Try it: /pm-skills:measure-dashboard-requirements "Your context here"
A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.
When requesting a new dashboard from data/analytics teams
To define KPI tracking for a product, feature, or team
When formalizing ad-hoc reporting into a persistent dashboard
Before quarterly planning to specify what visibility you need
When onboarding stakeholders who need self-serve analytics
You need the event tracking that feeds dashboards -> use measure-instrumentation-spec; instrument first, visualize second
You are designing an experiment readout, not a standing dashboard -> use measure-experiment-design and measure-experiment-results
You want OKR progress scored at cycle close -> use measure-okr-grader
The questions the dashboard should answer are not yet agreed -> frame outcomes first with foundation-okr-writer or define-problem-statement
Invoke the skill by name (/pm-skills:measure-dashboard-requirements on Claude Code, $measure-dashboard-requirements on Codex):
/pm-skills:measure-dashboard-requirements "Your context here"
Or reference the skill file directly: skills/measure-dashboard-requirements/SKILL.md
When asked to specify dashboard requirements, follow these steps:
Define the Purpose
Start with the questions this dashboard should answer, not the charts it should show. What decisions will this dashboard inform? A dashboard without clear purpose becomes a vanity metrics display.
Identify the Audience
Specify who will use this dashboard, how often, and in what context. An executive weekly review has different needs than a team’s daily standup board.
Specify Key Metrics
For each metric, document: name, business definition (in plain language), calculation formula, data source, and baseline/target values. Ambiguous metrics lead to misaligned dashboards.
Design Visualizations
Recommend chart types based on what the data should communicate. Time trends need line charts; comparisons need bar charts; compositions need pie/treemaps. Include dimension breakdowns.
Define Filters and Segments
Specify what drill-downs users need: date ranges, user segments, product areas, geographic regions. Anticipate the “slice and dice” questions users will ask.
Document Data Sources
Identify where data comes from and any known data quality issues. Note latency requirements.does the dashboard need real-time data or is daily refresh sufficient?
Set Permissions and Access
Determine who can view what. Some metrics may need restricted access. Consider both security requirements and organizational politics.
Use the template in references/TEMPLATE.md to structure the output. A complete spec fills every template section: Overview; Purpose and Questions; Audience; Key Metrics; Visualization Specifications; Filters and Segments; Data Sources; Access and Permissions; Alerts and Thresholds; Acceptance Criteria; Open Questions; and Appendix.
See references/EXAMPLE.md for a completed example.
Dashboard Name: [Name]
Requestor: [Who requested this]
Date: [When requirements captured]
Priority: [High/Medium/Low]
Target Delivery: [When needed]
[Question 1 - e.g., “Are users successfully completing onboarding?”]
[Question 2 - e.g., “Where do users drop off in the funnel?”]
[Question 3 - e.g., “Which cohorts have the best retention?”]
[Decision 1]
[Decision 2]
[Decision 3]
[Out of scope item 1]
[Out of scope item 2]
Audience Usage Frequency Primary Questions [Role/Team 1] [Daily/Weekly/Monthly] [What they care about] [Role/Team 2] [Daily/Weekly/Monthly] [What they care about] [Role/Team 3] [Daily/Weekly/Monthly] [What they care about]
When will this be viewed?
[E.g., “Weekly team meeting review”, “Daily morning check”, “Monthly board prep”]
What device/format?
