Eight dimensions. Traffic lights. Real benchmarks. Surfaces the problems you don't know you have.
org health, organizational health, health diagnostic, health dashboard, health check, company health, functional health, team health, startup health, health scorecard, health assessment, risk dashboard
python scripts/health_scorer.py # Guided CLI โ enter metrics, get scored dashboard
python scripts/health_scorer.py --json # Output raw JSON for integration
Or describe your metrics:
/health [paste your key metrics or answer prompts]
/health:dimension [financial|revenue|product|engineering|people|ops|security|market]
What it measures: Can we fund operations and invest in growth?
Key metrics:
What it measures: Are customers staying, growing, and recommending us?
Key metrics:
What it measures: Do customers love and use the product?
Key metrics:
What it measures: Can we ship reliably and sustain velocity?
Key metrics:
What it measures: Is the team stable, engaged, and growing?
Key metrics:
What it measures: Are we executing our strategy with discipline?
Key metrics:
What it measures: Are we protecting customers and maintaining compliance?
Key metrics:
What it measures: Are we winning in the market and growing efficiently?
Key metrics:
Each dimension is scored 1-10 with traffic light:
Overall Health Score:
Weighted average by company stage (see references/health-benchmarks.md for weights).
| If this dimension is red... | Watch these dimensions next |
|---|---|
| Financial Health | People (freeze hiring) โ Engineering (freeze infra) โ Product (cut scope) |
| Revenue Health | Financial (cash gap) โ People (attrition risk) โ Market (lose positioning) |
| People Health | Engineering (velocity drops) โ Product (quality drops) โ Revenue (churn rises) |
| Engineering Health | Product (features slip) โ Revenue (deals stall on product) |
| Product Health | Revenue (NRR drops, churn rises) โ Market (CAC rises; referrals dry up) |
| Operational Health | All dimensions degrade over time (execution failure cascades everywhere) |
ORG HEALTH DIAGNOSTIC โ [Company] โ [Date]
Stage: [Seed/A/B/C] Overall: [Score]/10 Trend: [โ Improving / โ Stable / โ Declining]
DIMENSION SCORES
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ฐ Financial ๐ข 8.2 Runway 14mo, burn 1.6x โ strong
๐ Revenue ๐ก 5.8 NRR 104%, pipeline thin (1.8x coverage)
๐ Product ๐ข 7.4 NPS 42, DAU/MAU 38%
โ๏ธ Engineering ๐ก 5.2 Debt at 30%, MTTR 3.2h
๐ฅ People ๐ด 3.8 Attrition 24%, eng morale low
๐ Operations ๐ก 6.0 OKR 65% completion
๐ Security ๐ข 7.8 SOC 2 Type II complete, 0 incidents
๐ฃ Market ๐ก 5.5 CAC rising, win rate dropped to 22%
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
TOP PRIORITIES
๐ด [1] People: attrition at 24% โ engineering velocity will drop in 60 days
Action: CHRO + CEO to run retention audit; target top 5 at-risk this week
๐ก [2] Revenue: pipeline coverage at 1.8x โ Q+1 miss risk is high
Action: CRO to add 3 qualified opps within 30 days or shift forecast down
๐ก [3] Engineering: tech debt at 30% of sprint โ shipping will slow by Q3
Action: CTO to propose debt sprint plan; COO to protect capacity
WATCH
โ People โ Engineering cascade risk if attrition continues (see dimension interactions)
You don't need all metrics to run a diagnostic. The tool handles partial data:
references/health-benchmarks.md โ benchmarks by stage (Seed, A, B, C)scripts/health_scorer.py โ CLI scoring tool with traffic light output