Visual Prompt Craft — structured prompts for blog imagery

Bad images come from bad prompts. A one-line prompt ("diagram comparing AI girlfriend apps") produces generic slop. A structured 200-400 word prompt with explicit composition, materials, lighting, palette, and mood produces agency-grade output from the same model. This skill defines the mandatory prompt anatomy, calibrated against reference prompts of proven quality (see references/example-prompts.md — study one before writing your first prompt of a session).

The 9-part prompt anatomy (write ALL parts, in this order)

1. Subject declaration — one dense opening sentence: image type + subject + aesthetic school + mood. Example pattern: "Horizontal editorial illustration for a premium tech-lifestyle blog article about X. Minimalist editorial layout combined with [style], [mood] mood."

2. Canvas — aspect ratio and exact background: color WITH hex code, texture/grain. Blog targets: hero/featured 16:9, in-article 3:2, comparison panels 16:9.

3. Composition map — name every zone and what sits in it (center subject, supporting elements upper-right, caption strip bottom). Use positional language: "spanning ~60% width, centered", "arranged diagonally from upper-right to lower-left", "tilted at 35°".

4. Per-element specification — the quality multiplier. EVERY major element gets its own block: material ("hand-thrown stoneware", "brushed aluminum", "soft-touch matte plastic"), exact color (+hex), state/emotion, position/rotation, and 2-3 micro-details ("visible brushstroke texture", "small beads of melted snow on her temples", "subtle paper grain"). Micro-details are what make renders read as photographed rather than generated.

5. Character & emotion — give subjects (even objects) personality and a felt emotional state. Faces: specify exact expression mechanics ("one winking eye and a small crooked grin", "lips slightly parted as if mid-breath, jaw relaxed"). For people: hair, skin detail, wardrobe texture, what they are doing and feeling — never just "an attractive woman".

6. Style triangulation — anchor the style with X-meets-Y references AND explicit negatives: "1970s Scandinavian children's book illustration meets Eastern European folk ceramics — NOT anime, NOT chibi". The negative side is as load-bearing as the positive.

7. Lighting & render spec — light direction and quality ("soft studio lighting from upper-left", "cinematic side-light with snow-bounce highlights"), material behavior ("authentic glazed ceramic behavior", "sharp lens reflections"), depth of field, shadows ("soft cast shadows beneath, as if gently levitating").

8. Palette block — every color in the image listed with hex codes, mapped to its element.

9. Mood line + quality anchor — closing line: comma-separated mood adjectives, then the bar: "Awwwards / Behance site-of-the-day quality, 4k." For photoreal: "crisp commercial beauty photography, cinematic color grading."

If in-image text/UI is needed, add a typography block: font archetype by name ("bold condensed uppercase grotesk like Druk Wide Bold"), casing, tracking, and the exact strings. Keep in-image text minimal — models still mangle long copy.

Blog visual archetypes (pick one, then apply the anatomy)

• Hero / featured image — the article's emotional headline. Photoreal or stylized-3D scene expressing the article's core promise. This is the image that earns the click from SERP/social.
• Editorial diagram — concept made spatial (hub-and-spoke, decision tree, layered stack). Clean vector-editorial style, brand palette, generous whitespace, max 7 labeled nodes.
• Comparison panel — N options side by side as styled cards with consistent iconography. NOT a screenshot of a spreadsheet.
• Photoreal scene — a person in a moment that illustrates the section (using an AI companion app at night, morning coffee with chat open). Cinematic, shallow depth of field, full per-element + lighting spec.
• Data chart — matplotlib renders from {slug}-data.json stay canonical for real numbers; this skill styles their framing (title card, palette), never invents data points.

Pleasur.AI brand constraints (hard)

• Palette, persona and voice: pull from brand-config.md (visual-gen config section) — do not invent.
• Adult brand, tasteful: suggestive at most, NO nudity/explicit imagery in blog visuals; 18+ adult subjects only; AI-generated persons only — no real-person likeness, no celebrity resemblance, no deepfake framing.
• No real third-party logos or trademarks; fictional/generic glyphs only.
• Internal-stack ban applies to image text, captions and alt text (no tool/vendor names).
• Alt text: written for readers and SEO (descriptive, keyword-aware), never the generation prompt.

Workflow (mandatory)

1. For each [VISUAL:type=...] placeholder, draft the full 9-part prompt in content-pipeline/images/{slug}/prompts.md BEFORE any generation call.
2. Self-check against the anatomy: all 9 parts present? Per-element micro-details? Negatives? Palette hexes? If any part is missing, the prompt is not done.
3. Generate, then judge the output like an art director: composition, faces/hands, text artifacts, brand fit. If weak, diagnose WHICH anatomy part under-specified the failure, strengthen it, regenerate (up to 2 retries per visual), and log the diagnosis in prompts.md.
4. Record final prompt + model + retry count in the visuals manifest (manifest.json).

The bar: every blog visual should look like it came from a design agency, not a stock generator.