[E.g., “Desktop browser”, “TV screen in office”, “Mobile for on-the-go”]
Attribute Value Business Definition [Plain language explanation] Calculation [Formula: numerator / denominator, etc.] Data Source [Where data comes from] Granularity [Daily/Weekly/Monthly] Current Baseline [Current value if known] Target [Goal value] Notes [Edge cases, known issues]
Attribute Value Business Definition [Plain language explanation] Calculation [Formula: numerator / denominator, etc.] Data Source [Where data comes from] Granularity [Daily/Weekly/Monthly] Current Baseline [Current value if known] Target [Goal value] Notes [Edge cases, known issues]
Attribute Value Business Definition [Plain language explanation] Calculation [Formula: numerator / denominator, etc.] Data Source [Where data comes from] Granularity [Daily/Weekly/Monthly] Current Baseline [Current value if known] Target [Goal value] Notes [Edge cases, known issues]
Metric Definition Source Target [Metric 1] [Short definition] [Source] [Target] [Metric 2] [Short definition] [Source] [Target] [Metric 3] [Short definition] [Source] [Target] [Metric 4] [Short definition] [Source] [Target]
Attribute Value Purpose [What question this answers] Chart Type [Line/Bar/Pie/Table/etc.] X-Axis [Dimension - e.g., Date, Category] Y-Axis [Metric(s)] Series/Breakdown [How data is grouped] Interactivity [Tooltips, drill-down, click actions] Position [Top-left, prominent, etc.]
Attribute Value Purpose [What question this answers] Chart Type [Line/Bar/Pie/Table/etc.] X-Axis [Dimension - e.g., Date, Category] Y-Axis [Metric(s)] Series/Breakdown [How data is grouped] Interactivity [Tooltips, drill-down, click actions] Position [Top-left, prominent, etc.]
Attribute Value Purpose [What question this answers] Chart Type [Line/Bar/Pie/Table/etc.] X-Axis [Dimension - e.g., Date, Category] Y-Axis [Metric(s)] Series/Breakdown [How data is grouped] Interactivity [Tooltips, drill-down, click actions] Position [Top-left, prominent, etc.]
┌─────────────────────────────────────────────────┐
│ [KPI Card 1] [KPI Card 2] [KPI Card 3] │
├────────────────────────┬────────────────────────┤
│ [Chart 1: Trend] │ [Chart 2: Funnel] │
├────────────────────────┴────────────────────────┤
│ [Chart 3: Detailed Table] │
└─────────────────────────────────────────────────┘
Filter Type Default Value Options Date Range Date picker Last 30 days Custom, presets [Filter 2] [Dropdown/Multi-select] [Default] [Options] [Filter 3] [Dropdown/Multi-select] [Default] [Options]
Chart Filter Type [Chart 1] [Filter] [Type] [Chart 2] [Filter] [Type]
Segment Name Definition Use Case [Segment 1] [Criteria] [When to use] [Segment 2] [Criteria] [When to use]
Source Type Owner Latency Quality Notes [Source 1] [Database/API/File] [Team] [Real-time/Daily/etc.] [Known issues] [Source 2] [Database/API/File] [Team] [Real-time/Daily/etc.] [Known issues]
Refresh Frequency: [Real-time / Hourly / Daily / Weekly]
Refresh Time: [When refresh should complete, e.g., “by 6am UTC”]
Historical Data Needed: [How far back, e.g., “Last 12 months”]
Data Retention: [How long to keep, e.g., “Rolling 2 years”]
[Known data quality issue 1 and how to handle]
[Known data quality issue 2 and how to handle]
Role/Group Access Level Restrictions [Group 1] Full access None [Group 2] View only Cannot export [Group 3] Limited Only sees [section]
Data Element Sensitivity Handling [Element 1] [PII/Confidential/etc.] [Mask/Aggregate/Restrict]
Condition Threshold Action Recipients [Metric 1] drops below [Value] Send email [Who] [Metric 2] exceeds [Value] Slack alert [Channel]
[Question 1 for data team]
[Question 2 needing clarification]
[Link to related dashboard 1]
[Link to related dashboard 2]
[Link to metric definitions]
[Link to data dictionary]
Requirements version 1.0. Update as needs evolve.
Dashboard Requirements: Product Health Dashboard
Dashboard Name: Product Health Dashboard
Requestor: Maya Johnson, Product Manager
Date: January 2026
Priority: High
Target Delivery: End of Q1 2026
Are users healthy? What is our overall user engagement and are users getting value from the product?Where do users struggle? Which parts of the product have the highest friction or drop-off?What features drive retention? Which features, when adopted, correlate with long-term retention?Are we trending up or down? How do key metrics compare to previous periods?
