Scaffold and build Splunk custom visualizations using Canvas 2D. Use this skill whenever the user wants to create, modify, debug, fix, or package a Splunk custom visualization app — including visualization_source.js, formatter.html, visualizations.conf, savedsearches.conf, webpack config, harness.json, or anything involving SplunkVisualizationBase. Also triggers for Splunk Cloud vetting errors (check_for_trigger_stanza, check_for_prohibited_files), blurry/HiDPI canvas issues, custom font embedding in Splunk vizs, Dashboard Studio custom viz integration, and scaffolding parent apps that bundle multiple vizs.
Instalación
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Scaffold and build Splunk custom visualizations using Canvas 2D. Use this skill whenever the user wants to create, modify, debug, fix, or package a Splunk custom visualization app — including visualization_source.js, formatter.html, visualizations.conf, savedsearches.conf, webpack config, harness.json, or anything involving SplunkVisualizationBase. Also triggers for Splunk Cloud vetting errors (check_for_trigger_stanza, check_for_prohibited_files), blurry/HiDPI canvas issues, custom font embedding in Splunk vizs, Dashboard Studio custom viz integration, and scaffolding parent apps that bundle multiple vizs.
You are an expert Splunk developer specializing in custom visualizations built with the Splunk Visualization Framework (Canvas 2D rendering, AMD modules, webpack). You generate production-ready code, not prototypes.
Architecture Overview
Requires Splunk Enterprise 10.2+ or Splunk Cloud. The visualizations.conf configuration and custom viz framework were significantly improved in 10.2. The target platform (Cloud, Enterprise, or both) is determined in Step 1 and affects which vetting constraints are applied — see the Platform Differences table.
A Splunk custom visualization is a standalone Splunk app that renders search results using Canvas 2D. It consists of:
Formatter UI — HTML form that exposes user-configurable settings in the Splunk dashboard editor
Visualization source — JavaScript AMD module that extends SplunkVisualizationBase with Canvas 2D rendering
Build tooling — webpack bundles the source into a single visualization.js AMD module
Build/deploy scripts — Shell scripts to build, package, and deploy the app
Step 1: Gather Requirements
Before generating code, ask the user (or extract from context):
Target platform: Splunk Cloud, Splunk Enterprise, or both. This determines which vetting constraints apply (see Platform Differences below). When in doubt, default to both — this produces an app that passes Splunk Cloud vetting and also works on Enterprise.
Viz name: short lowercase identifier (e.g., network_graph, heatmap_grid). Used as both the app ID and the visualization stanza name.
Display label: human-readable name for the Splunk UI (e.g., "Network Graph", "Heatmap Grid"). Max 30 characters.
Description: one-line description. Max 80 characters. See Description Best Practices in Splunk Design Guidelines.
Expected SPL columns: which fields the search must produce (e.g., _time, source, dest, value). Distinguish required vs optional columns. Ask if the viz will share a base search with other panels — if so, use configurable field names (see rule 18) instead of hardcoding column names like value.
Configurable settings: what the user should be able to tweak from the formatter panel (e.g., colors, sizes, toggles, units). For each setting, determine: name, type (text/radio/dropdown), default value.
Rendering approach: what to draw on the canvas (shapes, lines, text, gradients, animations).
Custom no-data message: ask the user if they want a custom "awaiting data" message rendered on the canvas when no data is flowing (e.g., "Awaiting telemetry data"). If yes, the viz will detect a _status field from an SPL appendpipe fallback and render the message centered on the canvas. Optionally, an emoji can be displayed above the text for visual flair. If no, the viz falls back to Dashboard Studio's default placeholder (grey bar chart icon or VisualizationError text).
If the user provides a vague request, ask clarifying questions before scaffolding.
Platform Differences
The table below summarises the key differences that affect generated code. When the target is both, apply all Splunk Cloud constraints — they are a strict superset of Enterprise requirements.
Concern
Splunk Cloud
Splunk Enterprise
Both (default)
[id] stanza in app.conf
Required (check_version_is_valid_semver)
Optional but recommended
Required
[triggers] for visualizations.conf
Rejected (check_for_trigger_stanza)
Accepted but unnecessary
Omit
sc_admin role in default.meta
Required (check_kos_are_accessible) — admin role does not exist in Cloud
Required after scaffolding: Create harness.json in {viz_path}/ and add the viz name to both the vizs array and the appropriate categories group in harness-manifest.json (at the repo root). Both are mandatory for the test harness. Do not skip this step.
File Templates
README.md
Every viz app includes a README.md at the app root. This is the single source of documentation — it describes the visualization, installation, expected columns, SPL queries, configuration options, and build instructions. Structure:
# {Display Label} — Splunk Custom Visualization
{Description paragraph}
## Install1. Copy or symlink the `{app_name}/` directory into `$SPLUNK_HOME/etc/apps/`2. Restart Splunk: `splunk restart`3. The "{Display Label}" visualization appears in the viz picker.
## Required Columns
| Column | Type | Description |
|--------|------|-------------|
| {col} | {type} | {what it is} |
## Optional Columns
| Column | Type | Description |
|--------|------|-------------|
| {col} | {type} | {what it is} |
## Notes- Key data assumptions, units, fallback behaviour, etc.
## Search
\`\`\`spl
{full working SPL query}
\`\`\`
## Configuration
| Setting | Description | Default |
|---------|-------------|---------|
| {setting} | {what it does} | {default value} |
## Time Range`-1m` to `now` (historical). Do NOT use real-time (`rt-1m` to `rt`) — Splunk Cloud vetting rejects real-time saved searches (`check_for_real_time_saved_searches_for_cloud`).
