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Grafana dashboard optimize

Skill haomingz/skills/skills/grafana-dashboard-optimize

Optimizes Grafana Jsonnet dashboard content for observability and SRE best practices (RED/USE/Golden Signals). Use when auditing dashboard quality, improving monitoring effectiveness, enhancing diagnostic capabilities, or reviewing observability coverage. Focuses on content-level improvements without code structure refactoring.From its SKILL.md

Install
npx -y skills add haomingz/skills --skill grafana-dashboard-optimize

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

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Grafana Dashboard Content Optimization (Observability / SRE)

Audit and optimize dashboard content for observability best practices. Apply RED/USE/Golden Signals methodology, improve diagnostic value, and reduce cognitive load for on-call teams.

Not suitable for: Code structure refactoring (use grafana-jsonnet-refactor), initial JSON conversion (use grafana-json-to-jsonnet), or code style formatting.

Workflow with progress tracking

Copy this checklist and track your progress:

Optimization Progress:
- [ ] Step 1: Understand context (purpose, audience, strategy)
- [ ] Step 2: Run seven-dimensional content audit
- [ ] Step 3: Produce prioritized recommendations report
- [ ] Step 4: Apply changes (if requested)
- [ ] Step 5: Validate improvements

Step 1: Understand context

Before any edits, document:

  • Dashboard purpose and target audience (SRE/on-call/management)
  • Current monitoring strategy and key questions it should answer
  • Datasources, variables, time range settings
  • Row structure and panel organization
  • Annotations, dashboard metadata (__inputs, __requires, schemaVersion, graphTooltip, version), and pluginVersion

See references/full-optimization-playbook.md for detailed context gathering. If optimizing dashboards in a specific repo or stack, review local Jsonnet defaults and docs in the working directory for current conventions.

Step 2: Run seven-dimensional content audit

Audit across these dimensions:

  1. Panel semantics: Missing/duplicated views, diagnostic coverage
  2. Query optimization: rate/increase usage, aggregation, cardinality
  3. Variable design: Names, defaults, cascading relationships
  4. Visualization: Panel types, units, thresholds, legends, table field pruning
  5. Layout: Overview → symptoms → root cause flow
  6. Titles/descriptions: Unified title style, clarity, context, troubleshooting hints, every panel has a description
  7. Proactive additions: SLO/SLI, annotations, comparisons, runbooks, dashboard metadata parity

For the full audit checklist and visualization/layout guidance, see references/full-optimization-playbook.md. For observability strategies (RED/USE/Golden Signals), see references/observability-strategies.md. For color, thresholds, and table styling aligned with local repo conventions, see references/visual-style-guides.md.

Step 3: Produce prioritized recommendations

Create structured assessment report with:

  • Critical: Missing essential metrics, broken queries, misleading visualizations
  • Recommended: Important improvements with clear ROI
  • Optional: Nice-to-have enhancements

Include rationale and expected impact for each recommendation. Use template in references/report-template.md.

Step 4: Apply changes (if requested)

If user approves changes:

  • Use available unified libraries when present (commonly panels, standards, themes)
  • Keep code structure changes minimal (content-only optimization)
  • Include Jsonnet snippets for high-impact changes
  • Preserve datasource selection patterns and any __inputs / __requires blocks if present
  • Preserve schemaVersion, graphTooltip, version, and pluginVersion when present
  • Add or improve panel descriptions so every panel has a clear, actionable description
  • Match existing repo/dashboard structure (imports → config → constants → helpers → panels → rows → variables → dashboard)
  • For table panels, use the panels lib (no raw Grafonnet) and follow the detailed table guidance in references/full-optimization-playbook.md.

For query optimization patterns, see references/query-optimization.md.

Step 5: Validate improvements

Run the quality checklist below against the improved dashboard. If any check fails, return to Step 4, fix, and verify again.

Quality checklist

  • RED/USE/Golden Signals coverage is complete
  • Queries are efficient and bounded
  • Units and thresholds use standards.*
  • Panel titles are consistent and every panel has a clear, actionable description
  • Layout follows overview → symptoms → root cause
  • Table panels remove unused fields (overrides/thresholds, colors, widths, cell types applied)
  • Variables return values and have no duplicates/extras
  • Regex filters preserved or added where needed
  • Row membership is correct (panels align to row gridPos.y)
  • __inputs / __requires, annotations, and dashboard metadata remain valid and intentional

Guardrails

  • Do not refactor code structure; use grafana-jsonnet-refactor for that.
  • Avoid broad rewrites; focus on content quality and observability value.
  • Keep deep guidance in references/ instead of bloating this file.
  • Do not run jsonnetfmt / jsonnet fmt on generated Jsonnet files.

References (load as needed)

  • references/visual-style-guides.md
  • references/full-optimization-playbook.md for the complete framework
  • references/observability-strategies.md for RED/USE/Golden Signals
  • references/query-optimization.md for PromQL/SQL guidance
  • references/report-template.md for the assessment report format

Keep looking

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