Champion: [Name, role, strength: strong / moderate / weak]
Assessment: [1–2 sentences]

Outcomes — [Score]/5

Customer's stated goals: [List 2–3 goals from onboarding or last QBR]
Progress against goals: [On track / Partial / Off track]
Evidence of value: [Metric or quote that demonstrates ROI]
Assessment: [1–2 sentences]

Support Health — [Score]/5

Open tickets: [N] (priority breakdown: P1: X, P2: X, P3: X)
CSAT / NPS: [Score] (benchmark: >8 CSAT / >30 NPS = healthy)
Unresolved escalations: [Yes / No — details if yes]
Ticket trend (last 90 days): Increasing / Stable / Decreasing
Assessment: [1–2 sentences]

Commercial — [Score]/5

Seats licensed: [N] | Seats active: [N] ([X]% utilisation)
Payment history: [On time / Late — details]
Expansion signals: [Yes — describe / No]
Downgrade or cancellation signals: [Yes — describe / No]
Assessment: [1–2 sentences]

Top Risks

Risk	Severity	Mitigation
[Risk description]	High / Medium / Low	[Specific action to mitigate]

Recommended Actions

Immediate (this week):

[Action — owner — deadline]

This month:

[Action — owner — deadline]

Before renewal:

[Action — owner — deadline]

Renewal Forecast

Scenario	Probability	ARR at risk
Full renewal at current ARR	[X]%	£/$/€0
Renewal with contraction	[X]%	£/$/€[X]
Churn	[X]%	£/$/€[full ARR]

Recommended renewal play: [Expand / Hold / Save / Manage out]

Quality Checks

Score is based on data, not gut feel — each dimension has evidence
Risks are specific (not "low engagement" — something like "executive sponsor left in March, no replacement identified")
Actions have owners and deadlines
Renewal probability is calibrated against pipeline reality
Trend arrows reflect direction of change vs. last scorecard, not just current state

Anti-Patterns

Do not score health dimensions on gut feel — every score needs specific supporting evidence
Do not give a Green status to accounts with unresolved P1 issues or missed milestones
Do not list risks vaguely — "low engagement" without specifics is not actionable
Do not leave recommended actions without named owners and deadlines
Do not conflate product usage frequency with product value delivery