## Build
From the repo root:
\`\`\`bash
./build.sh {app_name}
\`\`\`
The tarball is output to `dist/{app_name}-1.0.0.tar.gz`.
default/app.conf
[id]
name = {app_name}
version = 1.0.0
[install]
is_configured = false
build = 1
[package]
id = {app_name}
check_for_updates = false
[ui]
is_visible = true
label = {display_label}
[launcher]
author = {author}
description = {description}
version = 1.0.0
The [id] stanza is required by Splunk Cloud vetting (check_version_is_valid_semver). Do NOT add a [triggers] stanza with reload.visualizations = simple — visualizations.conf is a Splunk-defined conf file and Splunk Cloud vetting (check_for_trigger_stanza) will reject it. The [triggers] stanza is only for custom (non-Splunk) config files. Keep the version in [id] and [launcher] in sync. is_configured MUST be false — Splunk Cloud vetting fails apps that ship is_configured = true (it claims a setup was already performed).
Character limits (enforced by Splunk's viz picker UI):
label: max 30 characters
description: max 80 characters
search_fragment: max 80 characters
The search_fragment is a partial SPL snippet that shows users how to structure their search for this visualization. It should produce the expected columns.
default/savedsearches.conf
Provide a complete, working saved search that demonstrates the visualization. Use historical time ranges — do NOT use real-time (rt-* to rt) as Splunk Cloud vetting rejects them (check_for_real_time_saved_searches_for_cloud). Include all display.visualizations.custom.* settings with sensible defaults.
Required for Splunk to export the visualization to other apps/users.
The global [] stanza is mandatory for Splunk Cloud vetting — without it the upload is blocked with check_meta_default_write_access.
Always include sc_admin alongside admin in write ACLs — the admin role is not available in Splunk Cloud. Without sc_admin, Cloud administrators cannot access the knowledge objects (check_kos_are_accessible).
static/ (app icons)
Splunk requires four PNG icon files in the static/ directory at the app root. These are displayed in the Splunk app browser, Manage Apps page, and Splunkbase. All four must exist — missing icons cause Splunk Cloud vetting warnings.
File
Size
Description
appIcon.png
36x36 px
Standard app icon
appIcon_2x.png
72x72 px
HiDPI (Retina) app icon
appIconAlt.png
36x36 px
Alternate icon (used on dark backgrounds)
appIconAlt_2x.png
72x72 px
HiDPI alternate icon
Use a simple, recognizable graphic on a transparent background. The Alt variants should be legible on both light and dark backgrounds — typically a lighter or inverted version of the primary icon.
formatter.html
Use Splunk's built-in form components. Multiple <form> elements with class="splunk-formatter-section" and section-label render as separate tabs in the format menu.
Container:
<splunk-control-group label="..." help="..."> — wraps each input. help shows helper text below the control.
Text area: <splunk-text-area name="{{VIZ_NAMESPACE}}.{setting}">
Radio: <splunk-radio-input name="{{VIZ_NAMESPACE}}.{setting}" value="{default}"> with <option> children
Dropdown: <splunk-select name="{{VIZ_NAMESPACE}}.{setting}" value="{default}"> with <option> children
Color picker: <splunk-color-picker name="{{VIZ_NAMESPACE}}.{setting}" type="{type}" value="{default}"> with optional <splunk-color> children
Color picker type values:splunkCategorical (default), splunkSemantic, splunkSequential, custom. Use custom with <splunk-color> children to define a bespoke palette:
{"name":"{app-name}-viz","version":"1.0.0","description":"{description}","scripts":{"build":"webpack --mode production","dev":"webpack --mode development --watch"},"devDependencies":{"webpack":"^5.90.0","webpack-cli":"^5.1.4"}}
visualization.css
Create this file with a transparent background on the root container. Splunk requires it to exist. Always default to background: transparent so the visualization inherits the dashboard's background color. Only use an opaque background if the user explicitly requests one.
.{app-name}-viz {
background: transparent;
}
.gitignore
Important: .gitignore is for local development only. Splunk Cloud vetting rejects apps containing .gitignore, .gitkeep, .github/, or any .git* files (check_for_prohibited_files). The build script must exclude all .git* files from the tarball using --exclude='.git*' or --exclude='.*'.
node_modules
preview.png (Visualization Picker Icon)
Splunk displays a preview.png in the visualization picker when the user selects a chart type. This file is required for a polished viz.
Fill the full 116×76 area — no gaps, borders, or empty margins. Show a recognizable, moderately detailed representation of the viz (not too minimal, not too busy)
Background
Use the viz's typical dark background color (e.g., #1a1a2e, #0d1117) — not transparent, since the picker has its own background
Prerequisite: Generating preview.png requires Python 3 with the Pillow library. Use a virtual environment (see rule 31) — pip install Pillow inside the venv.
Generation script — generate_preview.py is a temporary helper that creates preview.png for the viz. Place it alongside the viz source, run it once, then delete it. The script draws a simplified representation of the viz type on a 116×76 canvas.
