Quality standards and guardrails

Problem

As more contributors add chart types, interaction behaviors, and styling options to the graph library, there is no enforced standard for what constitutes acceptable output quality. One contributor's tooltip implementation may differ from another's; hover states may be inconsistent across chart types; accessibility (contrast ratios, screen reader labels, keyboard navigation) may be present on some charts and absent on others. Without defined quality standards and automated guardrails (visual regression tests, accessibility linting, interaction consistency checks), the library's output will drift toward inconsistency — making it feel like a collection of one-offs rather than a coherent visual system.

Context

• Teams are judging readiness for visual language, chart defaults, interaction behavior, and differentiated styling inconsistently because there is no single quality bar that covers correctness, UX clarity, failure handling, and maintenance expectations.
• Without explicit standards, work gets approved on local intuition and later re-opened when another reviewer finds a gap that was never written down.
• Expected touchpoints include dataface/core/render/chart/, chart design docs, examples, and visualization test coverage, review checklists, docs, and any eval or QA surfaces used to prove a change is safe to ship.

Possible Solutions

• A - Rely on experienced reviewers to enforce quality informally: flexible, but it does not scale and leaves decisions hard to reproduce.
• B - Recommended: define a concise quality rubric plus guardrails: specify acceptance criteria, required evidence, and clear anti-goals so reviews are consistent.
• C - Block all new work until a comprehensive handbook exists: safer in theory, but too heavy for the milestone and likely to stall momentum.

Plan

1. List the failure modes and review disagreements that matter most for visual language, chart defaults, interaction behavior, and differentiated styling, using recent work as concrete examples.
2. Turn those into a small set of quality standards, required validation evidence, and explicit guardrails for unsupported or risky cases.
3. Update the relevant docs, task/checklist expectations, and test or QA hooks so the standards are actually enforced.
4. Use the rubric on a representative set of recent or in-flight items and tighten the wording anywhere it still leaves too much ambiguity.

Implementation Progress

Review Feedback

• [ ] Review cleared