---
name: imagegen
description: "Use for visual deliverable routing and image generation/editing tasks, including new images, image edits, image upscaling/restoration/enhancement, UI mockups, diagrams, infographics, posters, icons, logos, charts, visual assets, and ambiguous visual requests. Helps choose between Mermaid, Python plotting, static layout/code screenshots, web/app development, and AI image generation/editing."
license: Complete terms in LICENSE.txt
---

# ImageGen Manus

Use this skill to decide the correct production route for visual deliverables. A correct visual is not merely attractive; it must fit the user's intended medium, purpose, constraints, hierarchy, and acceptance criteria.

## Core Principle

Identify the image's **job** before choosing a tool or style. Optimize for conversion, explanation, brand recognition, product accuracy, game usability, UI credibility, readable text, precise data mapping, or edit fidelity as appropriate.

Do not start by asking about model names, API parameters, or internal generation settings. Ask the user only for missing user-facing constraints that affect correctness, such as final medium, aspect ratio, exact text, language, brand colors, reference style, product constraints, transparency, or preservation requirements.

## Authoritative Routing

Use the simplest route that satisfies the user's likely deliverable. Explicit user requirements override defaults. When multiple explicit deliverables are requested, produce them in the user's stated order; if no order is stated, start with the most directly named deliverable.

| User intent | Required route |
| --- | --- |
| Explicit **Mermaid**, sequence diagram/时序图, flowchart/流程图, ER/class/state/Gantt diagram, graph syntax, dependency graph, hierarchy, formal architecture diagram, or any node-edge/process/system diagram where relationships must be accurate | Write Mermaid or another deterministic diagram source first. Render to PNG/SVG only if an image artifact is requested or useful for delivery. Do not replace a structured diagram request with AI-generated diagram art. |
| Numeric data, CSV/spreadsheet/table values, axes, scales, coordinates, measured comparisons, trend lines, statistical/scientific/financial charts, or “比例尺要准确/精细图表” | Use Python plotting or another deterministic visualization workflow. Preserve numeric mapping, units, axis ranges, tick labels, legends, and proportions. Use AI generation only for optional decorative assets, not for the chart itself. |
| Explicit code, 写代码, HTML/CSS/React, source files, demo/制作demo, working or functional prototype, interactivity, deployment, real website/app, or implementation | Build the requested software deliverable. Do not answer with image-only output. A bare “prototype/原型” request without code/demo/functionality wording is not automatically a development request. |
| UI image, app screen image, dashboard image, visual mockup, prototype image/原型图, 生成UI, poster, illustration, logo, icon, product visual, sticker/card/game asset, marketing visual, or other final visual asset | Use AI image generation or image editing unless the same request explicitly names Mermaid, precise charting, source code, a demo, or functional implementation. Deliver final image files. |
| Existing image plus requests to replace, remove, translate, localize, restyle, add objects, alter a region, preserve identity/product, change content, **or upscale/restore/enhance resolution** | Use image editing/generation. Preserve unchanged areas explicitly. Do not use deterministic image processing for creative or semantic edits. For upscale or restore requests, use the image variation tool with the exact prompt specified in the Upscale/Restore recipe in `references/prompt-recipes.md`; do not add extra instructions or rephrase it. |
| Resize, crop, rotate, flip, format conversion, simple stitching, or compression where no new visual content needs to be created | Use deterministic image processing. When a resize or crop changes the aspect ratio, check whether the user accepts losing edge content. Signals that the user accepts content loss include words like "crop to", "trim", "cut off the edges", or "just the center part". **When the user has not signaled acceptance of content loss, default to preserving all content**: use AI image editing to reconstruct or extend the image so that no existing content is sacrificed. |
| Ambiguous “画个图/做个图/做个示意/帮我可视化/design a page/做个界面/做个原型” | Match conditions rather than blindly following a fixed output sequence: Mermaid for structural relationships, Python for numeric precision, AI generation for visual or conceptual assets, static layout/code screenshot for deterministic static composition, and web/app development only for explicit functional or source-code deliverables. Deliver the first suitable version, then offer one focused next step. |

## Diagram Boundary

Separate **structured diagrams** from **conceptual explanatory visuals**.

| Type | Use when | Route |
| --- | --- | --- |
| Structured diagram | Node-edge relationships, sequence, architecture dependencies, formal process flow, hierarchy, labels, or machine-readable structure must be accurate | Mermaid or deterministic rendering first |
| Conceptual explanatory visual | The user wants a polished presentation visual, framework image, educational visual, decorative schematic, or infographic where visual communication matters more than exact graph syntax | AI image generation after organizing labels and hierarchy |
| Both accuracy and polish matter | The user needs a correct structure and a visually polished version | Create the structured diagram first, then optionally create a polished visual version after the structure is approved |

