Crun Character Reference

Crun Character Reference Generator

Use this skill to produce character reference artwork through Crun image models. It handles two entry paths — with a source image and without — and always finishes by returning the result file, selected model, and credits spent.

Use ../../../runtime/crun_cli.py, ../../../catalog/models.json, and the shared child skills:

• ../../crun-model-router/SKILL.md — model routing & live schema inspection
• ../../crun-account-credits/SKILL.md — balance & affordability estimation
• ../../crun-task-runner/SKILL.md — task creation, monitoring, recovery, result delivery (the authority)

Trigger

Activate for any of: character reference, character sheet, turnaround, multi-view, pose grid, nine-grid / 9-grid, expression sheet, emotion sheet, character design sheet, OC reference, 设定图, 角色参考, 三视图, 九宫格, 表情集设定, 立绘 — even if the user never says "Crun" or a model name.

Entry path decision

1. Inspect the current conversation for an attached or linked source image.
   • Image present → follow Path A (image-driven).
   • No image → follow Path B (text-driven).
2. Treat only resources visibly attached or explicitly linked in the current conversation as source material. A new session starts with no source material, even if prior sessions, chat history, tool traces, or generated JSON mention a media URL or local path. Never reconstruct or infer a task input from historical JSON.

Path A — source image provided

A1. Upload the source

python <root>/runtime/crun_cli.py upload <local-file>

Reuse an existing Crun resource URL directly. Never send Base64, data URIs, or local paths in a task input.

A2. Resolve the format

• If the user already named a format in the original request (e.g. "九宫格", "三视图", "expression sheet"), use it directly and skip the search.

• Otherwise, search the web for trending character-reference formats and distill three concrete options. Use queries such as:

  • 热门角色参考图格式 九宫格 三视图 表情集
  • trending character sheet formats 2025 pose grid turnaround expression sheet

  Build three option cards, each with: format name, a one-line description, an example use, and the model it will route to.

Present the three formats via interactive option buttons (use the AskUserQuestion tool) and let the user pick exactly one. Mirror the user's language for all labels and descriptions.

A3. Pick model & build payload (image-driven → image-edit)

The operation is image-edit — the goal is to keep character identity from the source while producing the chosen layout. Select the model by format from the table in "Format → model & prompt" below. Every image-edit candidate must have supports_reference: true in the catalog.

After choosing, inspect the live schema:

python <root>/runtime/crun_cli.py models describe --model <model>

Construct only fields the schema permits. Put the uploaded file_url into the reference-image field the schema defines (commonly image, img_urls, or reference_image — confirm from the live schema, never guess).

Path B — no source image (text-driven)

B1. Resolve style / gender / format online

Search the web for currently trending character styles, common gender options, and reference formats. Use queries such as:

• 热门角色风格 2025 赛博朋克 国风 二次元
• trending character art styles 2025
• character reference sheet popular layouts

Distill the results into choice groups and present them via interactive option buttons (AskUserQuestion):

1. Style — three trending styles (for example 赛博朋克 / 国风仙侠 / 日系二次元).
2. Gender — at least male / female; add neutral or other culturally relevant options when appropriate.
3. Format — three formats (for example 九宫格 / 三视图 / 表情集).

If the user already stated a style, gender, or format in the original request, preselect it and only ask for the groups that remain undecided. Mirror the user's language.

B2. Build the character prompt & pick model (text-driven → text-to-image)

Compose a clear English visual description from the chosen style + gender + any details the user added, then apply the format prompt template (see below). The operation is text-to-image. Select the model by format from the table.

Format → model & prompt

Pick the model from the table by format. The operation column assumes the entry path; if a chosen model does not support the needed operation, fall back to ../../crun-model-router/SKILL.md.

Prompt templates — fill [CHAR] with the English character description; keep the structural English verbatim, because image models render multi-panel layouts most reliably from English:

• Nine-grid: A 3x3 grid character reference sheet of [CHAR]. Nine equal panels in a 3 by 3 layout with thin dividing lines, each panel showing the same character in a different pose and expression. Clean white background. Consistent character design across all panels.
• Turnaround: Character turnaround reference sheet of [CHAR]. Three full-body views side by side: front view, side view, back view. Even spacing, consistent proportions, plain background, professional character design reference.
• Expression sheet: Expression sheet of [CHAR]. Multiple head-and-shoulder shots arranged in a grid, each showing a different emotion: happy, angry, sad, surprised, neutral, laughing, serious, shy, confused. Consistent character design, clean background.
• Design sheet: Character design sheet of [CHAR]. Multiple views plus detail callouts, a color palette with swatches, and short text labels. Professional concept-art layout, clean background.
• Hero portrait: Full-body character key art of [CHAR]. Dynamic pose, dramatic lighting, detailed costume, clean background, high-quality illustration.

Do not ask the image model to render long CJK text inside the artwork. For the design sheet, prefer models that handle short labels (openai/gpt-image-2) and keep labels minimal. If the user wants a caption or watermark on the final image, overlay it in post-processing rather than relying on the model.

Estimate & confirm

Read ../../crun-account-credits/SKILL.md. Estimate the final model and exact input before creating any task:

python <root>/runtime/crun_cli.py task estimate --model <model> --input-file <input.json>

Require affordable: true. Because the model was chosen by this skill (not explicitly named by the user), present a confirmation summary with model, format, style/gender (when relevant), aspect/resolution, and estimated credits, then wait for explicit OK via interactive buttons before spending. Do not treat the earlier format/style selection as the spend confirmation.

Create & deliver

Read ../../crun-task-runner/SKILL.md for the authoritative execution rules. Create once, capture task_id, and poll in short resumable rounds:

python <root>/runtime/crun_cli.py task create --model <model> --input-file <input.json>
python <root>/runtime/crun_cli.py task wait --task-id <task_id> --timeout-seconds 120

Follow crun-task-runner for all timeout, failure, and recovery rules. CreateTask charges credits and is never retried automatically.

Delivery format

Return a localized summary plus the normalized fields. The result, model, and credits spent are mandatory in every delivery:

✅ Character reference generated!
- Task ID: <task_id>
- Format: <format>
- Model: <model>
- Credits Used: <credits>
- File Path: <local_image_path>

![Character Reference](/absolute/path/to/file.png)

Prefer the local media path. Where the client renders inline media, preview the image with an absolute forward-slash path. Use the remote URL only as a fallback and identify any download error instead of claiming a failed download is local.

Localization

Write every user-facing string — search-result option cards, interactive buttons, confirmation summary, progress updates, errors, and the final delivery — in the user's language. Keep API field names, model IDs, task IDs, numeric values, file paths, and the structural English of the prompt templates unchanged.