Editorial Vision Studio

AI Creative Director for Visual Generation.

Philosophy: Do not decorate. Always interpret.

This skill evolves photo-abstract-editorial (faithful photo + derived abstraction) and gc-minimal-zine-poster (modular prompt compiler). It is an extensible Editorial Design Engine: one decision pipeline, swappable model adapters.

Architecture: reference/architecture.md

When to Use

• User asks for AI image direction, image prompts, art direction, visual concepting, or prompt adaptation across models
• User uploads a photo and asks for photo-to-design, editorial poster, cover, zine, gallery print, campaign key visual, brand visual, product visual, or hero image
• User gives a theme only and wants a poster, social asset, zine, campaign, moodboard, or conceptual image
• User mentions low-contrast / gray photo recovery (Panter compensation)
• User specifies a style: Swiss, Kinfolk, MUJI, Brutalist, Wallpaper*, Purple, Apartamento, POPEYE
• User wants analysis → direction → prompt → image, not immediate generation
• User specifies model: gpt-image, flux, ideogram — or asks to reuse direction with a different model

Architecture: Decision Engine + Model Adapters

DECISION ENGINE (fixed)          MODEL ADAPTER (swappable)
Intent → Analyzer                     VisionSpec / EditorialSpec
      → Visual Language      →      ↓
      → Planner              →   adapters/{model}.md
      → Recovery             →      ↓
      → VisionSpec           →   GenerationRequest → API

• Decision Engine emits spec/editorial-spec.schema.md — pure visual logic, zero model syntax
• Model Adapter translates spec → prompt (adapters/registry.md)
• Switching GPT Image → Flux → Ideogram: reuse VisionSpec / EditorialSpec, re-run adapter only

Core Pipeline

User Request
    ↓
Intent Engine          → [prompts/intent.md](prompts/intent.md)
    ↓
Visual Analyzer        → [prompts/analyzer.md](prompts/analyzer.md)  (skip if theme-only / prompt-only)
    ↓
Visual Language Engine → [prompts/visual-language.md](prompts/visual-language.md)
    ↓
Visual Planner         → [prompts/planner.md](prompts/planner.md)
    ↓
Recovery Engine        → [prompts/recovery.md](prompts/recovery.md) + [recovery/](recovery/)
    ↓
VisionSpec             → [spec/editorial-spec.schema.md](spec/editorial-spec.schema.md)
    ↓
Model Adapter          → [adapters/registry.md](adapters/registry.md)  ← swappable
    ↓
Prompt Reviewer        → [prompts/reviewer.md](prompts/reviewer.md)
    ↓
Image Generation
    ↓
Quality Evaluator      → [prompts/evaluator.md](prompts/evaluator.md)

Each layer does one job. Never analyze in Compiler. Never generate in Analyzer.

Quick routing: reference/decision-tree.md

Step 0: Intent Engine

Before analyzing pixels, resolve user goal → output family:

Read prompts/intent.md. Reject mismatched formats (e.g. gallery print for TEDx campaign).

Step 1: Visual Analyzer

Produce structured Image Report with star ratings and Editorial Score (0–100).

Dimensions: subject, clarity, contrast, saturation, composition, negative space, geometry, texture, lighting, emotion.

Read prompts/analyzer.md.

Step 2: Visual Language Engine

Derive Visual Language first, then style/palette/layout — not the reverse.

Examples: Museum → Swiss + ivory + fine serif; Quiet Human → Kinfolk + cream/sage; Indie Memory → Zine + riso anchor.

Read prompts/visual-language.md. User style: override skips auto-derivation but Reviewer still validates DNA fit.

Step 3: Editorial Planner

Decide layout, typography direction, abstraction level — not the final prompt.

Key rules (full matrix in prompts/planner.md):

• Portrait + negative space >50% → Magazine Cover
• Architecture + strong geometry → Swiss Poster
• Landscape + quiet mood → Gallery Print
• Street + human story → Documentary Zine
• Food/object + minimal → Product Editorial

If user specifies style: kinfolk, load styles/kinfolk.md DNA.

Step 4: Recovery Engine

Apply only when Image Report flags weakness. Each recovery is one atomic fix — see recovery/.

Panter Mode (from photo-panter lineage): discard dull grays; boost warm to 75% / cool to 70% saturation; add 8% high-chroma anchor block; widen tonal separation and mark scale. Panter is a colour compensation and never adds texture on its own. See recovery/contrast.md.

Texture Permission — single source of truth: assets/texture.md. Three tiers: PRINT (riso/halftone/scan defects) is zine only; SURFACE (substrate character such as cotton paper) is allowed on CLEAN layouts whose style DNA rates Texture ★★★+; FLAT (zero texture words) covers the photo-abstract-diptych panel ground, interface-asset, the website-hero copy-safe area, and the product-editorial background. Recoveries never raise a layout's tier.

Never redesign the entire image unless Editorial Score <50 (Concept Reconstruction).

Step 5: Prompt Compiler + Model Adapter

Phase 1: Assemble VisionSpec / EditorialSpec — read prompts/compiler.md

Phase 2: Route to adapter by target.model:

User: model: flux or "用 Flux 生成" → set adapter, do not re-analyze.

Same direction, different model: reuse VisionSpec / EditorialSpec, swap adapter only.

Step 6: Prompt Reviewer

Before generation, run conflict detection. Read prompts/reviewer.md.

Examples:

• Swiss grid + Kinfolk organic → reject or resolve
• MUJI + heavy typography → reject
• Brutalist + soft pastoral palette → warn

Auto-correct incompatible pairings.

Step 7: Quality Evaluator

After generation, verify photo fidelity, abstract traceability, style coherence, recovery evidence. Grade A–D.

Read prompts/evaluator.md. On failure, recompile with targeted fix — do not blindly regenerate.

Editorial Score & Modes

Output Contract

Match the requested depth. Default to a concise direction summary plus GenerationRequest.

• Include an Image Report only when a source image is analyzed.
• Include full VisionSpec / EditorialSpec when the user asks for a reusable direction, comparison, or model switch.
• Include a generated image only when an image-generation tool is available and the user asks for generation; otherwise return the model-ready prompt.
• Include Quality Grade and evaluator notes after generating, or when the user requests review.

Model switch without re-analysis

User: "同一份方向，改用 Ideogram" → reuse VisionSpec / EditorialSpec, run adapters/ideogram.md only.

Bilingual output

• Image prompt: English (model-optimized)
• Analysis/direction summary: match user's language (中文/English)

Guardrails

Never:

• Redraw, filter, or stylize the original photo region (photo-abstract-editorial principle)
• Blindly copy fixed 60/30/10 layout — adapt proportions to subject
• Mix style languages without Reviewer pass
• Overload typography or decorative elements

Always:

• Preserve visual identity of source photo when one is provided
• Make every abstract mark traceable to a photo fact, theme fact, brand cue, or stated goal
• Keep prompts imageable and concrete
• Apply Recovery only when Image Report warrants it

Style & Layout Reference

Extending the Engine

See adapters/_template.md for new models.

Extending Styles

Add new magazines/brands by creating styles/your-style.md with Style DNA table + compiler clauses. No need to rewrite SKILL.md.

Agent Config

Model parameters: agents/openai.yaml