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OKR trees, KPI dashboards, North Star Metric, leading/lagging indicators, and experiment design. Use when setting team goals, defining success metrics, building measurement frameworks, or designing A/B experiment guardrails.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/yonatangross/orchestkit --skill okr-designコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Chain patterns for multi-phase pipelines: MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building or debugging a pipeline skill.
OrchestKit doctor for health diagnostics across manifest integrity, hook configuration, skill validation, agent frontmatter, MCP server connectivity, CC version compatibility, and permission rules. Reports issues with severity levels and auto-remediation suggestions. Validates component counts, detects orphaned entries, and checks CC version matrix compliance. Use when diagnosing plugin health, troubleshooting configuration issues, or running pre-release checks.
Inspects the OrchestKit telemetry pipeline for the current project — lists all known telemetry files with write counts, sizes, schema status, growth trend, and orphan detection. Use when verifying the observability pipeline is healthy, debugging a missing writer, or auditing which files have schema locks vs. which are drift-vulnerable. Read-only — never modifies telemetry files.
SKILL.md を表示中
SOC 職業分類に基づく
| name | okr-design |
| compatibility | Claude Code 2.1.220+ |
| description | OKR trees, KPI dashboards, North Star Metric, leading/lagging indicators, and experiment design. Use when setting team goals, defining success metrics, building measurement frameworks, or designing A/B experiment guardrails. |
| tags | ["okr","kpi","north-star","metrics","experiment","goal-setting","leading-lagging","instrumentation"] |
| context | fork |
| agent | product-strategist |
| version | 1.0.0 |
| author | OrchestKit |
| user-invocable | false |
| disable-model-invocation | false |
| complexity | medium |
| persuasion-type | guidance |
| metadata | {"category":"document-asset-creation"} |
| allowed-tools | ["Read","Glob","Grep","WebFetch","WebSearch"] |
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
Onboarding completion rate — activation
Features used per user/week — engagement
Invite rate — virality
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