{"id":"bbaf5b77-e8e5-45d2-ada5-25ad83583a88","shortId":"XUQMzY","kind":"skill","title":"measure-dashboard-requirements","tagline":"Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs.","description":"<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->\n# Dashboard Requirements\n\nA 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.\n\n## When to Use\n\n- When requesting a new dashboard from data/analytics teams\n- To define KPI tracking for a product, feature, or team\n- When formalizing ad-hoc reporting into a persistent dashboard\n- Before quarterly planning to specify what visibility you need\n- When onboarding stakeholders who need self-serve analytics\n\n## Instructions\n\nWhen asked to specify dashboard requirements, follow these steps:\n\n1. **Define the Purpose**\n   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.\n\n2. **Identify the Audience**\n   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.\n\n3. **Specify Key Metrics**\n   For each metric, document: name, business definition (in plain language), calculation formula, data source, and baseline/target values. Ambiguous metrics lead to misaligned dashboards.\n\n4. **Design Visualizations**\n   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.\n\n5. **Define Filters and Segments**\n   Specify what drill-downs users need: date ranges, user segments, product areas, geographic regions. Anticipate the \"slice and dice\" questions users will ask.\n\n6. **Document Data Sources**\n   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?\n\n7. **Set Permissions and Access**\n   Determine who can view what. Some metrics may need restricted access. Consider both security requirements and organizational politics.\n\n## Output Format\n\nUse the template in `references/TEMPLATE.md` to structure the output.\n\n## Quality Checklist\n\nBefore finalizing, verify:\n\n- [ ] Purpose is framed as questions to answer, not charts to build\n- [ ] All metrics have clear definitions and calculation formulas\n- [ ] Data sources are identified and accessible\n- [ ] Visualization choices match the type of insight needed\n- [ ] Filters enable the drill-downs users will want\n- [ ] Refresh frequency matches decision-making cadence\n\n## Examples\n\nSee `references/EXAMPLE.md` for a completed 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(2,753 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-05-02T12:54:13.014Z","embedding":null,"createdAt":"2026-04-18T22:09:27.166Z","updatedAt":"2026-05-02T12:54:13.014Z","lastSeenAt":"2026-05-02T12:54:13.014Z","tsv":"'1':130 '2':164 '3':194 '4':221 '5':249 '6':278 '7':308 'access':312,323,371 'actual':63 'ad':95 'ad-hoc':94 'ambigu':215 'analyt':9,119 'answer':44,141,353 'anticip':269 'area':266 'ask':122,277 'audienc':167 'bar':241 'base':227 'baseline/target':213 'becom':159 'board':193 'breakdown':248 'build':60,357 'busi':203 'cadenc':395 'calcul':208,364 'chart':144,225,238,242,355 'checklist':343 'choic':373 'clear':55,157,361 'come':285 'communic':233 'comparison':239 'complet':401 'composit':243 'consid':324 'context':179 'daili':191,305 'dashboard':3,10,21,32,35,42,61,78,101,125,139,152,155,173,220,297 'data':16,23,51,58,210,231,280,284,290,302,366 'data/analytics':80 'date':261 'decis':65,149,393 'decision-mak':392 'defin':25,83,131,250 'definit':204,362 'design':222 'determin':313 'dice':273 'differ':185 'dimens':247 'display':48,69,163 'document':29,37,201,279 'down':258,385 'drill':257,384 'drill-down':256,383 'enabl':381 'exampl':396,402 'execut':181 'featur':89 'filter':14,251,380 'final':345 'follow':127 'formal':93 'format':332 'formula':209,365 'frame':349 'frequenc':390 'geograph':267 'help':57 'hoc':96 'identifi':165,282,369 'includ':11,246 'inform':64,153 'insight':378 'instruct':120 'issu':292 'key':196 'known':289 'kpi':26,84 'languag':207 'latenc':294 'lead':217 'line':237 'make':394 'match':374,391 'may':320 'measur':2 'measure-dashboard-requir':1 'metric':12,46,162,197,200,216,319,359 'misalign':219 'name':202 'need':31,110,115,186,236,240,244,260,298,321,379 'new':77 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