Generate a viz-type-specific script based on the table below. Each script must:
Create a 116×76 RGBA image
Fill with the viz's dark background color
Draw a simplified but recognizable representation of the viz
Save as preview.png in the same directory
Print a confirmation message
Viz type preview templates:
Viz Type
What to Draw
Gauge / Meter
Arc with colored fill segment, center value text
Heatmap / Grid
Small grid of colored rectangles with varying intensity
Network Graph
Circles (nodes) connected by lines (edges)
Status Board
Colored rounded rectangles with short labels
Timeline / Gantt
Horizontal bars at different Y positions
Bar / Column
Vertical bars of varying height with axis line
Radial / Donut
Colored arc segments forming a ring with center text
Line Chart
Polyline with dots on a simple axis
Single Value
Large centered number with small label below
Example generation script (gauge type):
#!/usr/bin/env python3"""Generate preview.png for the viz picker (116x76)."""import math, os
try:
from PIL import Image, ImageDraw, ImageFont
except ImportError:
print("ERROR: Pillow is required. Install with: source .venv/bin/activate && pip install Pillow")
raise SystemExit(1)
W, H = 116, 76
img = Image.new("RGBA", (W, H), (26, 26, 46, 255)) # dark bg
draw = ImageDraw.Draw(img)
# Draw a simplified gauge arc
cx, cy, r = W // 2, H // 2 + 8, 28
draw.arc([cx - r, cy - r, cx + r, cy + r], 200, 340, fill=(80, 80, 120), width=6)
draw.arc([cx - r, cy - r, cx + r, cy + r], 200, 290, fill=(0, 200, 120), width=6)
# Center value text (use default font)try:
font = ImageFont.truetype("/System/Library/Fonts/Helvetica.ttc", 14)
except Exception:
font = ImageFont.load_default()
draw.text((cx, cy - 6), "75", fill=(255, 255, 255), font=font, anchor="mm")
out = os.path.join(os.path.dirname(os.path.abspath(__file__)), "preview.png")
img.save(out)
print(f"Created {out} ({W}x{H})")
Workflow: After generating the viz source files, create generate_preview.py customized for the specific viz, run it with python3 generate_preview.py, verify the output, then delete the script. Only preview.png ships with the app.
harness.json (MANDATORY)
Create appserver/static/visualizations/{app_name}/harness.json alongside formatter.html. Also add the viz name to vizs in harness-manifest.json at the repo root. Both are required for the test harness — do not skip.
When creating a new viz, read references/core-template.md for the complete AMD module template. It includes all lifecycle methods (initialize, getInitialDataParams, formatData, updateView, reflow, destroy), the _lastGoodData caching pattern, _status sentinel handling, HiDPI canvas setup, and the _drawStatusMessage / _ensureCanvas helpers. Use it as the starting point — do not deviate from the structure.
Critical Rules
Use var, not const/let. Webpack targets AMD for Splunk's RequireJS environment. Some Splunk versions run older JS engines. Stick with ES5 (var, function, for loops). No arrow functions, no template literals, no destructuring.
Always handle HiDPI displays. Set canvas.width/height to rect.width * dpr and call ctx.scale(dpr, dpr). All drawing math uses the CSS pixel dimensions (rect.width, rect.height), NOT canvas.width/height.
Never assume canvas is visible. Check rect.width > 0 && rect.height > 0 before drawing. Splunk may call updateView while the viz is hidden. This check is required in every method that draws — not just updateView, but also _drawStatusMessage and any other custom drawing methods.
Always null-check ctx. canvas.getContext('2d') can return null if the canvas is detached. Add if (!ctx) return after every getContext('2d') call — in updateView, _drawStatusMessage, and any other method that obtains a context.
Reset ctx.shadowBlur after use. Canvas shadow state leaks into subsequent draw calls if not explicitly reset to 0.
Reset ctx.globalAlpha after use. Same leaking behavior as shadows.
formatData must return a plain object, not the raw data. Returning the raw Splunk data object causes issues with Splunk's internal caching.
Throw SplunkVisualizationBase.VisualizationError for user-facing errors (missing columns, bad data). This displays a clean error in the Splunk UI instead of a silent failure.
Settings from formatter come as strings. Always parse: parseInt(x, 10) for integers, parseFloat(x) for floats, === 'true' for booleans.
Font usage: sans-serif for labels, monospace for numeric values, custom fonts via base64 embedding. The default convention for system fonts is:
sans-serif for all labels, headings, badges, status text, no-data messages, legend text, axis labels, and any non-numeric text (e.g., ctx.font = 'bold 12px sans-serif')
monospace only for numeric value readouts where digit alignment matters — temperatures, times, percentages, pressures, speeds, lap times, etc. (e.g., ctx.font = 'bold 16px monospace')
Never use monospace for labels or descriptive text.
For custom fonts, read references/custom-fonts.md — covers base64 embedding in visualization.css, centralised font management via shared/fonts.css, the document.fonts.ready wait pattern, ctx.font quoting pitfalls, and the harness-vs-Splunk OpenType feature leakage gotcha. Always include a system font fallback (e.g., sans-serif).
JSON data files (e.g., lookup tables, coordinate maps) can be placed next to visualization.js and loaded with require('../filename.json') — webpack will bundle them inline.
No this in helper functions. Keep drawing helpers as pure functions that take ctx, dimensions, and data as arguments. Only the four lifecycle methods (initialize, getInitialDataParams, formatData, updateView) plus destroy should use this.
Animation timers: Two approaches depending on animation style:
setInterval — for periodic state-driven redraws (e.g., flashing LEDs, blinking indicators). Use this.invalidateReflow() to trigger redraws. Store the timer ID on this and clear it in destroy(). Guard against creating duplicate timers.
requestAnimationFrame — for continuous smooth animation (e.g., flowing particles, scrolling tickers, conveyor belts). Store the frame ID on this._animFrame, cancel with cancelAnimationFrame(this._animFrame) in destroy(). Preferred for 60fps rendering as it syncs with the browser's refresh rate and avoids timer backlogs.
XSS prevention with SplunkVisualizationUtils. When inserting dynamic strings from search results into the DOM (innerHTML, text nodes, attributes), use SplunkVisualizationUtils.escapeHtml(str) to prevent XSS injection. This is required for Splunk certification. For dynamic URLs, use SplunkVisualizationUtils.makeSafeUrl(url) to strip unsafe schemes like javascript:. Canvas-only vizs that never touch the DOM with user data can skip this.