Prioritization of product improvements based on friction points
Feature investment decisions based on retention correlation
Resource allocation to high-impact areas
Early warning on user health problems before they hit revenue
Deep-dive analysis on specific features (separate feature dashboards exist)
Real-time operational monitoring (use DataDog for that)
Individual user support (use admin tools)
Financial/revenue metrics (finance owns that dashboard)
Audience Usage Frequency Primary Questions Product Team Daily Feature adoption, user friction Leadership Weekly Overall health trends, KPIs Engineering Weekly Error rates, performance impact on UX Customer Success Daily Account health signals
When will this be viewed?
Product team: Daily standup (quick KPI check) and weekly deep-dive
Leadership: Weekly product review meeting
CS: Before customer calls to assess account health
What device/format?
Desktop browser (primary)
Shared on TV in product team area
Exported to PDF for monthly board reports
Attribute Value Business Definition Unique users who performed any meaningful action in the product on a given day Calculation COUNT(DISTINCT user_id) WHERE action_type NOT IN (‘login’, ‘logout’) AND event_date = date Data Source events.user_actions table Granularity Daily Current Baseline 12,400 Target 15,000 by end of Q2 Notes Excludes bot accounts and internal users
Attribute Value Business Definition Ratio of daily active users to monthly active users, indicating how often users return Calculation DAU / MAU (rolling 30-day MAU) Data Source Derived from events.user_actions Granularity Daily Current Baseline 0.32 (32%) Target 0.40 (40%) Notes Industry benchmark for SaaS is 0.20-0.40
Attribute Value Business Definition Percentage of MAU who have used each core feature at least once in the past 30 days Calculation COUNT(DISTINCT users who used feature) / MAU Data Source events.feature_usage table Granularity Daily (rolling 30-day) Current Baseline Varies by feature (see chart) Target Top 5 features > 50% adoption Notes Breakdown by feature; shows per-feature adoption
Attribute Value Business Definition Percentage of users from a signup cohort who are still active after N days Calculation (Active users in cohort at day N) / (Total users in cohort) Data Source events.user_actions + users.signups Granularity Weekly cohorts, measured at D7, D14, D30, D60, D90 Current Baseline D30: 42% Target D30: 55% Notes Compare cohorts over time to see if retention is improving
Attribute Value Business Definition Time from signup to completing first meaningful action (creating first project) Calculation MEDIAN(first_project_created_at - signup_at) Data Source users.signups + events.project_created Granularity Daily (rolling 7-day average) Current Baseline 2.3 days Target < 1 day Notes Users who never create a project counted as NULL/excluded
Metric Definition Source Target DAU Unique users with meaningful action events.user_actions 15,000 DAU/MAU Stickiness ratio Derived 40% Feature Adoption % MAU using each feature events.feature_usage Top 5 > 50% D30 Retention Users active 30 days post-signup events + users 55% TTFV Time to first project creation events + users < 1 day
Attribute Value Purpose At-a-glance health check of key metrics Chart Type KPI Cards (4 cards in a row) Metrics Shown DAU, DAU/MAU, D30 Retention, TTFV Comparison Show vs. previous period and vs. target Interactivity Click card to see trend chart Position Top of dashboard, most prominent
Attribute Value Purpose Show DAU and MAU trends over time Chart Type Line chart with dual axis X-Axis Date (daily) Y-Axis Left: DAU, Right: DAU/MAU ratio Series/Breakdown DAU line, MAU line, DAU/MAU line Interactivity Hover for values, zoom on date range Position Top-left main section
Attribute Value Purpose Show which features users are/aren’t adopting Chart Type Horizontal bar chart X-Axis Adoption rate (%) Y-Axis Feature name Series/Breakdown Single series, sorted by adoption Interactivity Click bar to see feature trend over time Position Top-right main section
