Purpose

Provides a systematic methodology for crafting effective prompts for visual AI image generators. Covers prompt architecture, style vocabulary, composition controls, lighting direction, negative prompting, model-specific parameter tuning, and consistency techniques so that generated images reliably match the creator's intent.

When to use this skill

• User asks to write, improve, or debug a prompt for Midjourney, DALL-E, Stable Diffusion, or similar
• Task requires art direction: defining visual style, mood, composition, or lighting for AI generation
• User needs negative prompts to eliminate artifacts or unwanted elements
• Consistency work: maintaining a character, style, or scene across multiple generations
• User wants to understand model-specific parameters (--ar, --v, --s, CFG scale, sampling steps)
• Batch prompt creation for a series of related images (e.g., product shots, character sheets, storyboards)
• User says "this image isn't coming out right" and needs prompt diagnosis

Do not use this skill when

• The task is text-based prompt engineering for LLMs — use

  prompt-crafting

• The user wants to edit or manipulate an existing image — use

  image-editor

• The user wants to directly generate an image (not craft the prompt) — use

  imagegen

• The task is brand identity or logo design process — use

  brand-guidelines

  for strategy, then this skill for generation prompts
• The user needs to build a UI that displays images — use

  frontend-design

  or

  react-typescript

Operating procedure

Step 1 — Creative brief intake

Gather these inputs before writing any prompt:

1. Subject — what is the image of? (person, object, scene, concept)
2. Purpose — where will this image be used? (social media, website hero, print, concept art, personal)
3. Target model — which generator? (Midjourney v6, DALL-E 3, SDXL, Flux, other)
4. Aspect ratio — landscape (16:9), portrait (9:16), square (1:1), or custom
5. Style direction — any reference images, artists, art movements, or aesthetic keywords
6. Constraints — brand guidelines, content policies, elements that must NOT appear
7. Quantity — single image or series; if series, what varies between images

Step 2 — Prompt architecture

Build the prompt using this layered structure, from most to least important:

[Subject & Action] + [Environment/Setting] + [Style & Medium] + [Composition & Framing] + [Lighting] + [Mood & Atmosphere] + [Color Palette] + [Technical Parameters]

Subject & action layer

• Lead with the primary subject: "a weathered lighthouse keeper", not "an image of a man"
• Include action or pose if relevant: "standing at the railing, looking out to sea"
• Specify key details: age, expression, clothing, distinguishing features
• Use concrete nouns over abstract descriptions

Environment / setting layer

• Ground the subject in a specific place: "on a rocky coastal cliff during a storm"
• Include time of day and season if relevant to lighting/mood
• Add depth cues: foreground, midground, background elements

Style & medium vocabulary

Composition & framing controls

Lighting vocabulary

Mood & atmosphere

• Use emotional adjectives: "melancholic", "triumphant", "eerie", "serene", "chaotic"
• Pair mood with environmental cues: "desolate wasteland" vs. "lush paradise"
• Color temperature reinforces mood: warm = comfort/energy, cool = calm/isolation

Step 3 — Negative prompting

Define exclusions to steer the model away from common failure modes:

Universal negative prompt baseline:

blurry, low quality, low resolution, watermark, text, signature, cropped, out of frame, worst quality, jpeg artifacts

Anatomy-specific (for human subjects):

deformed hands, extra fingers, extra limbs, fused fingers, mutated hands, bad anatomy, malformed limbs, extra arms, extra legs, cloned face

Style-specific negatives:

• Photorealistic:

  illustration, cartoon, anime, painting, drawing, CGI

• Illustration:

  photo, photograph, realistic, photographic

• Clean design:

  cluttered, busy background, noisy, grain

Guidance: Only include negatives relevant to your target style. Over-stuffing the negative prompt can constrain the model too aggressively and produce flat results.

Step 4 — Model-specific parameters

Midjourney (v5/v6)

--ar

1:1

16:9

9:16

3:2

2:3

21:9

--v

5.2

6

6.1

--s

0

1000

--c

0

100

--q

0.25

0.5

1

2

--no

--no text, watermark, blurry

--seed

--sref

--sref <url>

--cref

--cref <url>

DALL-E 3 (via ChatGPT / API)

• No direct parameter flags; control via detailed natural language in the system prompt
• Specify aspect ratio in the request: "wide landscape format", "tall portrait format", "square"
• Use "I NEED the image to exactly match this description" to reduce DALL-E's prompt rewriting
• Negative concepts go in the prompt body: "without any text or watermarks"
• Seed control: not user-facing; use very specific descriptions for consistency

Stable Diffusion (SDXL / SD 1.5)

5

12

7

20

50

30

1024×1024

1216×832

832×1216

512×512

768×512

<lora:stylename:0.7>

Step 5 — Consistency techniques

For maintaining visual coherence across a series:

1. Seed pinning — use the same seed number across variations; change only the element you want to vary.
2. Reference images — provide reference URLs (Midjourney

   --sref

   ,

   --cref

   ) or img2img in SD.
3. Style transfer — lock the style by referencing a specific artist, movement, or prior generation.
4. Character sheets — generate a multi-angle character reference sheet first, then use it as reference for scene images.
5. Prompt templating — create a base prompt template with

   {variable}

   slots; only swap the variable per image.
6. Consistent terminology — use the exact same descriptive phrases for recurring elements across all prompts in a series.

