Visual judge
Judge a UI screenshot against written acceptance criteria — a vision model describes the rendered page neutrally, then an LLM judge compares it to the spec and returns strict JSON with pass/fail and per-violation severity. Use to self-check frontend changes from a screenshot, or as a CI visual-regression gate that only fails when a human would care.From its SKILL.md
npx -y skills add nanoodlecom/noodle-skills --skill visual-judgeAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.7 KB, 891 tokens by cl100k_base, as published. Nobody here has run it
Visual judge
Runs the bundled nanoodle workflow workflows/visual-judge.noodle-graph.json against the
NanoGPT API in two stages: a vision model describes the screenshot (Screenshot)
exhaustively and neutrally — it never judges — then an LLM judge compares that description
against your acceptance criteria (Spec) and returns strict JSON (Verdict):
{"pass": bool, "violations": [{"issue", "severity"}]} with severity
critical | major | minor. Requires Node.js >= 20 and the nanoodle npm package
(npx nanoodle fetches it).
Typical agent use: after editing a component, screenshot the page (Playwright, your
project's verify tooling), run this, and only commit when pass is true — or read the
violations and fix them.
API key
The run needs a NanoGPT API key. Use whichever is available:
NANOGPT_API_KEYalready set in the environment — prefer this; no extra flags.- A
.envfile containingNANOGPT_API_KEY=...— pass--env-file <path>only when the key is not already in the environment. (With this CLI,--env-fileoverrides ambientNANOGPT_API_KEY.)
Never print the key.
Run
From this skill's directory (or prefix paths if running from elsewhere). Replace the screenshot path and the spec with the real ones:
npx nanoodle run workflows/visual-judge.noodle-graph.json \
--input "Screenshot=@/path/to/screenshot.jpg" \
--input "Spec=The pricing page must show three plan cards, a visible Buy button on each, and no overlapping sections."
A spec that lives in the repo attaches as a file instead (.md/.txt are read as text):
--input "Spec=@docs/page-spec.md"
The output is text, so no --out directory is needed. The stdout JSON summary carries the
verdict; a shell gate is one jq away:
npx nanoodle run workflows/visual-judge.noodle-graph.json \
--input "[email protected]" --input "[email protected]" \
| jq -e '.outputs.Verdict | fromjson | .pass' > /dev/null # exit 1 = failed the check
Inspect the interface anytime with:
npx nanoodle inspect workflows/visual-judge.noodle-graph.json
Inputs
| Key | Required | What to pass |
|---|---|---|
Screenshot | yes | Screenshot file path (@path) or https URL |
Spec | yes | Acceptance criteria as prose: what must be visible, what must not overlap, required copy |
System prompt | no | Override the judge's instructions (default judges layout vs spec, ignores cosmetic noise) |
Outputs
Verdictis a text output: printed on theVerdict:stderr line and included in the stdout JSON asoutputs.Verdict— itself a JSON string:{"pass": boolean, "violations": [{"issue": string, "severity": "critical"|"major"|"minor"}]}. Fail hard oncritical/major; treatminoras annotation.
Serve as an MCP tool
The same graph file works unchanged as a typed MCP tool with Screenshot and Spec
required — the agent can self-check its own UI changes before committing:
mkdir -p ~/noodles && cp workflows/visual-judge.noodle-graph.json ~/noodles/visual-judge.json
claude mcp add nanoodle -- npx nanoodle-mcp --graphs ~/noodles
Cost
Each run costs about $0.01 in NanoGPT credit (one openai/gpt-5.4-mini vision call
plus one zai-org/glm-5.2 judge call at temperature 0). Runs spend real credit — tell the
user before running repeatedly.
What ships with it: 1 file
2.1 KB alongside SKILL.md