# Image Gen Prompts > Image Generation Prompt Engineering — Universal - Skill: `fralapo/image-gen-prompts` (Agent Skill, multi-file: 21 files) - Install (CLI): `npx skillmds@latest add fralapo/image-gen-prompts` - Raw SKILL.md: https://api.skillmd.com/api/skills/fralapo/image-gen-prompts/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: fralapo (https://skillmd.com/u/fralapo) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/fralapo/image-gen-prompts --- # Image Generation Prompt Engineering — Universal Generic prompt-engineering skill for **any** modern image generation / editing model. Works model-agnostic by default; switches to model-specific guidance when the user names a model. Strong prompts share the same anatomy across models — subject, environment, lighting, camera/lens, style, format, and (for edits) what to preserve. What differs is **syntax surface**: Midjourney uses `--flags`, GPT Image 2 likes structured JSON, Nano Banana parses natural-language addressing of reference images, SDXL leans on weighted token blocks plus negative prompts, FLUX rewards prose density, Ideogram is the strongest at typography. ## When to Use This Skill - User asks to write or improve an image prompt for any model - User wants to edit an uploaded image (outfit swap, background change, face-preserving restyle, object addition/removal, inpainting) - User requests identity-preserved portraits, celebrity integration, aging sequences, multi-character group shots - User wants a bento/infographic layout, isometric diorama, exploded-view product poster, magazine cover, comic panel, storyboard grid, e-commerce main image, YouTube thumbnail - User needs style-specific prompts: cinematic, Kodak Portra, anime, 3D chibi, pixel art, Baroque frame, ukiyo-e, Swiss design, cyberpunk, RAW iPhone, 90s point-and-shoot, 35mm direct flash - User mentions "preserve face", "same face 100%", "change only the background", "keep pose", "multi-view", "action figure box", "exploded view" - User wants Raycast-friendly prompts with `{argument name="..." default="..."}` placeholders - User explicitly names a model (Midjourney, MJ, SDXL, FLUX, GPT Image 2, Nano Banana, etc.) ## Model Routing Whenever the user names a model in their query, **read the matching file** under `references/models/` for syntax surface and quirks, then layer it on top of the universal anatomy below. | Trigger keywords (any) | Read | |---|---| | `nano banana`, `nano-banana`, `gemini 2.5 flash image`, `gemini image`, `nano banana pro`, `纳米香蕉` | `references/models/nano-banana.md` | | `gpt image 2`, `gpt-image-2`, `duct-tape`, `gpt image 2 prompts` | `references/models/gpt-image-2.md` | | `gpt image 1`, `gpt-image-1`, `dall-e 3`, `dall·e 3`, `dalle 3` | `references/models/gpt-image-1.md` | | `midjourney`, `mj`, `--ar`, `--stylize`, `--sref`, `--cref`, `niji`, `--niji` | `references/models/midjourney.md` | | `stable diffusion`, `sd 1.5`, `sdxl`, `sd 3`, `sd3`, `sd3.5`, `comfyui`, `automatic1111`, `forge` | `references/models/stable-diffusion.md` | | `flux`, `flux.1`, `flux dev`, `flux schnell`, `flux pro`, `black forest labs` | `references/models/flux.md` | | `imagen`, `imagen 3`, `imagen 4` | `references/models/imagen.md` | | `ideogram`, `ideogram 2`, `ideogram 3` | `references/models/ideogram.md` | | `recraft`, `recraft v3` | `references/models/recraft.md` | | `seedream`, `seedream 4`, `seedream 5`, `doubao image`, `jimmeng`, `byteimage` | `references/models/seedream.md` | | no model named | default to model-agnostic universal pattern; ask user which model | Always start with `references/models/_index.md` if you need to scan all routes at once. ## Two Capability Tiers (general) | Capability | "Standard" tier | "Pro" tier | |---|---|---| | Photorealistic portraits | ✅ | ✅ (sharper skin/pores) | | Multi-image fusion | ⚠️ 2–3 refs | ✅ 5+ refs | | Text rendering in image | Short labels only | Long quotes, multi-line, CJK | | Bento/infographic layouts | Simple grids | 8+ modules with data fields | | Dense crowd scenes | Drifts past 4–5 faces | Holds identity across many | | Structured JSON/YAML prompts | Partial parsing | Reliable parsing | | Complex conditional rules | Weak | Strong | Per-model mapping of "Standard" vs "Pro" is documented in each `references/models/.md`. Examples: - Nano Banana = Standard; Nano Banana Pro / Nano Banana 2 = Pro - GPT Image 1 / DALL·E 3 = Standard; GPT Image 1.5 = Standard+; GPT Image 2 = Pro - Midjourney v6 = Standard; v7 = Pro - SD 1.5 = Standard; SDXL / SD3 / SD3.5 = Pro - FLUX.1 [schnell] = Standard; FLUX.1 [dev] / [pro] = Pro - Seedream 4.0 / 4.5 = Standard; Seedream 5.0 = Pro **Rule of thumb:** default to Pro tier for infographics, posters with headlines, multi-character scenes, structured JSON/YAML prompts, and CJK text. Standard tier suffices for single-subject edits and short prompts. ## Universal Prompt Anatomy Every strong prompt stacks these blocks. Order is flexible; labels are optional when context is clear. ``` [Subject] who/what — reference handling ("keep face 100%") [Action/Pose] what they're doing, body orientation [Wardrobe] clothes, accessories, materials, color [Environment] location, props, ground, background wall [Lighting] key/fill/rim, time-of-day, quality (hard/soft), color temp [Camera/Lens] 85mm f/1.4, drone top-down, orthographic, eye-level [Style ref] film stock (Kodak Portra 400), director, era (2000s), render engine [Format] aspect ratio (1:1, 16:9, 9:16), resolution (1080x1080, 8K) [Preserve] (edit mode) what must NOT change [Negative] anti-artifacts: "no text", "no second object", "no warping" ``` ### Minimum viable template — generation ``` A {style/medium} of {subject} {action} in {environment}. {Wardrobe + props}. Lighting: {key + rim + mood}. Camera: {lens, angle}. {Aspect ratio}, {resolution}. {Style ref}. ``` ### Minimum viable template — image edit ``` {Edit verb} the {target region} of the attached image: {specific change}. Preserve: face/identity, pose, outfit, background lighting — zero alteration. Only modify: {explicit region}. Match the original {lighting logic / color grade / perspective}. ``` ## Reference Inputs (per model) How to address an uploaded reference image varies. Universal pattern works across most natural-language models; flag-based models need explicit syntax. | Model family | Phrasing | |---|---| | Nano Banana / Gemini | "the person in the uploaded image", "same face 100% from reference" — natural language | | GPT Image 2 | "Based on this character", "Using this portrait", "Upload a source image" — natural language | | Midjourney | `--cref ` for character ref, `--sref ` for style ref, `--cw 100` weight | | Stable Diffusion / FLUX | IP-Adapter / ControlNet at the workflow level, not in the prompt text | | Imagen / Ideogram / Recraft | API-level `referenceImages` payload; prompt text references via "the provided reference" | For exact syntax see the matching file under `references/models/`. ## Category Catalog Detailed templates and examples live in `references/`. - `references/patterns.md` — universal vocabulary: lighting, camera, ratios, style tags, composition idioms, single-object rules, multi-reference fusion, negative-prompt vocabulary - `references/templates.md` — fill-in templates for 18+ common categories (portrait, product, poster, diorama, infographic, comic grid, exploded-view, e-commerce hero, YouTube thumbnail, character sheet, storyboard, etc.) - `references/identity-preservation.md` — face-lock phrases, failure modes, fixes, multi-person handling, mixed-style face preservation, aging sequences - `references/structured-prompts.md` — JSON / YAML / XML formats and which models parse them reliably - `references/text-rendering.md` — putting readable headlines, quotes, CJK in the image, per-model tier limits - `references/editing-workflow.md` — non-destructive editing, inpainting, multi-image fusion, before/after, character consistency across panels - `references/examples.md` — curated working prompts per category, attributed, with the model that produced each - `references/workflows.md` — Direct Generation, Content Illustration (article/video → image), and Remix/Personalization conversation patterns - `references/external-corpora.md` — pointers to public prompt libraries (YouMind 12k+, EvoLink ~3k, Awesome lists) when the user wants proven prompts rather than fresh composition - `references/models/` — per-model quirks, syntax surface, parameters, anti-patterns ## Quick Rules (model-agnostic) 1. **Paragraph + constraint lines beats tag soup** for natural-language models (Nano Banana, GPT Image 2, FLUX, Imagen). For Midjourney and SDXL, dense comma-separated tag blocks plus flags still work. 2. **Specify aspect ratio explicitly** — `1080x1080`, `16:9`, `9:16`, or `--ar 9:16` for Midjourney. Don't rely on defaults. 3. **Identity anchor every edit.** "Same face 100% from reference", "keep facial features exactly consistent", "do not change the face". 