Attribute Value Purpose Compare retention across weekly cohorts Chart Type Cohort heatmap (weeks × retention periods) X-Axis Days since signup (D1, D7, D14, D30, D60, D90) Y-Axis Signup week cohort Series/Breakdown Color intensity = retention % Interactivity Hover for exact values Position Middle section, full width
Attribute Value Purpose Identify where users struggle in key flows Chart Type Funnel chart X-Axis Funnel step Y-Axis Users (absolute and %) Series/Breakdown Steps: Signup → Onboarding Complete → First Project → Invited Team → Paid Interactivity Click step to see breakdown by segment Position Bottom-left
Attribute Value Purpose Detailed view for deep-dive analysis Chart Type Data table with sorting Columns Date, DAU, MAU, DAU/MAU, New Signups, Churned Users, Feature 1-5 adoption Interactivity Sort by any column, export to CSV Position Bottom section, collapsible
┌─────────────────────────────────────────────────────────────────────┐
│ [DAU: 12.4K] [Stickiness: 32%] [D30 Ret: 42%] [TTFV: 2.3d] │
│ ▲ +5% ▼ -2% ▲ +3% ▼ +0.2d │
├────────────────────────────────┬────────────────────────────────────┤
│ 📈 Engagement Trend │ 📊 Feature Adoption │
│ [Line chart: DAU/MAU] │ [Horizontal bars by feature] │
├────────────────────────────────┴────────────────────────────────────┤
│ 🔲 Retention Cohort Heatmap │
│ [Week cohorts × D1/D7/D14/D30/D60/D90] │
├────────────────────────────────┬────────────────────────────────────┤
│ ⬇️ Funnel Analysis │ 📋 Detailed Data Table │
│ [Signup → Value funnel] │ [Sortable metric table] │
└────────────────────────────────┴────────────────────────────────────┘
Filter Type Default Value Options Date Range Date picker Last 30 days Last 7/30/90 days, MTD, QTD, Custom Plan Type Multi-select All Free, Starter, Professional, Enterprise User Segment Multi-select All New (<30d), Active, At-risk, Churned Platform Dropdown All Web, iOS, Android
Chart Filter Type Feature Adoption Feature category Dropdown (Core, Advanced, Admin) Funnel Entry point Dropdown (Organic, Paid, Referral)
Segment Name Definition Use Case New Users Signed up within last 30 days Track onboarding effectiveness At-Risk No login in 14+ days but not churned Target for re-engagement Power Users > 20 sessions per month Understand ideal user behavior Enterprise On Enterprise plan Compare enterprise vs. SMB health
Source Type Owner Latency Quality Notes events.user_actions Snowflake table Data Engineering 1 hour 99.9% complete events.feature_usage Snowflake table Data Engineering 1 hour Some features not instrumented users.signups Snowflake table Data Engineering Real-time Authoritative source users.subscriptions Snowflake table Data Engineering Daily Synced from Stripe
Refresh Frequency: Hourly during business hours, daily overnight
Refresh Time: Dashboard current as of top-of-hour; overnight refresh complete by 6am UTC
Historical Data Needed: Last 24 months
Data Retention: Aggregated data retained indefinitely; raw events 24 months
Bot traffic filtered but occasional false positives; flag if DAU spikes >20% unexpectedly
Feature usage for “Reports” feature incomplete before Nov 2025 (instrumentation added)
Enterprise accounts have multiple users; user_id is individual, account_id needed for account-level views
Role/Group Access Level Restrictions Product Team Full access None Engineering Full access None Leadership Full access None Customer Success Limited Cannot see individual user data Sales View only Cannot export, account-level only
Data Element Sensitivity Handling User email PII Not displayed; use user_id Account name Confidential Visible to CS/Sales only
Condition Threshold Action Recipients DAU drops below 10,000 Email + Slack Product team D30 retention drops below 35% Email PM + Leadership TTFV exceeds 5 days Slack Onboarding squad Feature adoption (any) drops >10% week-over-week Email Feature owner
Should we include revenue/MRR on this dashboard or keep it separate?
Do we need real-time DAU or is hourly sufficient?