Available SplunkVisualizationUtils helpers:
escapeHtml(str) — encode strings for safe DOM insertion
makeSafeUrl(url) — strip unsafe URL schemes
getCurrentTheme() — returns 'dark' or 'light'
normalizeBoolean(val) — coerce string/int to boolean
Invalidation methods (do not override, call when needed):
this.invalidateFormatData() — re-run formatData on next cycle
this.invalidateUpdateView() — re-run updateView on next cycle
this.invalidateReflow() — re-run reflow on next cycle
setupView() — called once before the first updateView, useful for one-time DOM setup
onConfigChange(configChanges, previousConfig) — called when formatter settings change
reflow() — called when the container resizes; typically call this.invalidateUpdateView() here
Configurable field names for shared searches. In Splunk dashboards, a single base search often feeds multiple panels via post-process or shared results. When a viz only needs one or a few columns from a wide search, add a formatter setting (e.g., field) that lets the user specify which column to read. This avoids requiring users to create separate searches or rename columns. Pattern:
In formatter.html (default should be a realistic column name, not a generic placeholder):
<splunk-control-grouplabel="Field Name"help="Column name from your search (e.g. cpu_usage, response_time)"><splunk-text-inputname="{{VIZ_NAMESPACE}}.field"value="count"></splunk-text-input></splunk-control-group>
In formatData — pass through colIdx and row, do NOT read config here (see rule 21):
In updateView — read the field name from config and extract the value:
var ns = this.getPropertyNamespaceInfo().propertyNamespace;
var fieldName = config[ns + 'field'] || 'count'; // must match formatter default — see rule 19var rawVal = 0;
if (data.colIdx[fieldName] !== undefined) {
var v = parseFloat(data.row[data.colIdx[fieldName]]);
if (!isNaN(v)) rawVal = v;
}
In savedsearches.conf (always include the field setting explicitly):
Use this pattern whenever the viz displays a single value (gauges, single-value displays) or a small subset of a larger search result. For vizs that consume many specific columns (like a multi-metric dashboard panel), hardcoded column names are fine.
Formatter HTML defaults are NOT sent to the JS on first load. The value="..." attribute on formatter inputs only takes effect after the user opens the Format panel and interacts with it. On initial render (and for saved searches without explicit settings), config[ns + 'setting'] is undefined, so the JS || fallback is what actually runs. This means:
The JS default (|| 'fallback') must exactly match the formatter HTML default (value="fallback")
The savedsearches.conf must explicitly include every setting to avoid relying on defaults
Never use a generic fallback like 'value' in JS if the formatter defaults to something else like 'speed'
Test the viz with a fresh panel (no saved config) to verify defaults work correctly
Real-time search handling. Splunk real-time searches (rt-1m to rt) accumulate rows over time. The count in getInitialDataParams controls how many rows the viz receives, and data.rows is ordered oldest-first. This has two implications:
Always read the last row for latest-value vizs (gauges, single-value displays):
// WRONG — reads the oldest row, goes stale as results accumulatevar row = data.rows[0];
// CORRECT — reads the most recent rowvar row = data.rows[data.rows.length - 1];
Size count appropriately for the viz type:
Single-value / gauge vizs: count: 50 — only needs the latest row, small buffer keeps updates snappy
Grid / table vizs: count: 10000 — needs all rows for display
For vizs that iterate all rows (charts, tables, maps), data.rows[0] through data.rows[length-1] is fine. But for vizs that display a single current value, always use data.rows[data.rows.length - 1].
Use VisualizationError for the no-data state (empty/missing rows). In Dashboard Studio v2, return false from formatData causes Splunk to show its own default grey bar chart placeholder and never call updateView — there is no way to display a custom message. Throwing VisualizationError is the only mechanism that works in Dashboard Studio v2 to show a meaningful "Awaiting data" message. For fully custom no-data rendering (custom fonts, emojis, styled text on canvas), use the _status field + SPL appendpipe pattern described in rule 27.
Cache last good data to prevent flashing. In real-time searches, Splunk can briefly call formatData with empty data.rows between result batches. Without caching, this causes the "Awaiting data" error to flash momentarily even though data was flowing moments before. Fix: store the last successful formatData result on this._lastGoodData and return it when rows are temporarily empty. Only throw the error on the very first call (before any data has ever arrived):
Important: Only cache-return for the empty-rows check. If formatData also validates required columns, that check should also return _lastGoodData before throwing, so a transient batch with missing columns doesn't flash an error:
if (colIdx.required_field === undefined) {
if (this._lastGoodData) returnthis._lastGoodData;
thrownewSplunkVisualizationBase.VisualizationError('...');
}
Cache in updateView too — not just formatData. In Dashboard Studio, Splunk can pass data = false directly to updateView even when formatData returned cached data (e.g., when a chain/post-process search temporarily returns zero rows between result batches). Without a cache fallback in updateView, the viz goes blank with no error message. Always use this pattern:
updateView: function(data, config) {
if (!data) {
if (this._lastGoodData) { data = this._lastGoodData; }
else { return; } // or draw a no-data placeholder
}
// ... rest of drawing code ...
}
This provides two layers of protection: formatData caching prevents VisualizationError flashing, and updateView caching prevents blank canvas flashing.
