Cover Cream Orange Knowledge Poster

Cream Orange Knowledge Poster Cover

Use this skill to create a cover/poster prompt, or generate the final image when the user explicitly asks for direct image generation. The result should feel like a premium editorial technical infographic: warm paper, strong headline, clear system logic, hand-drawn diagram energy, and enough detail to reward close reading.

This is the visual style member of the cream-orange-knowledge family. Use it directly for covers/posters. When the user wants a coordinated article package, route planning through article-visual-planner and pass this skill as the selected style, for example /article-visual-planner:cover-cream-orange-knowledge-poster.

Output Type

Use the explicit --out-type parameter to decide what to output.

• --out-type template: output the invocation template only.
• --out-type prompt: output the final image prompt only.
• --out-type all: output the template first, then the final image prompt.
• Omitted --out-type: default to template.

Treat 直接生成, 生成海报, 生成封面, 出图, and 生成图片 as direct image generation when an image tool is available.

Template Output

For template-only mode, output exactly this structure and fill every field:

使用 $cover-cream-orange-knowledge-poster 生成一张封面
主题词：{topic}
副标题：{subtitle}
画幅比例：{ratio}
语言：{language}
用途：{use_case}
信息结构：{information_structure}
核心视觉隐喻：{visual_metaphor}
补充背景：{context}
禁用元素：{forbidden_elements}

Required Inputs

Extract or infer:

• Topic / main title: required.
• Subtitle: optional.
• Aspect ratio: optional; infer from use case.
• Language: Chinese, English, or mixed Chinese-English.
• Use case: X article cover, WeChat cover, blog header, tutorial cover, technical infographic, knowledge poster, PPT title visual.
• Information structure: stage progression, before/after comparison, system architecture, feedback loop, maturity ladder, decision framework, bounded vs unbounded contrast.
• Visual metaphor: loop, ladder, control room, architecture stack, map, flywheel, pipeline, dashboard, board diagram, or infer from topic.
• Extra context: optional.
• Forbidden elements: optional; combine with style defaults.

Aspect Ratio Defaults

• X article cover / X header: 5:2, 1500x600.
• WeChat article cover: 2.35:1, 1640x700.
• Blog header / tutorial cover: 16:9, 2048x1152.
• Technical infographic / knowledge poster: 4:3, 1600x1200.
• Square knowledge card: 1:1, 1536x1536.
• Vertical poster: 4:5, 1600x2000.
• PPT cover: 16:9, 1920x1080.

State the exact ratio and canvas size in prompt-only output.

Visual System

Use these rules strictly:

• Background: warm cream paper, soft off-white, subtle grid or paper grain, light scan texture, faint shadowed border when useful.
• Color: cream base, charcoal black, warm gray, burnt orange, muted terracotta, small beige accents.
• Orange usage: headline keyword, stage tabs, arrows, loop segments, important nodes, chart bars, callout badges, status chips.
• Linework: black/charcoal marker outlines with clean editorial control, slightly hand-drawn but more polished than whiteboard sketches.
• Typography: bold condensed editorial headline for the title, hand-drawn or notebook labels for diagram details, clear hierarchy.
• Icons: simple unified line icons such as prompt bubble, LLM brain, database, code window, gear, checklist, chart, magnifier, user, shield, memory, API, tool, human review.
• Layout: strong title layer, structured diagram body, visible reading path, modular panels, clear arrows, no random decoration.
• Texture: use light dot grids, corner marks, thin dividers, paper tape, ruler-like lines, or small circuit traces sparingly.

Avoid dark/cyberpunk backgrounds, blue-purple AI gradients, glossy 3D, neon tech, realistic robots, stock business people, photoreal UI screenshots, chaotic arrows, unreadable microtext, fake dense paragraphs, random circuit-board filler, and palettes that drift away from cream/orange/charcoal.

Information Structure Selection

Pick one primary structure unless the user specifies one:

• Stage progression: for evolution, roadmap, maturity, migration, learning paths.
• Before/after comparison: for prompt-first vs loop-first, old vs new workflows, risk comparisons.
• System architecture: for AI agents, RAG, tool use, memory, observability, orchestration.
• Feedback loop: for evaluation, monitoring, continuous improvement, product iteration.
• Maturity ladder: for capability levels, organizational adoption, system sophistication.
• Decision framework: for practical team choices, tradeoffs, evaluation criteria.
• Bounded vs unbounded contrast: for safety, guardrails, autonomy, human review.

Prompt Output

For prompt-only mode, produce a provider-neutral image-generation prompt. Do not mention provider names, model names, or runtime-specific syntax unless the user explicitly asks.

Include:

1. Exact canvas ratio and size.
2. Topic, subtitle, use case, and language.
3. Selected information structure and why it fits.
4. Visual metaphor with 1-3 concrete anchors.
5. Text-image relationship and title hierarchy.
6. Background, linework, typography, and cream-orange-charcoal color system.
7. Diagram logic: panels, arrows, loop direction, labels, and hierarchy.
8. A concise avoid list.

If generating directly, keep analysis internal and output only the generated image result.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: imartinstudio
• Source: imartinstudio/cover-prompt-skills
• License: MIT

Install and usage instructions live in the source repository linked above.