experience-judge

name

experience-judge

description

Score the visual quality of the public surfaces the experience-audit organ captured — layout, typography, coherence, trust — and record verdicts. Use when asked to judge, review, or grade how the public sites/pages LOOK (not just whether they load), or to run the visual half of the experience audit. The mechanical half (reachable/fast/light/no-errors) is scripts/experience-audit.py; this skill is its human/model-in-the-loop visual complement.

experience-judge

experience-audit.py measures the MECHANICAL rungs of a visitor's experience (reachable, fast, light, no broken images, no console errors, has a title). It cannot judge whether the page looks good. This skill is that missing rung: read the latest capture, score each surface on layout, typography, coherence, trust (each 0-5), and append the verdict to the judgment register so the effector can file bounded visual-fix tasks.

A judgment is pinned to the exact pixels it judged: cite the screenshot's sha256. A re-sweep that changes the capture invalidates the old verdict — so judge against the CURRENT artifact only.

Steps

1. Read the latest sweep artifact. logs/experience-audit.json (schema limen.experience_audit.v1). If it is missing or stale, run python3 scripts/experience-audit.py --sweep first (needs playwright + chromium for screenshots; the http tier captures no shots, so a real visual judgment requires the playwright tier). Each surface entry carries its url, screenshot_sha256, captured_at, and the mechanical rungs already scored.

2. View each surface's shot. logs/experience/shots/<id>.png (latest capture, overwritten each sweep). Confirm the file's sha256 matches the artifact's screenshot_sha256 before judging — if they differ, re-sweep (the shot is newer/older than the recorded judgment target).

3. Score each surface 0-5 on:

   • layout — spacing, alignment, hierarchy, responsive fit
   • typography — legibility, scale, consistent families/weights
   • coherence — visual consistency with the rest of the estate; does it feel like one brand
   • trust — does it read as credible/professional to a first-time visitor (the demand-funnel test) Set verdict: pass when the surface is clearly acceptable to show a buyer; verdict: fail when a visible defect would repel the visitor. Record concrete, PII-clean, public-safe defects and one suggested_fix.

4. Append rows to the register. For each judged surface, append a row under its id in institutio/observatory/experience-judgments.yaml (newest last), with judged_at, captured_at (copied from the artifact), screenshot_sha256, model (your model id or a human handle), verdict, scores, defects, suggested_fix. Keep the existing schema exactly — run python3 scripts/experience-audit.py --doctor and confirm it exits 0 (it validates verdicts and 0-5 scores).

5. Open a PR. Branch feat/experience-judgments-<mmdd> off origin/main in a worktree, stage ONLY institutio/observatory/experience-judgments.yaml, commit (body ends with the Co-Authored-By: Claude Fable 5 line), push, and open a PR whose body cites each judged surface's screenshot sha256 (the pixels the verdict is pinned to) and summarizes the scores. Do not commit anything under logs/ (gitignored artifacts). The effector (generate-experience-backlog.py) reads the merged verdicts and files bounded EXP-<surface>-visual tasks for any verdict: fail.