compare-screenshots
Compare screenshots against the intended design, distinguishing approved references from historical baselines.
Skill by transitive-bullshit
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/transitive-bullshit-compare-screenshots
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, writing
Reviews
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What's inside
- Skill name
compare-screenshots- Description
- Compare screenshots against the intended design, distinguishing approved references from historical baselines. Use for iterative implementation matching, before/after visual review, objective image telemetry, or checking a lone capture for flat, empty, or badly framed content.
- License
- MIT
- Instructions
- 179 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- name: "compare-screenshots" does not match folder "skill"
Files (36)
- SKILL.md
- reference-landmarks.md · references/
- subagent-visual-review.md · references/
- visual-parity-diff.eval.mjs · scripts/
- visual-parity-diff.mjs · scripts/
- README.md · test/fixtures/target-without-helper-packages/
- fixture.png · test/fixtures/target-without-helper-packages/candidate/
- fixture.png · test/fixtures/target-without-helper-packages/reference/
- visual-parity-diff.test.mjs · test/
- LICENSE · vendor/pixelmatch/
- index.js · vendor/pixelmatch/
- package.json · vendor/pixelmatch/
- LICENSE · vendor/pngjs/
- bitmapper.js · vendor/pngjs/lib/
- bitpacker.js · vendor/pngjs/lib/
- chunkstream.js · vendor/pngjs/lib/
- constants.js · vendor/pngjs/lib/
- crc.js · vendor/pngjs/lib/
- filter-pack.js · vendor/pngjs/lib/
- filter-parse-async.js · vendor/pngjs/lib/
- filter-parse-sync.js · vendor/pngjs/lib/
- filter-parse.js · vendor/pngjs/lib/
- format-normaliser.js · vendor/pngjs/lib/
- interlace.js · vendor/pngjs/lib/
- packer-async.js · vendor/pngjs/lib/
- packer-sync.js · vendor/pngjs/lib/
- packer.js · vendor/pngjs/lib/
- paeth-predictor.js · vendor/pngjs/lib/
- parser-async.js · vendor/pngjs/lib/
- parser-sync.js · vendor/pngjs/lib/
- parser.js · vendor/pngjs/lib/
- png-sync.js · vendor/pngjs/lib/
- png.js · vendor/pngjs/lib/
- sync-inflate.js · vendor/pngjs/lib/
- sync-reader.js · vendor/pngjs/lib/
- package.json · vendor/pngjs/
SKILL.md (first lines)
# 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](../design-with-images/SKILL.md) 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](references/reference-landmarks.md) 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:From transitive-bullshit/skills, MIT.
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Signals
- Stars
- 4
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Oct 18, 2026 (10 days ago)
- Last release
- Sep 6, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/transitive-bullshit/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
compare-screenshots skill. Agent Mart. https://agentmart.dev/skills/transitive-bullshit-compare-screenshots (last updated 2026-10-24)