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Designs OKR trees, KPI frameworks, North Star metrics, leading/lagging indicators, and A/B experiment guardrails for team goals and measurement.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ork:okr-designThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Structure goals, decompose metrics into KPI trees, identify leading indicators, and design rigorous experiments.
Structure goals, decompose metrics into KPI trees, identify leading indicators, and design rigorous experiments.
Objectives are qualitative and inspiring. Key Results are quantitative and outcome-focused — never a list of outputs.
Objective: Qualitative, inspiring goal (70% achievable stretch)
+-- Key Result 1: [Verb] [metric] from [baseline] to [target]
+-- Key Result 2: [Verb] [metric] from [baseline] to [target]
+-- Key Result 3: [Verb] [metric] from [baseline] to [target]
## Q1 OKRs
### Objective: Become the go-to platform for enterprise teams
Key Results:
- KR1: Increase enterprise NPS from 32 to 50
- KR2: Reduce time-to-value from 14 days to 3 days
- KR3: Achieve 95% feature adoption in first 30 days of onboarding
- KR4: Win 5 competitive displacements from [Competitor]
| Check | Objective | Key Result |
|---|---|---|
| Has a number | NO | YES |
| Inspiring / energizing | YES | not required |
| Outcome-focused (not "ship X features") | YES | YES |
| 70% achievable (stretch, not sandbagged) | YES | YES |
| Aligned to higher-level goal | YES | YES |
See references/okr-workshop-guide.md for a full facilitation agenda (3-4 hours, dot voting, finalization template). See rules/metrics-okr.md for pitfalls and alignment cascade patterns.
Decompose the top-level metric into components with clear cause-effect relationships.
Revenue (Lagging — root)
├── New Revenue = Leads × Conv Rate (Leading)
├── Expansion = Users × Upsell Rate (Leading)
└── Retained = Existing × (1 - Churn) (Lagging)
## Metrics Framework
North Star: [One metric that captures core value — e.g., Weekly Active Teams]
Input Metrics (leading, actionable by teams):
1. New signups — acquisition
2. Onboarding completion rate — activation
3. Features used per user/week — engagement
4. Invite rate — virality
5. Upgrade rate — monetization
Lagging Validation (confirm inputs translate to value):
- Revenue growth
- Net retention rate
- Customer lifetime value
| Business | North Star Example | Why |
|---|---|---|
| SaaS | Weekly Active Users | Indicates ongoing value delivery |
| Marketplace | Gross Merchandise Value | Captures both buyer and seller sides |
| Media | Time spent | Engagement signals content value |
| E-commerce | Purchase frequency | Repeat = satisfaction |
See rules/metrics-kpi-trees.md for the full revenue and product health KPI tree examples.
Every lagging metric you want to improve needs 2-3 leading predictors.
## Metric Pairs
Lagging: Customer Churn Rate
Leading:
1. Product usage frequency (weekly)
2. Support ticket severity (daily)
3. NPS score trend (monthly)
Lagging: Revenue Growth
Leading:
1. Pipeline value (weekly)
2. Demo-to-trial conversion (weekly)
3. Feature adoption rate (weekly)
| Indicator | Review Cadence | Action Timeline |
|---|---|---|
| Leading | Daily / Weekly | Immediate course correction |
| Lagging | Monthly / Quarterly | Strategic adjustments |
See rules/metrics-leading-lagging.md for a balanced dashboard template.
Every metric needs a formal definition before instrumentation.
## Metric: Feature Adoption Rate
Definition: % of active users who used [feature] at least once in their first 30 days.
Formula: (Users who triggered feature_activated in first 30 days) / (Users who signed up)
Data Source: Analytics — feature_activated event
Segments: By plan tier, by signup cohort
Calculation: Daily
Review: Weekly
Events:
user_signed_up { user_id, plan_tier, signup_source }
feature_activated { user_id, feature_name, activation_method }
Event naming: object_action in snake_case — user_signed_up, feature_activated, subscription_upgraded.
See rules/metrics-instrumentation.md for the full metric definition template, alerting thresholds, and dashboard design principles.
Every experiment must define guardrail metrics before launch. Guardrails prevent shipping a "win" that causes hidden damage.
## Experiment: [Name]
### Hypothesis
If we [change], then [primary metric] will [direction] by [amount]
because [reasoning based on evidence].
### Metrics
- Primary: [The metric you are trying to move]
- Secondary: [Supporting context metrics]
- Guardrails: [Metrics that MUST NOT degrade — define thresholds]
### Design
- Type: A/B test | multivariate | feature flag rollout
- Sample size: [N per variant — calculated for statistical power]
- Duration: [Minimum weeks to reach significance]
### Rollout Plan
1. 10% — 1 week canary, monitor guardrails daily
2. 50% — 2 weeks, confirm statistical significance
3. 100% — full rollout with continued monitoring
### Kill Criteria
Any guardrail degrades > [threshold]% relative to baseline.
See rules/metrics-experiment-design.md for guardrail thresholds, performance and business guardrail tables, and alert SLAs.
| Pitfall | Mitigation |
|---|---|
| KRs are outputs ("ship 5 features") | Rewrite as outcomes ("increase conversion by 20%") |
| Tracking only lagging indicators | Pair every lagging metric with 2-3 leading predictors |
| No baseline before setting targets | Instrument and measure for 2 weeks before setting OKRs |
| Launching experiments without guardrails | Define guardrails before any code is shipped |
| Too many OKRs (>5 per team) | Limit to 3-5 objectives, 3-5 KRs each |
| Metrics without owners | Every metric needs a team owner |
prioritization — RICE, WSJF, ICE, MoSCoW scoring; OKRs define which KPIs drive RICE impactproduct-frameworks — Full PM toolkit: value prop, competitive analysis, user research, business caseproduct-analytics — Instrument and query the metrics defined in OKR treeswrite-prd — Embed success metrics and experiment hypotheses into product requirementsmarket-sizing — TAM/SAM/SOM that anchors North Star Metric targetscompetitive-analysis — Competitor benchmarks that inform KR targetsVersion: 1.0.0
npx claudepluginhub yonatangross/orchestkit --plugin orkBuilds tailored metrics frameworks for products or businesses, from North Star metric and metric tree to counter-metrics and dashboards. Use for KPI trees, AARRR, HEART, or OKR requests.
Designs OKRs with North Star metric, input metrics tree, and cadence. Activates on 'set OKRs', 'define objectives', 'build metrics tree', or North Star queries.
OKR design — create objectives and key results with a North Star metric, input metrics tree, and cadence. Use when asked to "set OKRs", "define our objectives", "what should we measure this quarter", "design our OKR framework", "build a metrics tree", or "what's our North Star".