## Scenario Classification

Assign one primary scenario and any secondary scenario before prompting or editing.

| Scenario | Use when the visual is for | Success criterion |
| --- | --- | --- |
| Website or landing page | Hero images, section visuals, blog covers, page visuals | Layout fit, whitespace, brand tone, text-safe area |
| Product or commerce | Product renders, packaging shots, marketplace images | Product accuracy, material credibility, clean presentation |
| Marketing or social | Ads, posters, campaign visuals, thumbnails | Strong focal point, emotional clarity, readable hierarchy |
| UI or app mockup | App screens, dashboards, interface concepts | Plausible layout, consistent controls, legible structure |
| Logo or icon | Brand marks, app icons, favicons, symbolic assets | Simplicity, recognizability, scalability |
| Game or asset pack | Sprites, props, tiles, characters, backgrounds | Reusable silhouette, consistent perspective, clean edges |
| Character or portrait | People, mascots, avatars, recurring characters | Identity consistency, natural anatomy, intended mood |
| Diagram or infographic | Explainers, conceptual visuals, structured visuals | Correct structure, readable labels, low ambiguity |
| Transparent asset | Cutouts, stickers, overlays, compositing assets | Clean alpha, complete subject, no unwanted background |
| Precise image edit | Modification of an existing image | Change only requested regions; preserve everything else |
| Image upscale or restore | Upscaling, restoring, or enhancing resolution of an existing image | Higher resolution and clarity while strictly preserving original content, style, and identity |

Read `references/scene-decision-matrix.md` when scenario tradeoffs are unclear or when a request spans multiple media.

## Cross-Cutting Policies

### UI images

Default UI-related image requests to direct image generation as visual mockups. Do not create HTML/CSS/React, render a webpage, and screenshot it merely because the image resembles an interface. Use code, web/app development, or screenshot workflows only when the user explicitly asks for source code, editable front-end files, interactivity, deployment, a working prototype, or deterministic layout output.

When prompting UI images, specify screen type, navigation, information architecture, component hierarchy, realistic spacing, device or browser frame, light/dark mode, brand palette, and exact UI labels or sample data. Describe the output as a visual mockup; do not imply functional software.

### Text-bearing visuals

Default text-bearing visual image requests to direct generation of the complete final image, with the text rendered by the image generation model itself. Treat Chinese, English, Japanese, Korean, Arabic, and other writing systems as first-class generation targets; do not assume non-English or dense copy requires a separate text overlay workflow. Text presence, language, or amount of copy is never a reason to generate a blank/background-only image and then add text with Python, scripts, canvas, HTML, SVG, PIL, or manual compositing.

Strictly forbidden: do not create a poster, menu, infographic, UI mockup, card, flyer, banner, or other text-bearing image by first generating an empty background and then overlaying the required text programmatically. This prohibition applies especially to Chinese text and multilingual layouts. Use deterministic layout or post-processing only when the user explicitly asks for editable source/layout files, pixel-perfect corporate typography, print-production files, legally exact fine print, exact long tables, machine-readable diagrams, or source-controlled design assets; even then, do not present that route as a workaround for Chinese or non-English text generation.

Before prompting, organize meaningful copy into title, subtitle, sections, labels, callouts, CTA, footnotes, and UI labels as appropriate. Preserve exact proper nouns, prices, dates, numbers, Chinese characters, punctuation, and required terminology. Reduce redundant wording only when it harms visual readability and is not user-required.

Verify generated text against the user-stated requirements: required wording, language, hierarchy, placement, omissions, duplications, and critical numbers/dates/names. Do not run a separate generic garbled-text audit. If one or two targeted generations still fail critical text accuracy and exactness is required, regenerate with a narrower text block, reduce visual density, or ask the user whether exact editable layout/source files are required; do not fall back to Python/scripted text overlay as the default correction path.

### Static layout/code screenshot

Use a static layout or code screenshot workflow only when the user explicitly needs deterministic static visual layout, exact typography or placement, reproducible image composition, editable layout/source files, or source-controlled design assets, but does not need functional software, deployment, data interactivity, or editable application source. Do not use this route merely because the visual resembles a UI, contains Chinese/non-English text, or contains a lot of text.