Do NOT throw VisualizationError for missing individual fields in real-time vizs. When a real-time search first starts (or a playback begins), some fields may not exist in the initial results. For these transient missing-field cases, fall back to safe defaults so the viz renders immediately and updates as data arrives:
// WRONG — throws error for a single missing fieldif (isNaN(value)) {
thrownewSplunkVisualizationBase.VisualizationError('Column not found');
}
// CORRECT — renders immediately with 0, updates when data arrivesif (isNaN(value)) {
value = 0;
}
Never read config in formatData. Splunk internally caches formatData results and the interaction between config-dependent formatData logic and Splunk's caching causes inconsistent update timing — some vizs update instantly while others stall for up to a minute on the same dashboard with the same search. Keep formatData a pure data-only pass-through:
// WRONG — reading config in formatData causes caching/timing issuesformatData: function(data, config) {
var ns = this.getPropertyNamespaceInfo().propertyNamespace;
var fieldName = config[ns + 'field'] || 'speed';
// ... extract value based on config ...return { value: value };
}
// CORRECT — formatData passes through data, updateView reads configformatData: function(data, config) {
if (!data || !data.rows || data.rows.length === 0) {
if (this._lastGoodData) returnthis._lastGoodData;
thrownewSplunkVisualizationBase.VisualizationError('Awaiting data');
}
var fields = data.fields;
var colIdx = {};
for (var i = 0; i < fields.length; i++) {
colIdx[fields[i].name] = i;
}
var row = data.rows[data.rows.length - 1];
return { colIdx: colIdx, row: row };
}
For multi-column vizs with hardcoded field names (no config dependency), you can extract values in formatData using getVal(row, 'fieldName', 0) — this is fine because the field names are constants, not config-dependent. The rule is: no config access, no this.getPropertyNamespaceInfo() in formatData.
savedsearches.conf.spec must document every custom setting. The README/savedsearches.conf.spec file in each viz app must list every display.visualizations.custom.* setting used in formatter.html and savedsearches.conf. Without this, splunk btool check reports "Invalid key" errors. If multiple viz apps are bundled into a single parent Splunk app, the spec entries from each viz must also be present in the parent app's README/savedsearches.conf.spec.
Use /_bump to reload static assets without restarting Splunk. After rebuilding visualization.js, navigate to http://<splunk>:8000/en-US/_bump (must be logged in) and click "Bump version". Then hard-refresh the browser (Cmd+Shift+R / Ctrl+Shift+R). This clears Splunk's static file cache without a restart. A restart is only needed when changing config files (app.conf, visualizations.conf, savedsearches.conf).
Label settings: always offer alignment when a viz has a configurable label. If a viz draws a title/label (e.g., a heading above a gauge or chart), provide a labelAlign formatter setting with left/center/right options. Reserve space for the label in the layout calculation so it doesn't collide with the viz content — shrink the viz area rather than overlapping:
In formatter.html:
<splunk-control-grouplabel="Label Align"help="Horizontal alignment of the label text"><splunk-radio-inputname="{{VIZ_NAMESPACE}}.labelAlign"value="center"><optionvalue="left">Left</option><optionvalue="center">Centre</option><optionvalue="right">Right</option></splunk-radio-input></splunk-control-group>
In updateView — reserve space in the layout, then draw at the top:
var labelAlign = config[ns + 'labelAlign'] || 'center';
var labelReserve = label ? 28 : 0; // shrink viz area to make room// Use labelReserve when calculating available height for the viz contentvar availH = h - labelReserve - otherPadding;
// Draw label at the top of the panelif (label) {
var labelFontSize = Math.max(8, Math.min(20, radius * 0.13));
ctx.font = 'bold ' + labelFontSize + 'px sans-serif';
ctx.fillStyle = 'rgba(255,255,255,0.5)';
ctx.textBaseline = 'top';
ctx.textAlign = labelAlign;
var lPad = Math.max(10, w * 0.04);
var labelX = labelAlign === 'left' ? lPad : labelAlign === 'right' ? w - lPad : cx;
ctx.fillText(label, labelX, 4);
}
The key principle: reserve space first, draw later. Don't try to squeeze the label into the gap between the viz and the panel edge — reduce the viz size to create a guaranteed gap.
Drilldown from Canvas-based visualizations. When the viz needs click-through to drill into data, read references/drilldown.md for the full hit-rect tracking pattern, click/hover handlers, Dashboard Studio drilldown configuration, and token format. Always document drilldown setup in the viz README.
Use original ingested field names (no aliases required). Vizs must reference fields by the exact name used at indexing time. Never require users to rename fields with as aliases in SPL just to match a viz's hardcoded expectations. This keeps SPL straightforward (latest(field_name) as field_name) and prevents silent breakage from mismatched aliases. The only exceptions are:
Display renames in table-style vizs (e.g., | rename status as Status) where the column header is the user-facing label
Computed/derived fields that don't exist at ingestion (e.g., eval delta = field_a - field_b)
Custom no-data message via _status field and SPL appendpipe. In Dashboard Studio v2, the "Awaiting data" overlay (from VisualizationError) and the default placeholder (grey bar chart icon) are both rendered outside the viz's sandboxed iframe — CSS and JS inside the viz cannot hide or style them. The only way to display a fully custom no-data state is to ensure the search always returns at least one row.
SPL pattern — append a fallback row with a _status field when the main search returns zero results:
The appendpipe only produces a row when the main search has zero results (where count=0). When real data is flowing, it adds nothing. Include dummy values for required fields so formatData doesn't throw a column-missing error.
In formatData — detect the _status field early and return a sentinel object:
// Check for status message from appendpipe fallbackif (colIdx._status !== undefined) {
var statusRow = data.rows[data.rows.length - 1];
var statusVal = statusRow[colIdx._status];
if (statusVal) {
return { _status: statusVal };
}
}
In updateView — intercept the sentinel before normal rendering:
if (data && data._status) {
this._ensureCanvas();
this._drawStatusMessage(data._status);
return;
}
The _ensureCanvas and _drawStatusMessage methods are defined in the core template above. Key design points:
The text auto-scales down to fit 85% of the container width, preventing overflow on small panels
The emoji renders at full opacity above the dimmed text for visual hierarchy
The message string comes from the SPL _status field, so it can be changed without rebuilding the viz
If using a custom font (e.g., Formula1), replace sans-serif in _drawStatusMessage with the custom font family
_ensureCanvas is needed because updateView may be called before the canvas exists (e.g., on first load with the status fallback)
When NOT to use this pattern: If the viz's SPL search uses | stats commands that always return a row (e.g., | stats count returns 0 instead of empty), Dashboard Studio will always pass data to the viz and the default placeholder never appears. In that case, _status/appendpipe is unnecessary.
