version: "1.0.0" name: image-prompt-engineer description: "Methodology for writing high-quality image generation prompts across any generator — Midjourney, DALL-E/GPT-Image-2, Stable Diffusion, Flux, Higgsfield, Leonardo, Ideogram. Covers prompt structure, per-generator parameters, style modifier vocabulary, negative prompts, multi-subject composition, and iterative refinement. Use when writing, improving, or troubleshooting an image generation prompt for any tool or any subject (not just photography, not just UI mockups). Triggers on: 'write an image prompt', 'Midjourney prompt', 'DALL-E prompt', 'Stable Diffusion prompt', 'image generation prompt', 'improve this prompt', 'negative prompt'."

Image Prompt Engineer — Prompt Methodology Skill

Teaches HOW to write a great image-generation prompt, for any generator and any subject. This is a methodology skill, not a lookup library — it gives the structure, vocabulary, and per-generator parameter syntax needed to construct a prompt from scratch or fix one that isn't producing the right result.

Not in scope here: a curated database of ready-made prompts (see gpt-image-prompts skill for 476+ examples you can browse/search/adapt), and not UI-screenshot-specific composition rules (see imagegen-frontend-web / imagegen-frontend-mobile for those). This skill is the general-purpose methodology underneath all three.

When to use this skill

• Asked to write an image prompt and no existing curated prompt fits the need
• Asked to improve, debug, or extend an existing prompt that isn't producing the right output
• Building a prompt for a generator not covered by another skill (Stable Diffusion, Flux,

Higgsfield, Leonardo, Ideogram, etc.)

• Any agent (Image Prompt Engineer, AI Video Producer, content writers, design agents)

needs to produce or QA an image-generation prompt

Core Methodology — The Five-Layer Prompt Structure

Every effective image prompt is built in layers, ordered most-important-first. Most generators weight earlier tokens more heavily, so put what matters most up front.

1. Subject — who/what is in frame, with concrete (not vague) descriptors

• Bad: "a woman" → Good: "a woman in her 30s with curly red hair, wearing a tailored navy blazer"

1. Action / Composition — pose, framing, what's happening, where elements sit

• Subject position (centered, rule-of-thirds, foreground/background), interaction with environment

1. Environment / Setting — location, time of day, weather, background treatment
2. Lighting — source, direction, quality, color temperature (this is the single highest-leverage

lever for photorealism and mood — never leave it implicit)

1. Style / Technical — art style or photography genre, camera/lens specs if photoreal,

color grade, quality boosters, generator-specific parameters (always last)

Template:

[Subject, concrete details] + [action/pose/composition] + [environment/setting] +
[lighting: source, direction, quality, color temp] + [style/medium] +
[technical: lens/camera OR art-style specifics] + [quality boosters] + [generator params]

Concrete beats vague every time. "Shallow depth of field, f/1.8 bokeh" beats "blurry background" — name the mechanism, not the visual effect, because generators were trained on photography/art captions that use mechanism-language.

Per-Generator Parameter Reference

Midjourney

Parameters go at the end of the prompt, each prefixed --.

DALL-E / GPT-Image-2

No bracket-parameter syntax — everything is natural language. Precision comes from explicit, plain-English description rather than tokens.

• Specify aspect/composition in words: "wide cinematic shot," "square product photo on white background"
• Specify lighting and perspective explicitly — the model follows literal descriptions well
• Style is steered by naming a medium/genre directly: "isometric vector illustration,"

"35mm analog film photo," "studio Ghibli-style animation still"

• No native negative-prompt field — exclusions must be phrased positively

("clean background" instead of "no clutter") or stated as a constraint in the sentence ("no text or logos anywhere in the image")

• See gpt-image-prompts skill for 476+ tested examples across 5 categories in this exact style

Stable Diffusion (and forks: SDXL, SD3, Automatic1111/ComfyUI workflows)

Token-weighted, supports a true negative-prompt field.

Flux (FLUX.1, Flux Pro/Dev)

Natural-language-first like DALL-E, but rewards longer, highly detailed descriptive paragraphs over keyword-stacking. Strong photorealism — lean on real photography vocabulary (lens, film stock, lighting setup) rather than generic quality tags.

Higgsfield

Built for motion/video-adjacent generation — prompts need a motion/camera-move layer in addition to the static-image layers above.

• Use models_explore(action:'recommend') to pick the right Higgsfield model for the goal before generating.

Leonardo AI

Preset-driven — prompt is plain language, but model/preset choice carries a lot of the style weight (PhotoReal, Illustration, Anime presets each interpret the same prompt differently). Supports negative prompt field and an "Elements" (LoRA-like) system for style consistency.

Ideogram

Strongest of the mainstream generators at rendering legible text-in-image — if the deliverable needs accurate on-image typography (posters, signage, logos with text), prefer Ideogram and state the exact text in quotes within the prompt.

Style Modifier Vocabulary

Lighting terms: golden hour, blue hour, overcast diffused light, hard direct sunlight, softbox key light, rim/edge light, Rembrandt lighting, butterfly lighting, split lighting, volumetric light/god rays, neon practical lighting, chiaroscuro, backlit silhouette, high-key (bright, low contrast), low-key (dark, high contrast)

Camera/lens terms: 35mm/50mm/85mm focal length, wide-angle distortion, telephoto compression, shallow depth of field (f/1.4–f/2.8), deep focus (f/8–f/16), bokeh, macro, low angle / high angle / bird's-eye / worm's-eye, Dutch tilt, tilt-shift, anamorphic lens flare, long exposure, motion blur

Art style terms: isometric, flat vector illustration, watercolor, oil painting, cel-shaded anime, photorealistic, hyperrealistic, claymation, low-poly 3D, brutalist, art nouveau, cyberpunk, vaporwave, minimalist, maximalist, editorial photography, documentary photography, fine art print

Quality boosters (use sparingly — generator-dependent, can be redundant on newer models): "highly detailed," "8k," "sharp focus," "professional photography," "award-winning" — on modern models (Midjourney v6+, GPT-Image-2, Flux) these add less than they used to; concrete descriptive language outperforms generic quality tags. Prefer specificity over boosters.

