mtarcure/claude-vibe-squad/visual-regression-baseline

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Use when a known-good render and candidate captures must be compared reproducibly: lock viewport, scale, theme, locale, and time; mask declared dynamic regions; produce tolerance-bounded pixel/perceptual deltas; and classify each delta before replacing the reference set.

Install

npx vskill@latest install mtarcure/claude-vibe-squad/visual-regression-baseline

SKILL.md

Visual Regression Baseline

Capture stable, comparable visual references so a UI/render change can be judged as intended design movement vs. accidental drift — a deterministic diff, not a screenshot glance.

Steps

  1. Define the capture set. Enumerate the states that must be verified: routes/screens, key components,

breakpoints (mobile/tablet/desktop), theme variants (light/dark), and salient interaction states (hover/focus/error/empty/loading). Each entry is one named, reproducible capture.

  1. Establish the baseline. On the known-good build, capture each entry to an immutable, named reference

(via chrome-devtools/playwright take_screenshot/browser_take_screenshot, fresh Chrome). Record the capture conditions with it: viewport size, device-scale factor, color scheme, locale, and the app build/commit. A baseline without its conditions is not a baseline.

  1. Neutralize non-determinism BEFORE diffing. Freeze or mask sources of pixel noise that are not the

change under test: fixed clock/seeded RNG, disabled animations/transitions, stable fonts (wait for webfont load), and masked dynamic regions (timestamps, avatars, ads, carousels, generated IDs). Masks are declared per-entry and versioned with the baseline — an unmasked dynamic region is a false FAIL.

  1. Re-capture deterministically. On the candidate build, re-capture the SAME set under the SAME recorded

conditions. Any condition mismatch (viewport, scale, theme, locale) invalidates the comparison — re-capture, do not diff across conditions.

  1. Diff and compare. Compare candidate vs. baseline per entry. Use a pixel/perceptual diff with a declared

tolerance (anti-aliasing/sub-pixel threshold) and the entry's masks applied. Produce a diff artifact (highlighted delta image) for every non-identical entry.

  1. Human/`view_image` review of diffs. A non-zero diff is a SIGNAL, not a verdict. Review each diff image

(view_image on the codex lane, or a lane image-read) and classify: intended (accept → promote to the new baseline, with a note on what changed and why), or regression (reject). Never auto-accept a diff to silence it.

What counts as a regression FAIL

  • A visual delta outside tolerance in an UNmasked region that was NOT an intended, reviewed change.
  • A capture that could not be produced under the recorded conditions (broken render, crash, missing state).
  • A diff accepted without human/view_image classification (an unreviewed baseline promotion is itself a FAIL).

Acceptance

  • Every verified state has a named baseline WITH its recorded capture conditions and declared masks.
  • Candidate captures are produced under identical conditions; cross-condition diffs are rejected, not tolerated.
  • Every non-identical entry has a diff artifact AND a human/view_image classification (intended vs regression).
  • Baseline promotion is explicit and attributed; dynamic-region masking is versioned with the baseline.
  • The tool's numeric diff is never the sole verdict — an out-of-tolerance unmasked delta blocks acceptance

until reviewed, and a masked/tolerated delta is documented, not hidden.

When to invoke

  • The S4 required visual-verify gate of project/web-app and project/game-production (and any card that must

judge a UI/render change).

When NOT to invoke

  • No visual surface under test (headless/backend-only work).
  • As a substitute for accessibility (wcag-conformance-audit) or functional e2e — it verifies looks-right, not

works-right or accessible.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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