Dashboard Studio JSON: keep viz options empty. When embedding custom visualizations in a Dashboard Studio v2 JSON dashboard, do NOT hardcode formatter settings in the viz's options block. Use empty options instead:
Why: When a user changes a setting via the Format panel, Dashboard Studio writes a new fully-qualified key (e.g., my_app.my_gauge.colorTheme: "neon") but does not remove the original short key (e.g., colorTheme: "default"). The viz JS reads config[ns + 'colorTheme'] which resolves to the short key's stale value — the user's change is silently ignored. By starting with empty options, the only keys that exist are the ones the user explicitly sets, so there is no conflict.
The viz's JS || fallbacks (e.g., config[ns + 'colorTheme'] || 'default') handle the initial "no settings" state correctly — this is the same code path that runs on first load in Splunk (see rule 19).
Text readability across all themes. Every text element drawn on canvas must be readable regardless of the active theme or what's behind it (colored fills, glows, dark backgrounds):
Use pure white (#ffffff) for all informational text — labels, names, values, sub-text. White provides maximum contrast on dark dashboards and remains legible over colored fills. Never use theme-dependent text colors for text that overlays colored elements — they can be low-contrast in some themes.
Reserve colour for meaning, not decoration — only colour a value when the colour itself communicates status (e.g., green/yellow/red for thresholds). All other text stays white.
Sub-text uses white at reduced opacity (rgba(255,255,255,0.5) minimum) — never go below 50% opacity for text the user needs to read. 30% opacity is only acceptable for purely decorative text.
Test every theme visually — default, dark, and neon themes have very different background intensities. Text that's readable on default may vanish on neon.
Per-theme colour palettes must maintain the theme's visual identity. Neon theme should use neon-family colours (#00ff88, #ffff00, #ff0066) — not blues and purples that belong to the default theme.
README.md must pass markdown linting. Every generated README.md must be valid, well-formatted Markdown that passes standard linting (e.g., markdownlint). Specifically:
Headings: use ATX style (#), increment by one level only (## after #, not #### after ##), blank line before and after
Lists: consistent markers (- for unordered), blank line before the first item, proper indentation for nested lists
Tables: every pipe must have a space on both sides of content (| Column | Type | not |Column|Type|). Separator rows use | --- | --- | with spaces. Blank line before and after the table. Never omit spaces around pipes — this is a hard requirement
Code blocks: fenced with triple backticks, language identifier on opening fence (```spl, ```bash, ```json), blank line before and after
Line length: no hard limit, but avoid excessively long lines in prose (code blocks and tables are exempt)
Trailing whitespace: none
Final newline: file ends with exactly one newline
No bare URLs: use [text](url) for links
No HTML: use Markdown equivalents
Always use a Python virtual environment. When running Python scripts (preview image generation, icon creation, data processing, or any task requiring pip install), never install packages directly on the user's system. Always create or reuse a virtual environment:
Never use pip install --break-system-packages or bare pip install outside a venv
If .venv/ already exists in the repo, activate it rather than creating a new one
Add .venv/ to the repo's .gitignore if not already present
The venv is a local development tool — it is never included in Splunk app tarballs
Smooth real-time numeric values with a client-side tween. Splunk real-time searches typically bin at 200 ms–1 s, so the viz receives a new sample roughly every half-second. Rendering raw values directly causes smooth-motion elements (needles, bars, rotations, positions) to snap between samples — visible jerkiness that undermines a live-data feel. For any viz where the data is numeric and continuous, decouple the render rate from the data rate with a client-side ease-out tween:
updateView stores the latest sample as a target, and snaps current to target on first sample so the viz doesn't sweep from 0 on dashboard load.
A short-lived setInterval(16 ms) or requestAnimationFrame loop lerps current → target with a frame-rate-independent ease-out:
Each tick redraws the viz using current. Idle-stop the timer when the value has settled for a few frames to save CPU; restart it from the next updateView that changes the target.
Expose a smoothness formatter setting (per-second follow speed). Default 8 closes ~95% of the gap within 500 ms — practically indistinguishable from a snap for sharp transitions but removes jitter during dwell. 0 disables tweening and restores snap behaviour.
Clear the timer in destroy().
Choosing a smoothness value. Smoothness is a rate constant, not a "level". Time to close 95% of the gap ≈ 3 / smoothness. Dashboard Studio polls at ~1 Hz, so the tween's relationship to the poll interval matters:
High values (10–14) — settle time ~200–300 ms, well inside the 1 s poll window. The needle chases, then rests until the next sample. You will see a subtle 1 Hz pulse between "active chase" and "still" phases. Best for analysis/coaching views where sub-second accuracy matters.
Default (8) — ~375 ms settle. Compromise. Pulse is softer but still visible during sustained acceleration.
Low values (2–4) — settle time ~750 ms–1.5 s, longer than the poll interval. The tween never finishes before the next sample arrives, so motion is continuous across poll boundaries and the 1 Hz pulse disappears entirely. Trade-off: the displayed value lags reality by ~1 s. Best for atmospheric / broadcast-style HUDs where "looks like a live TV telemetry overlay" matters more than instantaneous accuracy.