Step 6 — Iteration and refinement

1. Generate initial results with the crafted prompt.
2. Diagnose issues using this checklist:
   • Wrong style → adjust style/medium keywords; add style-negatives
   • Wrong composition → make framing/angle instructions more explicit
   • Wrong lighting → specify lighting type by name; add "no flash photography" if too flat
   • Artifacts → strengthen negative prompt; adjust CFG or steps
   • Too literal / too abstract → tune stylize (MJ) or CFG (SD)
3. Document what changed between iterations and why (Iteration Notes).
4. After 3+ failed iterations, reconsider the fundamental approach rather than tweaking parameters.

Decision rules

• Prompt length > 75 tokens (SD) or overly complex → simplify; focus on the most important 5–7 descriptors. Models lose coherence with prompt-stuffing.
• Conflicting styles in one prompt (e.g., "photorealistic watercolor") → pick one style; use blending only with explicit intent and model support.
• No aspect ratio specified → always specify; default square rarely matches the use case.
• No negative prompt → always include at minimum the universal baseline negatives.
• Anatomy in the image → always include anatomy-specific negatives for human/animal subjects.
• Requesting text in images → warn that most models render text poorly; suggest adding text in post-production. DALL-E 3 handles short text better than others.
• Brand or commercial use → flag content policy and licensing considerations for the target model.
• Reproducibility required → mandate seed pinning and document all parameters.
• Vague style words ("cool", "nice", "beautiful") → replace with specific aesthetic vocabulary from the style table.

Output structure

Deliver these sections for each prompt request:

1. Prompt Brief

Summary of creative intent: subject, purpose, target model, aspect ratio, style direction.

2. Full Prompt

The complete, ready-to-paste prompt text with all layers applied.

[Full prompt text here]

3. Negative Prompt

Separate negative prompt text (for SD; for Midjourney, integrated via

--no

[Negative prompt text here]

4. Parameter Settings

Model-specific parameters in a copy-pasteable format:

--ar 16:9 --v 6 --s 250 --seed 42

or

CFG: 7 | Steps: 30 | Sampler: DPM++ 2M Karras | Seed: 42 | Resolution: 1024x1024

5. Iteration Notes

Table tracking changes across prompt versions:

6. Consistency Guide (for series)

Template, fixed elements, variable elements, seed, and reference image strategy.

Anti-patterns

• Prompt-dumping — cramming 20+ disconnected style keywords into one prompt. Models lose coherence; pick 3–5 complementary descriptors maximum.
• Ignoring aspect ratio — generating square images for a 16:9 banner creates cropping headaches or distortion. Always match output to intended use.
• No negative prompts — omitting negatives in Stable Diffusion especially leads to watermarks, text artifacts, and anatomical errors that are trivially preventable.
• Vague style words — "beautiful", "stunning", "epic" carry almost zero signal for the model. Replace with concrete aesthetics: "golden hour rim lighting" beats "beautiful lighting".
• Conflicting style instructions — "photorealistic anime oil painting" fights itself. Be intentional about style blending or pick one.
• Ignoring model strengths — using Stable Diffusion for text-heavy graphics (DALL-E 3 is better) or DALL-E for precise parameter control (SD is better). Match task to model.
• No seed documentation — generating a great image and not recording the seed or parameters means it cannot be reproduced or iterated upon.
• Over-relying on artist names — ethical and legal grey area; also, model knowledge of specific artists varies. Describe the visual qualities you want instead.
• Skipping iteration notes — repeating the same failed adjustments because previous attempts weren't documented.

Related skills

• imagegen

  — directly generating images (this skill crafts the prompt; imagegen executes it)

• prompt-crafting

  — text-based prompt engineering for LLMs (sister skill for non-visual prompting)

• brand-guidelines

  — ensuring generated images align with brand identity standards

• image-editor

  — post-production editing of generated images

• worldbuilding-research

  — generating consistent visual world-building assets

• canvas-design

  — composing generated images into layouts and designs

Failure handling

• If the user provides no subject or purpose, ask: "What is the image of, and where will it be used?"
• If the target model is not specified, default to Midjourney v6 parameters but note the assumption and offer alternatives.
• If the user's description contains conflicting style directions, surface the conflict and ask them to prioritize.
• If the prompt exceeds the model's effective token window, summarize and offer both a full and condensed version.
• If the user expects photorealistic text rendering, warn about model limitations and suggest compositing text in post-production.
• If consistency across a series is failing, recommend switching to a seed-pinning + reference-image strategy before further iteration.