screenshot-critique
Use the unprimed sub agent as a second set of eyes before accepting visual work — MANDATORY before declaring any user-reported visual bug fixed or claiming a…
Skill by dzhng
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/dzhng-screenshot-critique
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: agent-building, testing, ui-design
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Node.js package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/screenshot-critique
What's inside
- Skill name
screenshot-critique- Description
- Use the unprimed sub agent as a second set of eyes before accepting visual work — MANDATORY before declaring any user-reported visual bug fixed or claiming a visual change verified; primed eyes pass defects fresh eyes catch.
- License
- MIT
- Instructions
- 164 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (3)
- SKILL.md
- receipts.eval.mjs · scripts/
- receipts.mjs · scripts/
SKILL.md (first lines)
# Screenshot Critique Use an unprimed sub-agent as a second set of eyes before accepting visual work. This is for visual defects, not pixel metrics; pair it with `compare-screenshots` when you also need numbers — including on a single shot with nothing to compare against, whose scene metrics say whether the frame has any content in it at all. ## Workflow 1. Capture or locate the exact PNGs/GIF frames under review. 2. Keep native-size evidence and create supplementary 2x-4x crops for every key feature, plus the full screenshot. Include complete endpoints and fades; recheck crop bounds after geometry changes. Crop selected units, city/town stacks, flags/poles, shadows, selection rings, labels/icons, roads, terrain features, water, and any artifact-prone area. If the complaint is about "too faint", "wrong order", or "not in perspective", the crop is mandatory. 3. Spawn one fresh explorer with `fork_context: false`; pass only the full images, the crops, and a short neutral task. Include the approved reference and user requirements when judging fidelity; withhold history, implementation details, prior verdicts and the expected answer. 4. Ask for concrete visible defects with confidence levels. Name likely risk categories: unit/prop depth ordering, layering, shadows, selection-marker contrast, ground-plane perspective, flag/pole attachment, label style and icon readability, blur, scale, lighting, artifacts, missing models, terrain feature readability, roads, water, and overall scan readability. 5. Run the returned critique through `scripts/receipts.mjs`. Every number it flags is a claim the critique cannot show a command for, and every file it flags is one the critique named but never wrote; strike those claims or re-measure them yourself before any of them reach a decision. 6. Compare the sub-agent's critique against your own inspection. Treat overlap as high-priority evidence. Treat novel high-confidence findings as bugs to inspect, not as taste notes to dismiss. 7. Record actionable findings in the spec, visual report, or next task plan before claiming the screenshot is accepted. ## Sub-Agent Prompt Use this shape, replacing the bracketed surface and attaching local images: ```text Fresh visual critique task. You have no project backstory and should only inspect the supplied screenshots and crops. First inspect the full screenshot for context, then inspect each crop at zoomed scale. Look for concrete visual/layout defects in [surface], especially unit/prop depth ordering, layering, shadows, selection-marker contrast, ground-plane perspective, flag/pole attachment, label style/icons, blur, scale, lighting, artifacts, missing models, terrain feature readability, roads, water, and scan readability. Do not assume these are correct. Return a concise list of issues you can see, with confidence and whether the issue is visible in the full image, the crop, or both. Describe what you see in words. You are looking at these images, not measuring them, so state no pixel value, luminance, percentage, ratio, count, or offset unless you ran a command that produced it and you quote that command and its output beside the number. "The hem reads clearly darker than the floor beside it" is a finding; "44.0 versus 33.6" without a receipt is a fabrication. The same goes for files: name no artifact you did not create with a tool you actually have. ``` Spawn config: - `agent_type`: `explorer` - `fork_context`: `false` - attach screenshots as `local_image` items - omit model overrides unless the user explicitly requests one - grant a measurement tool whenever you want numbers back — see Receipts ## Receipts A number in a critique is a **receipt or a guess**, and the two are indistinguishable once they are written down. An agent handed images and asked for defects will answer in whatever register the question was posed in: ask it how much darker the hem is and it returns a luminance mean, because that is what the answer is shaped like — not because it measured anything. Sometimes the estimate lands close and sometimes it inverts the finding; nothing in the report tells you which, and it reads as the strongest evidence there precisely because it is numeric. The same reflex names the crops and dumps it says it wrote. - **Match the tool surface to the output contract.** Before spawning, read your own prompt and ask which tool produces each claim you demanded. A judge with images and no shell can report what it sees and nothing else. If you want measurements, grant the tool that measures and name it in the prompt; otherwise stop asking for them. - **A number without its command is not a finding.** Every quantity must arrive with the command that produced it and that command's output, close enough to read together. Findings that fail this are struck, not discounted. - **Verify, don't trust the eye of the verifier.** Run `scripts/receipts.mjs` over the returned report — `node scripts/receipts.mjs <report.md>` — before you read the findings, so an unsupported number never gets to be persuasive first. It exits nonzero and prints each unbacked quantity and each file the report named but never wrote. `scripts/receipts.eval.mjs` is its own eval, over reports written in the eval whose right answer is known by construction, both directions pinned: run it after changing the checker, and never widen what counts as a receipt to make a failing case pass. - **Know which half of it is proof.** The file check is proof: the path is resolved on disk and a stated sha256 is recomputed, so an invented crop dies there. The number check is attribution, not execution — it asks whether a command is shown running with this number in its output, and a report that invents the whole transcript passes. Nothing readable from static text can separate an invented transcript from a real one. So a clean run means the claims are attributable and the files are real, not that anything ran; ask for the artifact and the hash when a number has to hold weight. - **Ordinal words are still findings; numbers are not.** "Clearly darker", "roughly a third of the panel", "the left edge is cut off" are what an eye can honestly report. Push the critique toward that register rather than trying to make its arithmetic more careful. - **The measured pass is a separate run.** When the words justify numbers, take the measurement yourself with `compare-screenshots`, or re-run the critic with a shell and this same receipt rule. Never repair a report by asking its author to double-check its own figures. ## Rules - **Mandatory before "fixed":** never declare a user-reported visual bug fixed on your own inspection — your eyes are primed by the fix you just made. Run
From dzhng/skills, MIT.
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- Stars
- 1,379
- Forks
- 84
- Open issues
- 0
- Last commit (this folder)
- Oct 14, 2026 (13 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/dzhng/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
screenshot-critique skill. Agent Mart. https://agentmart.dev/skills/dzhng-screenshot-critique (last updated 2026-10-24)