Diagram Design: evidence-driven visual explanations
Diagram Design performance and cost: measure authoring, rendering and revision separately
Plan a useful evaluation without turning template counts, progressive loading or sixteen functional cases into invented speed and cost claims.
What you will learn
- Separate the work into measurable stages
- Include dependencies and rework
- Design an honest benchmark record
Before you start
- Basic HTML and SVG concepts
- Ability to distinguish a system relationship from its visual layout
Choose a useful representation, account for simplification and interpret checker findings within their real scope.
Key takeaways
- Measure authoring, rendering and revision as separate stages.
- Count accepted artifacts and include repair effort.
- Functional test cases do not establish a speedup.
Separate the work into measurable stages
A diagram workflow includes understanding source material, selecting a representation, generating markup, checking it, rendering it and revising it after review. A fast file render does not imply fast authoring, and fewer source documents do not necessarily imply a shorter agent conversation. Measure the stages that dominate your actual team workflow.
Progressive references offer a plausible way to avoid loading unrelated instructions, but no token-consumption comparison was performed here. Log the references actually read and the revisions needed for comparable tasks before estimating a benefit. Use a fixed task set with different relationships, rather than repeating one easy architecture example.
Include dependencies and rework
Static markup avoids the need for a running diagram backend, but delivery may still involve fonts and an export browser. PNG generation adds raster dimensions, browser startup and output storage; SVG keeps vectors but depends on the destination’s font behavior. These costs differ from authoring-model usage and should appear separately in a budget.
Track repair effort as well as generation latency. A diagram that needs repeated label relocation or semantic correction can be more expensive than its first-pass timing suggests. Count accepted artifacts, not simply files written, and record whether a reviewer could answer the intended question without opening the original system description.
Design an honest benchmark record
Use the same source graph, output audience, size and acceptance criteria when comparing approaches. Keep cold dependency setup separate from steady-state export. Record machine and browser versions where rendering is involved, and report failed or rejected artifacts instead of removing them from the denominator.
The sixteen geometry cases establish functional behavior only. We did not benchmark model cost, browser rendering, host startup, memory usage or reader comprehension. The example record deliberately leaves those measurements unknown. Replace them with observed values only after running a reproducible evaluation on your own approved environment.
Implementation steps
- 1
Define a representative task set and acceptance rules.
- 2
Record source, output settings and tool versions.
- 3
Separate cold setup from repeat rendering.
- 4
Report unknown values and rejected results honestly.
Copy-ready example
{
"benchmarkStatus": "not performed",
"authoringTokens": null,
"firstRenderMs": null,
"exportMs": null,
"revisionMinutes": null,
"acceptedArtifactCost": null,
"readerComprehensionGain": null
}Frequently asked questions
Do selective references prove lower token costs?
They describe a loading strategy. Actual savings depend on host behavior, task complexity and revisions, and require a measured comparison.
Can checker duration stand in for generation speed?
No. Parsing synthetic rectangles is a different workload from source understanding, agent authoring, browser rendering and editorial review.
Sources
- README.mdSource checked 2026-09-08
- skills/diagram-design/references/output-spec.mdSource checked 2026-09-08
- scripts/verify-geometry.pySource checked 2026-09-08