Compare Screenshots

Judge images against the intended result. A user-approved design is the target; a historical baseline is only an earlier attempt and may be wrong. Metrics locate differences, never decide correctness. Use design-with-images for the full exploration-to-implementation loop.

Workflow

1. Establish the target. Use the user-approved reference and stated design requirements when available; do not replace them with your own taste. Otherwise derive the target from the visual requirement, what the thing depicts in reality and the domain skill that owns the look. Write it down in one or two concrete sentences ("low sun should cast long shadows east; trees fill the canopy; labels stay legible at this zoom").
   • If the right answer isn't clear — competing valid readings, a taste or product-intent call, a tradeoff only the owner can settle — stop and ask the user what the correct answer should be. Show them the comparison. Do not quietly default to the baseline to avoid asking; that bakes in whatever the baseline got wrong.
2. Confirm comparability so the differences you see are real, not capture artifacts: same viewport, DPR, route/page, frozen time/tick, camera intent, UI state, data, fonts/assets where they matter. If not comparable, fix capture setup or compare only a crop/feature where the mismatch is harmless.
3. Measure the approved design. For reference matching, read and complete Reference Landmarks before changing code or accepting a candidate. Then generate artifacts sized to the question: side-by-side, key-feature crops/zooms, grayscale, absolute grayscale heatmap, pixelmatch diff, per-image Sobel/edge maps, edge-difference heatmap, JSON metrics.
4. Inspect boundaries before judging the whole. For every changed visual effect, inspect all sides at native scale and in matched detail crops. Include the effect's full fade and surrounding space; a crop ending at the component box hides spill. Compare top/right/bottom/left extents separately, anchored to visible text, rules or silhouettes rather than inferred CSS bounds. Check every foreground feature crossed by the effect (lines, icons, text, adjacent panels): brightness, color, sharpness and continuity must match the target. A readable line can still be incorrectly dimmed. Record each check as reference observation → candidate observation → pass/fix/uncertain, with its crop. Use the landmark table to resolve local distances and contrast; full-frame averages cannot settle a local defect.
5. Judge each divergence against the target. For every place the two images differ, name what is actually there in plain terms — missing content, wrong camera, bad hierarchy, weak contrast, wrong depth, text overlap, layout shift, clipped edge, unexpected blur, style mismatch — and decide which side is closer to correct. The answer can be the candidate, the baseline, both wrong, or a genuine toss-up.
6. Get a neutral second opinion for disputed or high-stakes calls: a fresh subagent given only the two images and neutral labels, per references/subagent-visual-review.md.
7. Resolve mismatches to an approved target. When implementing a selected design, record material differences in spacing, shape, softness, typography and hierarchy; revise, recapture and repeat until resolved or the user changes the target. Do not silently exempt a difference because the code is simpler.
8. Conclude with one verdict: candidate is less wrong (accept, and re-bless the baseline if one exists), baseline is less wrong (reject), both wrong (another pass needed — say what's still off), or unclear (ask the user). Accept only when every boundary/overlap check passes or has an explicit user-approved deviation; uncertainty requires closer evidence, not a pass. Overall resemblance or a positive second opinion cannot cancel a local defect. Never accept on a lower score alone or reject on a higher one. Never hide content, blur detail, crop away differences, or make the capture less truthful to move a number.

Useful Metrics

Pick metrics that answer the question. For full visual comparisons, report:

• mae: mean absolute grayscale difference, 0..255, lower is closer.
• rmse: grayscale root mean square error, lower is closer.
• diffRatio16, diffRatio32, diffRatio64: fraction of pixels over each grayscale delta threshold.
• pixelmatchRatio: mismatch ratio from pixelmatch over grayscale images.
• edgeEnergyCurrent and edgeEnergyCandidate: average Sobel edge strength.
• edgeEnergyRatio: candidate/current. Far below 1 usually means missing geometry, props, labels, or terrain; far above 1 usually means noisy or incorrect detail.
• edgeDiffRatio32: fraction of pixels whose Sobel edge differs materially.
• avgLuminanceCurrent, avgLuminanceCandidate, avgLuminanceDelta: average brightness and delta. Use when a render is visibly too dark/light even if a broader distance score improves.
• Content proxies relevant to the scene: black/void ratio, terrain-like ratio, water-like ratio, team-color ratio, label/text mask ratio.