Match smoothness across vizs on the same dashboard. If one viz uses smoothness=2 (cinematic lag) and another uses smoothness=12 (responsive), related telemetry appears de-synced — the responsive one reacts first and the cinematic one trails, which reads as a bug. Pick one value per app and apply it as the default to every smoothing-enabled viz (JS initialiser, formatter input value=, and every savedsearches.conf example stanza).
❌ Tables or ranked lists where row positions reorder between samples — tweening re-orderings reads as glitchy, not smooth.
See the Smoothing Between SPL Samples recipe for the full implementation covering single-value tweens and multi-entity tweens keyed by identifier.
Parallel Workflow (new vizs only)
Background agents run unattended and cannot prompt for permission. To avoid silent denials, do all permission-sensitive setup in the foreground first, then spawn background agents only for commands that are already allowed.
Before spawning — run in foreground (main agent):
Ensure the venv exists and Pillow is installed: source .venv/bin/activate && pip install Pillow — this may need user approval the first time
Run npm install in the viz directory — this may need approval if not yet allowed
Write all source files (Steps 1-2) and the generate_preview.py / icon generation scripts
Then spawn two background agents in parallel:
Background: Image generation — run python3 generate_preview.py and the icon generation scripts (already allowed via Bash(python3:*)). Delete the scripts after. No pip or venv activation needed — that was done in the foreground step.
Background: Build — run npm run build in the viz directory (requires Bash(npm run:*) in settings). Verify visualization.js was created.
While these run, the main agent should update harness-manifest.json (add to vizs and categories), then run the verification checklist (Step 4).
The project's .claude/settings.json auto-allows python3, npm install, npm run, and ./build.sh — background agents inherit these permissions.
Do not try to parallelize file generation itself — the config files, formatter.html, JS defaults, and savedsearches.conf share settings names that must stay in sync. One agent keeping it all in context is more reliable than two trying to coordinate.
Step 3: Generate Build Script
The repo uses a single shared build.sh at the root. Do not generate per-viz build scripts — use the existing build.sh instead. Do not generate deploy scripts — apps should be installed via the Splunk UI (Manage Apps → Install app from file).
Usage
# Build a specific viz app
./build.sh {app_name}
# Build all viz apps in examples/
./build.sh
Output tarballs go to dist/{app_name}-{version}.tar.gz.
What build.sh does
npm install — installs dependencies if node_modules/ is missing
webpack build — bundles src/visualization_source.js into visualization.js
Font CSS — if shared/fonts.css exists and the viz's visualization.css doesn't already contain @font-face, prepends the shared font CSS (then restores the source file after packaging)
Package tarball — creates a tar.gz excluding dev files (.*, node_modules/, src/, package.json, webpack.config.js, __pycache__/, *.pyc)
The build script handles macOS-specific tar flags (--disable-copyfile, --no-xattrs, xattr -rc) automatically.
Common Canvas 2D Recipes
For drawing helpers (color scales, rounded rects, arcs, legends, grids, responsive text, hit testing), read references/canvas-recipes.md.
For smooth real-time animation (client-side tweening between SPL samples), read references/smoothing.md. This covers both single-value tweens (gauges, needles) and per-entity position tweens (moving items on a map), with the full timer/cleanup implementation.
Splunk Design Guidelines Reference
For chart-style vizs that use axes, legends, gridlines, or tooltips, read references/design-guidelines.md for official Splunk font stacks, color palettes (semantic, categorical, sequential, divergent), spacing constants, description best practices, and responsive design guidance. Canvas-only vizs (gauges, status boards) can skip this — use sans-serif and monospace per rule 10.
Viz Type Guidance
When the user describes what they want, map their description to one of these common viz categories and tailor the scaffolding accordingly:
Viz Type
Key Canvas Patterns
Typical SPL Columns
Gauge / Meter
Arcs, gradients, centered text
label, value, min, max
Heatmap / Grid
Grid layout, color scales, cell text
row, col, value
Network Graph
Lines between nodes, circles, labels
source, dest, value
Status Board
Rounded rects, color-coded cells, icons
name, status, detail
Timeline / Gantt
Horizontal bars, time axis, labels
_time, task, duration, status
Map / Floor Plan
Coordinate plotting, background image, markers
x, y, label, value
Bar / Column
Filled rects, axis lines, labels
category, value
Radial / Donut
Arc segments, center text, legend
label, value
For any viz type, always include a "no data" state. Ask the user whether they want a custom canvas-rendered message (rule 27 _status pattern) or the default Dashboard Studio placeholder (VisualizationError). If custom, the message text is defined in the SPL appendpipe fallback and rendered by _drawStatusMessage.
Step 4: Verify Completeness
For new vizs, verify all files are generated. For modifications to existing vizs, update all affected files — code changes that add/remove data fields, settings, or features MUST be reflected in README.md, savedsearches.conf, savedsearches.conf.spec, harness.json, and formatter.html. Never change the JS without updating the documentation and config files to match.
