Claude
760f979365
Five improvements to position the library as a serious engineering project: 1. Cross-tool compatibility — new README "Works With" section honestly documenting where skills run (Claude Code natively; SKILL.md bodies port to other agents and chat LLMs as system prompts). 2. Python helper scripts (stdlib-only) for the three strongest skills: - sprint-planning: capacity_calculator.py (recommended commitment) - rice-prioritisation: rice_calculator.py (ranks, flags quick wins/moonshots) - cs-health-scorecard: health_score.py (weighted total + RAG) Each is wired into its SKILL.md and synced to the plugin copies. 3. Explicit skill tiering — TIERS.md + README section marking 46 Production-Ready skills and calling out Experimental (external-dependency) ones; everything else is Stable. 4. Repository hygiene — new CHANGELOG.md (Keep a Changelog format) and SKILL-AUTHORING-STANDARD.md; refreshed SECURITY.md version table and helper-script disclosure; added .gitignore. 5. Related Projects — README section linking to alirezarezvani/claude-skills and the major awesome-claude-skills / awesome-claude-code lists. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
164 lines
6.3 KiB
Markdown
164 lines
6.3 KiB
Markdown
---
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name: cs-health-scorecard
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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."
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---
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# Customer Health Scorecard Skill
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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.
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## Required Inputs
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Ask for these if not already provided:
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- **Account name** and tier (enterprise / mid-market / SMB)
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- **Contract value** (ARR) and **renewal date**
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- **Product usage data** — logins, DAU/MAU ratio, key feature adoption
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- **Support data** — open tickets, CSAT or NPS score, recent escalations
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- **Engagement data** — last QBR date, executive sponsor status, champion name
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- **Commercial data** — payment history, expansion conversations, seats used vs. licensed
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- **Any known risks or recent changes** at the account
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## Scoring Framework
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Score each dimension 1–5. Weight as shown. Calculate weighted total out of 100.
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| Dimension | Weight | What to Score |
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|---|---|---|
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| **Product Adoption** | 30% | DAU/MAU ratio, breadth of features used, power users identified |
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| **Engagement** | 20% | QBR cadence, executive sponsor active, champion strength |
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| **Outcomes** | 20% | Customer hitting their stated goals / success metrics |
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| **Support Health** | 15% | Ticket volume trend, unresolved escalations, CSAT |
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| **Commercial** | 15% | On-time payments, seats utilised, expansion signals |
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**Score → RAG conversion:**
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- 80–100: Green (healthy, renew likely)
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- 60–79: Amber (at risk, needs attention)
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- 0–59: Red (high churn risk, escalate)
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## Programmatic Helper
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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%.
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```bash
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# Five scores 1-5 in order: adoption engagement outcomes support commercial
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python3 scripts/health_score.py --scores 4 3 4 2 5 --account "Acme Corp"
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# Or from JSON (lets you override the default weights per account/segment)
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python3 scripts/health_score.py --input account.json
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```
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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.
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## Output Format
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---
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# Customer Health Scorecard: [Account Name]
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**CSM:** [Name] | **Tier:** [Enterprise / Mid-Market / SMB]
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**ARR:** £/$/€[X] | **Renewal date:** [Date] | **Days to renewal:** [N]
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**Overall health:** [Green / Amber / Red] — [Score]/100
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**Last updated:** [Date]
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---
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## Health Score Summary
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| Dimension | Score (1–5) | Weight | Weighted Score | Trend |
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|---|---|---|---|---|
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| Product Adoption | [1–5] | 30% | [X] | ↑ / → / ↓ |
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| Engagement | [1–5] | 20% | [X] | ↑ / → / ↓ |
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| Outcomes | [1–5] | 20% | [X] | ↑ / → / ↓ |
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| Support Health | [1–5] | 15% | [X] | ↑ / → / ↓ |
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| Commercial | [1–5] | 15% | [X] | ↑ / → / ↓ |
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| **Total** | — | 100% | **[X]/100** | |
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---
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## Dimension Detail
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### Product Adoption — [Score]/5
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- **DAU/MAU ratio:** [X]% (benchmark: >25% = healthy)
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- **Key features adopted:** [List features in use]
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- **Features not adopted:** [List unused high-value features]
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- **Power users identified:** [Yes / No — how many]
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- **Assessment:** [1–2 sentences on adoption health]
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### Engagement — [Score]/5
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- **Last QBR:** [Date] — [Outcome summary]
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- **Next QBR:** [Scheduled / Overdue]
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- **Executive sponsor:** [Active / Passive / Vacant]
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- **Champion:** [Name, role, strength: strong / moderate / weak]
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- **Assessment:** [1–2 sentences]
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### Outcomes — [Score]/5
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- **Customer's stated goals:** [List 2–3 goals from onboarding or last QBR]
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- **Progress against goals:** [On track / Partial / Off track]
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- **Evidence of value:** [Metric or quote that demonstrates ROI]
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- **Assessment:** [1–2 sentences]
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### Support Health — [Score]/5
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- **Open tickets:** [N] (priority breakdown: P1: X, P2: X, P3: X)
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- **CSAT / NPS:** [Score] (benchmark: >8 CSAT / >30 NPS = healthy)
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- **Unresolved escalations:** [Yes / No — details if yes]
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- **Ticket trend (last 90 days):** Increasing / Stable / Decreasing
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- **Assessment:** [1–2 sentences]
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### Commercial — [Score]/5
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- **Seats licensed:** [N] | **Seats active:** [N] ([X]% utilisation)
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- **Payment history:** [On time / Late — details]
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- **Expansion signals:** [Yes — describe / No]
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- **Downgrade or cancellation signals:** [Yes — describe / No]
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- **Assessment:** [1–2 sentences]
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---
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## Top Risks
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| Risk | Severity | Mitigation |
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|---|---|---|
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| [Risk description] | High / Medium / Low | [Specific action to mitigate] |
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---
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## Recommended Actions
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**Immediate (this week):**
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1. [Action — owner — deadline]
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**This month:**
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1. [Action — owner — deadline]
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**Before renewal:**
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1. [Action — owner — deadline]
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---
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## Renewal Forecast
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| Scenario | Probability | ARR at risk |
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|---|---|---|
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| Full renewal at current ARR | [X]% | £/$/€0 |
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| Renewal with contraction | [X]% | £/$/€[X] |
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| Churn | [X]% | £/$/€[full ARR] |
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**Recommended renewal play:** [Expand / Hold / Save / Manage out]
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---
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## Quality Checks
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- [ ] Score is based on data, not gut feel — each dimension has evidence
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- [ ] Risks are specific (not "low engagement" — something like "executive sponsor left in March, no replacement identified")
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- [ ] Actions have owners and deadlines
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- [ ] Renewal probability is calibrated against pipeline reality
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- [ ] Trend arrows reflect direction of change vs. last scorecard, not just current state
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## Anti-Patterns
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- [ ] Do not score health dimensions on gut feel — every score needs specific supporting evidence
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- [ ] Do not give a Green status to accounts with unresolved P1 issues or missed milestones
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- [ ] Do not list risks vaguely — "low engagement" without specifics is not actionable
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- [ ] Do not leave recommended actions without named owners and deadlines
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- [ ] Do not conflate product usage frequency with product value delivery
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