본문으로 건너뛰기
cccskills
무료GitHub에 공개

dashboards-and-real-time-visualization

Design live monitoring dashboards with clear priorities, linked charts, and predictable update behavior. Covers mobile interactions, connection failures, and rendering performance so users can scan current conditions, spot anomalies, and explore details as data changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Design dashboards and live visualization systems. Use when the user needs monitoring views, streaming charts, coordinated interactions, downsampling, or performance-aware operational visualization.

설치 방법 보기

이럴 때 유용합니다

  • Design an operations dashboard using a WebSocket feed
  • Improve charts receiving frequent data updates
  • Plan linked chart selections and filters
  • Review a mobile monitoring screen
  • Define displays for stale data and lost connections

포함된 파일(6)

  • SKILL.md7.5 KB
  • agents/openai.yaml183 B
  • references/interaction-patterns.md1.3 KB
  • references/monitoring-vs-analysis.md810 B
  • references/performance-and-degradation.md1.2 KB
  • references/streaming-data-pipelines.md1.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Dashboards and Real-Time Visualization

Overview

Use this skill when the visualization is a system, not a screenshot. That means update cadence, latency, interaction design, observability, and rendering budgets matter as much as chart choice.

If the main request is about how to test a live dashboard, freeze streams, mock refresh behavior, or cover alerting and stale states end to end, route first to ../testing-data-visualizations/SKILL.md.

Mobile operational use is default unless explicitly excluded. Use ../../references/foundations/mobile-first-responsive-visualization.md to plan the mobile portrait dashboard, optional landscape mode, touch interaction, on-screen keyboard behavior, spotty connection handling, and alerting or vibration strategy.

This skill covers three tightly related problems:

  • real-time streaming and refresh behavior
  • layout hierarchy and scan-first dashboard composition
  • interaction patterns and coordinated views
  • performance and scale

Default Questions

  1. What is the update model?
    • append-only stream
    • periodic polling
    • event bursts
    • full snapshot replacement
  2. What latency matters?
    • sub-second monitoring
    • near-real-time operational review
    • asynchronous reporting
  3. What must the user do?
    • notice anomalies
    • compare current versus historical
    • inspect causes
    • filter and drill down
  4. What are the hard limits?
    • frame budget
    • memory budget
    • network budget
    • exportability
    • mobile bandwidth, battery, and thermal budget
  5. How many visualization instances can be visible at once?
    • single focal chart
    • a few coordinated panels
    • many repeated tiles or sparklines
  6. What mobile state must work under interruption?
    • spotty or offline connection
    • app backgrounding or tab visibility changes
    • one-handed use
    • alert acknowledgment
    • on-screen keyboard for filters or notes

Real-Time Design Rules

  • Show time windows clearly.
  • Distinguish live data from historical context.
  • Handle missing, late, and out-of-order events explicitly.
  • Keep the last known good visualization visible during reconnects, with stale, delayed, partial, offline, and error states distinct from normal live data.
  • Show last updated time and update cadence near the evidence.
  • Use aggregation, downsampling, or rollups before raw point dumping.
  • Provide alert thresholds, annotations, and state transitions, not just moving lines.
  • Use vibration, system notifications, or push-style alerts only for user-requested and meaningful state changes; always provide an in-app visual and accessible alert path.
  • Keep the most important metric stable in position and encoding.
  • Keep keys with their charts: direct labels or chart-adjacent keys beat a shared legend parked elsewhere on the screen.
  • Use sparklines and other microcharts when compact trend context helps scanning, but rely on nearby labels and values to carry meaning.
  • Design dashboards around situation awareness, not around maximum tile count.
  • Prefer self-explanatory panels and concise labels over instructional paragraphs scattered across the UI.

Layout and Scanning Defaults

  • Give the screen a clear focal path: current state first, then supporting context, then controls and secondary diagnostics.
  • Avoid grids where every tile has equal visual weight unless the user truly needs uniform scanning across peers.
  • Keep filters and toggles near the views they change instead of collecting everything in a distant control rail.
  • Reserve callouts and narrative copy for anomalies, caveats, or actions. The normal state should be legible without tutorial text.
  • Collapse or defer low-value controls so the live state stays visually dominant.
  • On mobile, do not stack the filter rail or diagnostic controls before the live state. Use bottom sheets, drawers, tabs, or inline controls that return the user to the affected visualization after Apply, Cancel, Reset, or close.
  • Use mobile landscape for monitoring views when a wide timeline, map, field, route, dense table, or multi-series trace is meaningfully clearer in a handheld wide orientation, but still provide a portrait summary.

Interaction Patterns

  • overview first, filter or focus, then details on demand
  • overview plus detail
  • focus plus context
  • brush and link
  • hover for preview, click for commitment
  • tap/focus for preview, tap again or explicit action for commitment on touch devices
  • drill-down and drill-through
  • persistent selections that survive updates
  • explicit reset and undo paths

Interactivity should reduce cognitive load, not hide essential context behind constant mouse movement.