Should cohorts be weekly or monthly granularity?
Feature Deep-Dive: Reporting (dashboard link)
Onboarding Funnel Dashboard (dashboard link)
Finance & Revenue Dashboard (dashboard link)
Metric Definitions Wiki (internal link)
Data Dictionary (internal link)
Instrumentation Spec for Feature Tracking (internal link)
Requirements version 1.0. Update as needs evolve.
See this skill applied to three different product contexts:
Storevine (B2B): Storevine B2B ecommerce platform . Campaigns adoption and revenue analytics dashboard requirements
Prompt:
measure-dashboard-requirements
Dashboard: Campaigns adoption and revenue . post-GA monitoring
Audience: Growth PM (daily), Merchant Success (weekly), Head of Product
1. Are non-adopter merchants sending their first campaign?
(primary hypothesis metric: first-send rate, 60-day window)
2. Is Campaigns driving measurable revenue for merchants?
(7-day attributed revenue per campaign send)
3. Is the email-related churn rate declining since GA?
(churn cohort analysis: merchants with and without Campaigns sends)
- First-send rate (60-day, non-adopter segment)
- Campaigns-attributed revenue (7-day window, rolling)
- Active Campaigns merchants (sent ≥1 campaign in last 30 days)
- Churn rate by Campaigns usage cohort
- Send failure rate and unsubscribe rate (guardrails)
Analytics platform: Amplitude (events) + Storevine order DB (revenue)
Need: full dashboard requirements doc with metric definitions,
visualizations, filters, data sources, and acceptance criteria.
Output:
Brainshelf (Consumer): Brainshelf consumer PKM app . Resurface experiment dashboard requirements for Amplitude
Prompt:
measure-dashboard-requirements
resurface experiment dashboard for amplitude. need it ready before
the a/b test starts (mar 9).
1. product team (priya, chloe, alex, jordan) . daily monitoring
2. marco (ceo) . weekly exec check-in, needs a single-screen summary
questions the dashboard should answer:
- is the treatment group returning more than control?
- are users clicking items in the digest?
- is the unsubscribe rate within the guardrail?
- what's the opt-in funnel conversion rate?
- are there segment differences (library size, cadence)?
1. 7-day return rate trend (treatment vs control, weekly)
2. email CTR trend (daily)
3. opt-in funnel (card viewed → opted in)
4. unsubscribe rate trend (weekly, with guardrail line)
5. segment breakdown table (library size, cadence)
filters: date range, experiment variant, library size segment.
Output:
Workbench (Enterprise): "Workbench enterprise collaboration platform: Blueprints post-launch monitoring dashboard requirements"
Prompt:
measure-dashboard-requirements
I need dashboard requirements for the Blueprints post-launch monitoring dashboard. Here's the context:
1. Rachel V. (PM) -- daily check: adoption trends, approval bottlenecks, template usage
2. Sandra C. (Head of Product) -- weekly review: executive summary, account growth, key health metrics
3. Karen L. (Engineering) -- real-time: system health, merge latency, error rates
**Key metrics from the PRD and experiment results:**
- Median time-to-approved (target: ≤2.5 days [fictional])
- Empty-section submission rate (target: ≤10% [fictional])
- Approval cycle count (target: ≤1.5 cycles [fictional])
- Blueprint adoption: monthly active Blueprint creators (target: 2,000 [fictional])
- Enterprise account growth (target: 500 → 650 in 12 months [fictional])
- Workbench analytics pipeline (event data from instrumentation spec)
- WebSocket provider telemetry (merge latency, connection count, error rate)
- CRM pipeline (account growth, enterprise tier)
- Support ticketing system (Blueprint-related ticket volume)
**Visualization preferences:**
- Time-to-approved: trend line over time (weekly median)
- Adoption: stacked area chart by department/template type
- Approval funnel: horizontal funnel chart
- System health: real-time gauges with alert thresholds
Please generate the full dashboard requirements including layout, filters, alerts, and acceptance criteria.
Output:
Before finalizing, verify: