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aesthete-agent

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Color palette generation, frontend aesthetics, UI color schemes — mechanically validated color output for UI/HTML, SVG slides, video covers, charts and posters. An 119-palette evidence library matched by register, OKLCH harmony generation, luminance ramps and image extraction; 60-30-10 role mapping with semantic colors; a 5-dimension scorecard plus WCAG contrast, color-blind simulation and AI-slop detection as four mechanical gates; card and six-scene visualizer previews; a preference-learning profile that evolves with every verdict. Named theme packs, full contrast matrices, CB-safe data-viz palettes, five-lens critique reports, and APCA (WCAG 3 draft) perceptual contrast included. Pure stdlib, offline. Skip when pure text layout, translation or polishing, copywriting, or any task unrelated to color and frontend aesthetics. Trigger words — color palette, color scheme, palette generation, frontend aesthetics, UI color, design aesthetics, hex, contrast, WCAG, color blind, visual taste, aesthete-agent.

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# aesthete-agent — Frontend Aesthetics Engine

Why LLM color output is bad, with evidence: models treat hex codes as text, regress to the median of training data (the frontend-tutorial median is purple/indigo), and cannot actually see color. The fix is not a better model — it is **outsourcing taste to process**: manifesto first, role mapping, mechanical validation, in-context self-review.

⛔ Iron rules:
1. **No bare palette writing.** Every color task goes through the six-step method; step 6 (five-lens review) is not skippable.
2. **No runtime access to the source site** — no public API, direct connections hit Cloudflare 403; all data ships offline.
3. **The brief is law**: colors and aesthetics pinned by the user are binding constraints. Already-shipped brand systems are out of scope for re-paletteing.
4. **Bounded verification**: build fully → one batched review → one batched fix round → at most one more confirmation → **stop polishing**.
5. **Candidate discipline**: user verdicts require ≥2 full-page candidates (`make_variants.py` builds the comparison sheet); a single proposal is not sent for review.
6. **Verdict write-back**: every accept/reject lands in `profile.json` (machine-read) and `critique.md` necropsies (failure modes) — no write-back, no growth.

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