### Multi-item visual sets

For card decks, icon packs, sticker packs, poster sets, product image sets, game asset packs, or similar requests, deliver **one standalone final image per requested item** by default. Do not deliver process images, raw subject-only illustrations, contact sheets, stitched previews, A4 sheets, PDFs, or combined boards unless explicitly requested. Archives may supplement many files, but must not replace direct access to final individual images when the user asked for images.

### Image edits and references

For image edits, change only the target region/object/text and preserve identity, pose, product shape, background, lighting, perspective, color palette, and non-target objects unless the user requests otherwise. When the user says “use this as reference,” match only the relevant dimension: identity, style, composition, color, or product form.

### Scenario-specific guardrails

| Scenario | Guardrail |
| --- | --- |
| Logo/icon | Start with a simple symbol or mark. Do not rely on generated typography for precise brand wordmarks unless the user accepts concept-only output. |
| Product image | Preserve product shape, labels, proportions, materials, and legally sensitive marks. Do not invent branding unless requested. |
| Transparent asset | Require a true transparent background, complete subject, clean alpha, and no colored fringe or unwanted shadow. |
| Character | Define stable identity anchors: age, face shape, hair, outfit, silhouette, palette, and style. |
| Precise chart | Never use AI image generation for the quantitative plot itself. |

## Production Brief Workflow

Build a short internal brief before generating or editing.

| Brief field | Determine |
| --- | --- |
| Purpose | What the visual must accomplish |
| Medium | Website, app, ad, marketplace, slide, game, print, social, or asset library |
| Subject | Main object, person, environment, interface, product, or concept |
| Composition | Orientation, framing, whitespace, safe area, hierarchy, camera angle, screen/device context |
| Style | Photographic, vector, 3D, editorial, flat, pixel art, brand style, or reference-matched |
| Text/content | Exact wording, language, hierarchy, labels, UI copy, callouts, or “no text” |
| Constraints | Aspect ratio, dimensions, transparency, brand colors, preservation requirements, excluded objects |
| Acceptance risks | Errors that would make the result unusable |

Ask only for missing essential information. If nonessential details are unspecified, make a reasonable assumption and proceed.

Use this prompt structure for new visuals:

```text
Create [image type] for [use case and audience].
Subject: [specific subject, interface, product, scene, or concept].
Composition: [framing, orientation, focal point, safe area, background depth, screen/device layout if relevant].
Style: [visual style, lighting, material, color palette, brand tone].
Text/content to render: [organized exact content block, UI labels, section labels, CTA, or “no text”].
Constraints: [aspect ratio, transparency, text handling, elements to include/exclude].
Avoid: [scenario-specific failure modes].
```

Use this prompt structure for edits:

```text
Edit the provided image. Change only [target region/object/text].
Preserve [identity, pose, product shape, background, lighting, perspective, color palette, other objects].
New result should [desired outcome].
Text/content to render if relevant: [exact replacement text and location].
Avoid: [unwanted changes, artifacts, text errors, style drift].
```

Read `references/prompt-recipes.md` for scenario-specific prompt structures.

## Lightweight Validation and Delivery

Do a **lightweight pass/fail check** before delivery, not an open-ended audit. Confirm only that the result satisfies the user's explicit request, the selected route is correct, and there is no obvious fatal defect that would make the artifact unusable. Do not perform forensic inspection, crop local regions for preview, run scripts to locate labels, search fonts, create masks, compare tiny details, or repeatedly re-open intermediate files unless the user requested that workflow or a visible critical failure blocks delivery.

If a critical requirement fails, correct only the single most important failure with one focused edit/regeneration or an appropriate route change. Do not keep refining for subjective polish, minor crop preferences, tiny spacing issues, uncertain text micro-errors, or speculative improvements. For text-bearing visuals, a quick visual check of requested headline/labels/numbers is enough; do not run a generic garbled-text audit or build a scripted correction pipeline.

Use deterministic image processing for operations where no new visual content is needed: downscaling, trimming, format conversion, or compression. When a resize or crop implies a new aspect ratio, default to preserving all content unless the user explicitly accepts edge loss; use AI editing to reconstruct or adapt the image when content must not be lost.

Deliver the artifact that matches the selected route: Mermaid source for Mermaid requests, chart image and source data/code as appropriate for Python plots, implementation files for code/demo/web/app requests, static layout images/source when requested, and final image files for AI generation or editing. Once the requested artifact has been delivered, **stop**. Do not continue generating extra variants, backup versions, additional fixes, or follow-up improvements after final delivery unless the user asks for them. Briefly state only essential scenario assumptions or known limitations that affect use.