4. **Non-destructive edits need an allow-list.** Name what changes AND what is preserved — most models otherwise drift on wardrobe/pose. 5. **Lens + lighting = photorealism.** `85mm f/1.4`, `Kodak Portra 400`, `three-point lighting`, `golden hour rim light`, `harsh on-camera flash` all carry real weight on every model. 6. **Single-object constraints must be repeated.** If you want exactly one object, write an explicit strict-single-object block (see `references/patterns.md#single-object-rule`). 7. **Text in image = quote it exactly.** Wrap exact strings in double quotes: `serif font reading "Stay Hungry, Stay Foolish"`. For CJK, prefer Pro-tier models. 8. **Use `[PLACEHOLDERS]`** (square brackets, uppercase) for reusable templates. Use `{argument name="x" default="y"}` for Raycast snippets — supported as a literal placeholder by Nano Banana, GPT Image 2, and any natural-language model. 9. **For bento/infographic layouts**, enumerate modules (M1…M8) with content type per cell — strongly favors Pro-tier models. 10. **Negatives go at the end** as a `NEGATIVE:` list or `Do NOT:` bullet — except SDXL/Midjourney where negatives have dedicated syntax (`--no` for MJ, dedicated negative prompt input for SD). 11. **For natural / un-AI looks**: explicitly request "natural skin texture, flyaway hairs, slight asymmetry, no glamour retouching, no beauty filter, no overly polished AI aesthetic". GPT Image 2 and Nano Banana Pro both respect these. 12. **For cross-image consistency** (same character across N panels): describe the character once in detail, then reference back as "the same character from panel 1" — works on GPT Image 2 and Nano Banana Pro; weaker on others. ## Anti-Patterns - Tag-salad prompts on natural-language models (`portrait, 8k, masterpiece, beautiful, highly detailed`) — under-perform vs structured paragraph - Asking for "photorealistic" without lens/lighting — produces generic render - Editing without a preservation clause — model mutates face/pose - Multiple conflicting aspect ratios in one prompt — one wins, unpredictably - Face-swap with only facial description (no ref image) — low fidelity - Packing > 10 named subjects in one prompt — identity drift past ~5 on Standard tier, past ~8 on Pro - Embedding exact text > 15 words on Standard-tier models — spelling breaks - Mixing Midjourney `--flags` into a Nano Banana / GPT Image / FLUX prompt — flags get parsed as literal text and pollute output - "Polished AI" defaults when shooting for candid lifestyle — explicitly veto glamour retouching - Forcing JSON on a tiny prompt — `{"subject":"red apple"}` is worse than `A red apple on a white table, studio lighting, 1:1.` ## Conversation Modes Three distinct entry points handled in `references/workflows.md`: - **Direct Generation** — user describes an image; you compose a prompt from anatomy + template + per-model file. - **Content Illustration** — user pastes article / video script / podcast notes; you analyze theme + tone, match to a template, and move into Remix. - **Remix / Personalization** — after a template is chosen, ask only the relevant personalization questions (gender, age, setting, mood, profession) and remix the template with the user's content. If the user just wants **proven, image-attested prompts** rather than a freshly-composed one, point them at the searchable corpora in `references/external-corpora.md` (12,000+ prompts at YouMind, ~3,000 GPT Image 2 cases at EvoLink, plus Awesome-list browsable indexes). ## Workflow 1. Ask user — generate from scratch, edit an existing image, illustrate content, or browse proven prompts? Which model? Which tier? Aspect ratio? 2. If a model is named, read `references/models/.md` first. 3. Identify category → pick from `references/templates.md`. 4. If editing with a reference image: lock identity + add preservation clause first (see `references/identity-preservation.md`). 5. Fill subject → environment → lighting → camera → format → style. 6. Add negatives only for known failure modes (not prophylactically). Use the model's native negative-prompt mechanism if it has one. 7. For text in image, follow `references/text-rendering.md` and downgrade to a Pro-tier model if the string is long or non-Latin. 8. Output the prompt in a single fenced block; user pastes into the target model UI/API. For Midjourney, end with the flag block; for SD/FLUX, output positive + negative prompt separately.