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/documd-visuals

@2672b69
by Markdown Viewermarkdown-viewer/skills3.4k stars
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Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

Use this Skill: https://skilld.dev/gh/markdown-viewer/skills/documd-visuals

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examplesservice-reliabilityforecast-range-band.md

Error Band — Forecast Range Around a Central Trend (Vega-Lite)

Best for: showing a central forecast with uncertainty or variability around it instead of pretending the line is exact Avoid when: you only have one deterministic series or the audience needs raw individual observations instead of intervals Answers: what the expected trend is, and how wide the plausible range becomes over time

{
  "config": { "range": { "category": ["#2b66c4", "#2f9e44", "#f3a33c", "#d1242f", "#7048e8", "#0f9b9b", "#c16f8a", "#7c5a3d"] } },
  "$schema": "https://vega.github.io/schema/vega-lite/v6.json",
  "width": 460,
  "height": 260,
  "data": {
    "values": [
      {"week": "2026-04-01", "mean": 92, "low": 84, "high": 101},
      {"week": "2026-04-08", "mean": 98, "low": 89, "high": 108},
      {"week": "2026-04-15", "mean": 105, "low": 95, "high": 117},
      {"week": "2026-04-22", "mean": 111, "low": 99, "high": 124},
      {"week": "2026-04-29", "mean": 116, "low": 103, "high": 130}
    ]
  },
  "layer": [
    {
      "mark": {"type": "errorband", "opacity": 0.22},
      "encoding": {
        "x": {"field": "week", "type": "temporal", "title": null},
        "y": {"field": "low", "type": "quantitative", "title": "Demand"},
        "y2": {"field": "high"}
      }
    },
    {
      "mark": {"type": "line", "strokeWidth": 2.5},
      "encoding": {
        "x": {"field": "week", "type": "temporal"},
    "title": {"text": "Forecast Range", "subtitle": "Expected demand with explicit low/high bounds", "anchor": "start"},
        "y": {"field": "mean", "type": "quantitative"}
      }
    }
  ]
}
  "mark": {"type": "line", "strokeWidth": 2.5, "color": "#7ca6ff"},

Data Shape

One row per period with a central estimate plus explicit low/high bounds. The band and the line reuse the same dataset in separate layers.

Key Options

Option Effect
Layered errorband + line Separates the range from the central forecast
y + y2 on the band layer Uses explicit interval bounds rather than inferred aggregate uncertainty
Temporal x encoding Keeps the interval aligned to ordered time periods
Low-opacity band Preserves the line as the primary reading target

Pitfalls

  • ❌ Showing one clean line for uncertain forecasts → ✅ the band is the point of the chart
  • ❌ Using a fully opaque band → ✅ it will bury the mean line and labels
  • ❌ Mixing unrelated confidence assumptions → ✅ define clearly what the interval means before encoding it

Alternatives

Variant Use instead
One deterministic trend trend-line-multi-series.md
Distribution by category boxplot-latency-distribution.md
Mean with discrete error bars only A Vega-Lite errorbar view
<!-- source: Vega-Lite docs (errorband composite mark / layered views) + examples gallery Error Bars & Error Bands -->

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    This skill provides documentation and examples for creating various visuals in Markdown using engines like ECharts, Vega, PlantUML, and HTML. It recommends using the vendor-owned @markdown-viewer/documd CLI tool for document conversion and verification. The skill itself contains no executable code or malicious patterns.

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Signed by skilld at 2672b69. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Activeupdated 3 weeks ago
Other metadata
compatibility
Figures are text in the Markdown — fenced blocks or bare HTML — and the host viewer renders them live: nothing to pre-render, build or install. Export to html / epub / docx / pdf is optional and needs Node 18+ and Chrome/Chromium, via `npx @markdown-viewer/documd`.

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