Before presenting the generated code, verify:
README.md exists with description, install, columns, search, configuration, drilldown (if applicable), time range, and build sections
All files in the directory structure are generated
app.conf has [id] stanza with name and version (required for Splunk Cloud vetting)
app.conf does NOT have a [triggers] stanza for visualizations.conf (it is a Splunk-defined conf, not a custom one)
app.conf package ID matches the directory name
app.conf version is consistent across [id] and [launcher] stanzas
visualizations.conf stanza name matches the directory name
visualization_source.js formatData validates required columns and throws VisualizationError
If custom no-data message requested: formatData detects _status field, updateView intercepts it, _ensureCanvas and _drawStatusMessage methods exist
visualization.css exists (transparent background by default)
metadata/default.meta exists with global [] access stanza, export = system, and sc_admin in all write ACLs (required for Splunk Cloud)
savedsearches.conf uses historical time ranges (no rt-* / rt — rejected by Splunk Cloud vetting)
static/ contains all four app icons: appIcon.png (36x36), appIcon_2x.png (72x72), appIconAlt.png (36x36), appIconAlt_2x.png (72x72)
preview.png exists (116×76px) in appserver/static/visualizations/{app_name}/ — fills full area with recognizable viz representation
harness.json exists alongside formatter.html with correct fields, formatter defaults (matching JS defaults), data mode, and sampleRows as strings
Viz name added to vizs and appropriate categories group in harness-manifest.json
.gitignore excludes node_modules
If modifying an existing viz: README.md updated to reflect new/changed columns, settings, and features
If modifying an existing viz: savedsearches.conf search query includes any new data fields
If modifying an existing viz: harness.json updated with new field controls and data columns
Step 5: Generate Test Harness Config (MANDATORY)
Every new viz MUST have a harness.json file and be added to harness-manifest.json. When modifying an existing viz, update its harness.json to match.
Every viz app includes a harness.json file that enables local browser testing without deploying to Splunk. A generic test-harness.html (containing zero viz-specific code) reads these files and renders any viz with interactive controls.
harness-manifest.json
A single manifest at the project root registers all vizs and optional shared config:
fontCSS (optional): path to a shared CSS file with @font-face declarations. Loaded once when any viz is selected.
pathTemplate (optional): URL path pattern to locate each viz's files. {name} is replaced with the viz name. Defaults to {name}/appserver/static/visualizations/{name} if omitted. Use this when viz apps live under a subdirectory (e.g., examples/) or when the repo layout differs from the standard flat structure.
categories (optional): groups vizs into themed sections in the harness picker dropdown. Keys are category names, values are arrays of viz names. If omitted, the harness shows all vizs in a single "All" group. When adding a new viz, add it to both vizs and the appropriate category.
vizs: array of viz app directory names. The harness loads {pathTemplate}/harness.json for each.
harness.json
Located alongside formatter.html in each viz's directory. Defines everything the test harness needs to render the viz with interactive controls.
Human-readable name shown in the viz picker dropdown
defaultSize
{ width, height }
No
Default panel dimensions in pixels when the viz is selected
noDataMessage
string
No
Custom message shown when "Test No Data" is clicked. Falls back to "No data available"
dependencies
string[]
No
JSON files to preload (e.g., ["track_splines.json"]). Loaded from the viz root dir, registered in the AMD module cache as ../{filename} and ./{filename}
fields
array
Yes
Data field controls shown in the sidebar (see below)
formatter
array
Yes
Formatter setting controls matching the viz's formatter.html (see below)
data
object
Yes
Defines how Splunk-format data is constructed (see below)
Field types (fields array):
Type
Properties
Description
slider
min, max, step, default
Range input with live value display
select
options, default
Dropdown. Options can be strings ("opt") or objects ({"v": "0", "l": "Off"})
text
default
Free text input
Optional field properties:
transform: "divide100" — divides the value by 100 before inserting into the data row (e.g., steer input -100..100 → -1.0..1.0)
Fields whose names start with _ (e.g., _numDrivers, _preset) are control fields — they influence data generation but are not inserted as columns.
Formatter types (formatter array):
Type
Properties
Description
radio
options (string[]), default
Toggle buttons
select
options, default
Dropdown
color
default
Color picker + hex text input
text
default
Free text input
Formatter setting names must match the suffixes used in formatter.html (e.g., colorScheme maps to config[ns + 'colorScheme'] in the viz JS). Defaults must match the JS fallback values (rule 19).
Data modes (data object):
Two generic modes — the harness has no domain-specific code:
single_row — builds one row from field values. Used for gauges, single-value displays, and any viz that reads data.rows[data.rows.length - 1].
columns: array of column names. Each column's value comes from the matching field's current value.
dynamicColumnName (optional): renames a column based on a formatter setting. Used when the viz has a configurable "Field Name" setting (rule 18). Pass {} (empty object) to explicitly disable dynamic column renaming.
Important: In single_row mode, every column in columnsmust have a corresponding entry in the top-level fields array with a default value. The harness builds the data row from fields[].default — if a column has no matching field, its value is '0'. Unlike multi_row mode (which reads from sampleRows), single_row mode does NOT use sampleRows as a fallback. Always provide slider, text, or select fields for every column.
multi_row — passes pre-defined sample rows. Used for charts, tables, maps, and any viz that iterates data.rows.
columns: array of column names (defines the field schema).
sampleRows: array of row arrays. Each row is an array of strings (Splunk always passes strings).
rowCountField (optional): name of a _-prefixed slider field that controls how many rows to show (slices from the start).
relativeTimeColumn (optional): string — column name whose values are treated as relative time offsets in seconds. The harness converts them to Date.now()/1000 - value, producing realistic absolute Unix timestamps for time-series vizs.
Column overrides: when a non-_ field name matches a column name, the slider value replaces that column's value in every row. This lets users change a value (e.g., track_id) across all sample rows interactively.
Usage
To test locally:
cd splunk_app && python3 -m http.server 8080
Open http://localhost:8080/test-harness.html. Select a viz from the dropdown. Adjust data fields and formatter settings — the canvas re-renders in real-time.
Adding a new viz to the harness
Do this as part of every scaffolding — it is not a separate or optional step.
Add "{name}" to the vizs array and the appropriate categories group in harness-manifest.json (at the repo root, next to test-harness.html)
No changes to test-harness.html are needed — it discovers everything from JSON.
Step 6: Scaffold a Dashboard Studio App (Optional)
When the user asks to scaffold a Dashboard Studio app that bundles multiple custom vizs, read references/dashboard-studio-app.md for the full directory structure, file templates, build pipeline, and namespace guidance.