Negative Prompt Patterns

Only Midjourney (--no), Stable Diffusion (negative field), and Leonardo have a true negative-prompt mechanism. For DALL-E/GPT-Image-2/Flux, rephrase exclusions as positive constraints inside the main prompt.

Common negative-prompt targets:

• Anatomy failures: deformed hands, extra fingers, asymmetric eyes, malformed limbs
• Unwanted artifacts: watermark, text, logo, signature, blurry, low quality, jpeg artifacts
• Composition failures: cropped, out of frame, duplicate, cluttered background
• Style leakage: name the unwanted style explicitly if the model keeps drifting toward it

(e.g., --no anime if a photoreal prompt keeps returning illustrated results)

Multi-Subject / Scene Composition Rules

1. Establish a clear hierarchy — name the primary subject first, secondary subjects after,

with explicit spatial relationships ("in the foreground... behind her, slightly blurred...")

1. Cap subject count realistically — most generators degrade past 2-3 named subjects with

distinct described attributes; complex group scenes need either acceptance of some randomness or multiple generation + compositing passes

1. Use weighting/emphasis to keep the hierarchy from collapsing (Midjourney ::,

SD (token:1.3)) — without it, generators often give equal visual weight to all mentioned elements regardless of prompt order

1. Describe interactions, not just co-presence — "two people shaking hands" generates

more coherently than "two people, a handshake" as a bolted-on fragment

1. One scene, one lighting setup — conflicting lighting descriptions for different

subjects in the same frame is a common cause of incoherent results

Iterative Refinement Pattern

1. Generate a baseline with the core 5-layer prompt — don't over-specify on attempt 1
2. Diagnose the gap — is it composition, style, lighting, or a specific element wrong?

Change ONE layer at a time; changing everything at once makes it impossible to learn what worked

1. Reuse the seed (where supported — Midjourney --seed, SD seed field) when refining

composition/style without wanting the whole image to change

1. Escalate specificity, not length — replace vague terms with concrete ones rather

than padding the prompt with more adjectives

1. When stuck after 3 iterations, suspect a structural conflict (e.g., asking for both

"soft diffused light" and "dramatic hard shadows") rather than a wording problem — re-read the prompt for contradictions before adding more detail

1. Document what worked — once a prompt pattern produces a reliable result for a

recurring need, that's a candidate to add to the gpt-image-prompts library rather than rediscovering it each time

Workflow

1. Identify the target generator (if unspecified, ask or default to GPT-Image-2/natural language)
2. Identify the subject type — is this photography-style (use the Image Prompt Engineer

agent's genre patterns for portrait/product/landscape/fashion), a UI mockup (route to imagegen-frontend-web/imagegen-frontend-mobile instead), or general/illustrative (use this skill's five-layer structure directly)

1. Build the prompt layer by layer: subject → action/composition → environment → lighting → style/technical
2. Apply the generator's parameter syntax from the reference table above
3. Add negative-prompt exclusions if the generator supports them; otherwise fold exclusions

in as positive constraints

1. If multiple subjects, apply the composition rules above
2. Generate, diagnose gaps against the 5 layers, refine one layer at a time
3. Run critique-imageprompt on the final prompt before delivery — invoke the critique-imageprompt

agent or paste the prompt into the critique checklist below. Address any FAIL or NEEDS WORK findings before finalizing.

Cross-References

• `gpt-image-prompts` skill — 476+ curated, production-tested GPT-Image-2 prompt examples

across 5 categories. Use it to find a ready-made starting point; use this skill to adapt, extend, or troubleshoot it, or to write one from scratch for a different generator.

• `imagegen-frontend-web` / `imagegen-frontend-mobile` skills — narrower, UI-screenshot-specific

composition rules (one-image-per-section, hero composition bias, app-native UI patterns). Route there first for website/app design-reference image tasks; this skill is the general fallback for everything else.

• Image Prompt Engineer agent ({agency-root}/agents/design/design-image-prompt-engineer.md) —

an autonomous agent specializing in photography-genre prompts (portrait, product, landscape, fashion) with deep genre-specific templates. Spawn that agent for hands-on photography prompt production; read this skill directly when you (or any agent) need the general cross-generator methodology, non-photography styles, or a generator the agent's templates don't cover (Higgsfield, Leonardo, Ideogram, Stable Diffusion parameter tuning).

• `critique-imageprompt` agent ({agency-root}/agents/critiques/critique-imageprompt.md) —

scores any prompt produced with this methodology against character consistency, layer completeness, specificity, style coherence, generator fit, and negative-prompt coverage. Invoke before delivery (Workflow step 8) or use the Quick Critique Checklist below for an inline self-check without spawning an agent.

Quick Critique Checklist

Before finalizing any image prompt, verify:

☐ Character attributes are concrete and complete (not "a woman" — name every visible trait) ☐ If multi-image series: exact same attribute phrases reused verbatim across all prompts ☐ Per-generator consistency mechanism in place (--cref, LoRA, seed, exact-match phrasing) ☐ All 5 layers present: subject / composition / environment / lighting / style ☐ Lighting is EXPLICIT — source, direction, quality, color temp (never left implicit) ☐ No style contradictions (lighting, medium, art style all coherent) ☐ Generator parameter syntax correct for target tool ☐ Negative prompts present (if supported) covering anatomy + artifacts + composition