For UI/document/layout reviews, also use crop bounds, text/foreground mask coverage, contrast checks, edge clipping, element positions, and before/after dimensions when those beat global pixel distance.

Single-Image Metrics

Every metric above measures one image against another, so none of them can answer "is this capture worth anything" when there is nothing to compare it to — and the pair score is symmetric, so an enormous distance never says which side is the empty frame. A few absolute numbers do, computed on a coarse grid from a single PNG:

• colorEntropyBits under ~3.0, or dominantColorShare over ~0.6: one colour owns the frame. A sparse scene, an unlit one, or a subject that never drew.
• edgeDensity under ~0.04: almost no form anywhere. Empty framing, a primitive-dominant scene, or the subject sitting outside the crop.
• luminanceContrast under ~60: fog, darkness, or haze compressing the whole frame into one band.
• transparentShare above 0 on a capture that should be opaque: the capture itself is wrong. A transparent pixel keeps whatever RGB it was left with, so an invisible frame can look rich until it is composited — the scene metrics composite before measuring, and name the invisible share rather than letting you infer it. The pair metrics above still read stored RGB, so this field is where transparency gets told either way.

These are thresholds for suspicion, not gates. A deliberately minimal design, a night scene, an empty-state screen, and a whiteboard all trip them honestly. Use them to decide where to look, then say what the frame is actually doing — never adjust a capture to raise a number, which is the same failure as cropping away a difference.

Distance Score

When a single fixed-pair number is useful, this default works for structural changes:

distance = 0.35 * diffRatio32 + 0.25 * pixelmatchRatio + 0.25 * edgeDiffRatio32 + 0.15 * min(1, abs(log2(edgeEnergyRatio)))

It measures distance from the other image, nothing more. Because the baseline can be wrong, a distance of 0 is not success and a large distance is not failure — a richer scene, clearer models, stronger labels, real depth, or better lighting all legitimately raise it. Use the score to find where the images move; decide who is right in step 5. Name the field for what it measures (distance, not "parity") so no one reads it as a verdict.

Report the full-frame score and, when UI dominates the shot, a labeled world-crop score. Use the world-crop score to locate renderer movement and keep the full-frame score so UI/camera mistakes stay visible.

Score Discipline

• Quote the previous and new distance for the same pair each iteration, then say whether the movement is toward the target, away from it, or diagnostic noise.
• Prefer edge metrics for missing-content bugs. A flat top-down map can show a deceptively moderate grayscale diff while edge energy proves trees, roads, city forms, or army silhouettes are absent.
• Segment out stable UI when it dominates and the question is the world render; keep a full-frame score too, labeled.
• If the camera is wrong, pixel scores are diagnostic only. Fix camera intent first, then judge the render.

Tooling

Visual comparison is integrated into buddhi-cli:

• buddhi scripts visual-parity-diff is a built-in helper with pure Python PNG decoding and pixel-diffing (no external binary or Node dependencies needed). Run it with REFERENCE_DIR=<png-folder>, CANDIDATE_DIR=<png-folder>, and optional OUT_DIR=<artifact-folder>. REPORT_ORDER=a,b,c pins ordering; CROPS_JSON=<file> adds labeled crops (keyed by image id, each crop in pixels or { "unit": "ratio" } normalized bounds). Pair reports carry a sceneMetrics block per side.
• Drop REFERENCE_DIR to run the same helper on a folder with no counterpart: it writes scene-metrics.json with the single-image numbers above and no diff artifacts. Use it on a lone screenshot, on a full capture set before anyone reviews it, or to find which side of a large distance is the empty one.
• For other tasks, adapt the same artifact set rather than adding one-off scripts to the application. Extend the helper if a needed pair is uncovered.

References

• references/reference-landmarks.md: landmark table for measuring approved designs.
• references/subagent-visual-review.md: neutral subagent prompt/config for an independent judgment when history could bias you.