For mobile dashboards, add step-through controls, search, or nearest-item selection for dense marks; do not rely on hover, pixel-perfect taps, or one-finger chart panning that traps page scroll.

Performance Defaults

  1. Budget for 16 ms frames only when truly needed.
  2. Reduce work before optimizing code:
    • fewer marks
    • smarter aggregation
    • smaller repaint regions
    • lower-frequency updates where appropriate
  3. Choose the renderer intentionally:
    • SVG for semantics and annotation
    • Canvas2D for dense 2D raster workloads
    • WebGL, deck.gl, PixiJS, Sigma.js, or Three.js for GPU-scale marks, maps, particles, graph rendering, or true 3D
  4. Use ring buffers, viewport culling, and multi-resolution summaries for long-running live views.
  5. Separate interaction state from render state so updates remain predictable.
  6. Use particles or glow in dashboards only for active flow, alert state, focus, or recency. Avoid ambient motion that competes with monitoring.

Output Expectations

  • Describe the update model and failure modes.
  • Describe mobile reconnect, stale-data, offline/partial-data, and low-bandwidth behavior.
  • Explain what a user should understand at first scan before touching any controls.
  • Explain what the mobile user sees first and how the main visualization remains visible around controls and keyboard input.
  • Define the interaction contract before building widgets.
  • State how the system degrades when data rate or mark count rises.
  • Include a technical design section for new work covering simultaneous chart count, per-instance and page-level budgets, and maintenance implications of the chosen rendering strategy.
  • Keep accessibility and export strategy visible.

References

  • Shared theory:
    • ../../references/foundations/storytelling-annotation-and-critique.md
    • ../../references/foundations/layout-hierarchy-and-self-explanatory-ux.md
    • ../../references/foundations/interaction-models-and-progressive-disclosure.md
    • ../../references/foundations/mobile-first-responsive-visualization.md
    • ../../references/foundations/implementation-design-and-tradeoffs.md
  • Skill references:
    • ./references/monitoring-vs-analysis.md
    • ./references/streaming-data-pipelines.md
    • ./references/interaction-patterns.md
    • ./references/performance-and-degradation.md
    • ../testing-data-visualizations/SKILL.md

Representative Prompts

  • "Build a real-time operations dashboard from a WebSocket feed."
  • "Keep this live chart smooth with 100 updates per second."
  • "Design brushing, cross-filtering, and annotations for this monitoring UI."
  • "Tell me if this should be a dashboard or a report."
  • "Critique this monitoring screen using operational dashboard principles."

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Review charts, diagrams, and dashboards for accessibility, including color contrast, keyboard access, and text alternatives. Get specific guidance for preserving key findings in screen readers, static exports, and reduced-motion views.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing dashboard colors and keyboard access
  • Writing chart alt text and long descriptions
  • Preparing accessible figures for PDF reports
openai/plugins7.5천2026년 10월 8일 업데이트

Give a photo collection a consistent look using Adobe tools to adjust color, tone, presets, and optional crops. Review a before-and-after sample before processing the full set, then receive links to the edited images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Giving travel photos a consistent look
  • Unifying photos taken in mixed lighting
  • Applying a warm or cinematic style across a photo set
openai/plugins7.5천2026년 10월 8일 업데이트

Visualize a logo, sketch, or design on products such as mugs, shirts, and business cards. Review a pilot image, generate a visually consistent set, and collect the results on a shareable Firefly Board.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preview a logo on mugs and shirts
  • Turn a hand-drawn sketch into product mockups
  • Create consistent brand presentation images
openai/plugins7.5천2026년 10월 8일 업데이트

Prepare images and videos for social platforms using Adobe tools. Create image crops that keep the subject in view, expand backgrounds when available, preview a full-set sample, and resize videos while preserving their aspect ratio.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating image variants for multiple social platforms
  • Cropping photos for Instagram Stories
  • Preparing LinkedIn images and YouTube thumbnails
openai/plugins7.5천2026년 10월 8일 업데이트

Create flyers, social posts, business cards, and other visuals from Adobe Express templates. Choose a template, customize its text and background color, and receive a design link, with additional editing and export options depending on the available tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating an event flyer
  • Designing an Instagram post
  • Customizing business card text and colors
openai/plugins7.5천2026년 10월 8일 업데이트

Turn an uploaded video into three highlight edits with Adobe Quick Cut. Choose a target length and style, then compare versions that feature different moments and download the ones you prefer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating a short highlight video for social media
  • Condensing interview footage into a highlight reel
  • Comparing three edits with different moment selections
openai/plugins7.5천2026년 10월 8일 업데이트

openai의 스킬 모두 보기

이 스킬의 문제 신고