c-level-advisor/skills/org-health-diagnostic/SKILL.md
Cross-functional organizational health check combining signals from all C-suite roles. Scores 8 dimensions on a traffic-light scale with drill-down recommendations. Use when assessing overall company health, preparing for board reviews, identifying at-risk functions, or when user mentions org health, health check, or health dashboard.
npx skillsauth add alirezarezvani/claude-skills org-health-diagnosticInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 — 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) → Engineering (freeze infra) → Product (cut scope) | | Revenue Health | Financial (cash gap) → People (attrition risk) → Market (lose positioning) | | People Health | Engineering (velocity drops) → Product (quality drops) → Revenue (churn rises) | | Engineering Health | Product (features slip) → Revenue (deals stall on product) | | Product Health | Revenue (NRR drops, churn rises) → Market (CAC rises; referrals dry up) | | Operational Health | All dimensions degrade over time (execution failure cascades everywhere) |
ORG HEALTH DIAGNOSTIC — [Company] — [Date]
Stage: [Seed/A/B/C] Overall: [Score]/10 Trend: [↑ Improving / → Stable / ↓ Declining]
DIMENSION SCORES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💰 Financial 🟢 8.2 Runway 14mo, burn 1.6x — strong
📈 Revenue 🟡 5.8 NRR 104%, pipeline thin (1.8x coverage)
🚀 Product 🟢 7.4 NPS 42, DAU/MAU 38%
⚙️ Engineering 🟡 5.2 Debt at 30%, MTTR 3.2h
👥 People 🔴 3.8 Attrition 24%, eng morale low
🔄 Operations 🟡 6.0 OKR 65% completion
🔒 Security 🟢 7.8 SOC 2 Type II complete, 0 incidents
📣 Market 🟡 5.5 CAC rising, win rate dropped to 22%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TOP PRIORITIES
🔴 [1] People: attrition at 24% — engineering velocity will drop in 60 days
Action: CHRO + CEO to run retention audit; target top 5 at-risk this week
🟡 [2] Revenue: pipeline coverage at 1.8x — Q+1 miss risk is high
Action: CRO to add 3 qualified opps within 30 days or shift forecast down
🟡 [3] Engineering: tech debt at 30% of sprint — shipping will slow by Q3
Action: CTO to propose debt sprint plan; COO to protect capacity
WATCH
→ People → 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 — benchmarks by stage (Seed, A, B, C)scripts/health_scorer.py — CLI scoring tool with traffic light outputtools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.