Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add renky1025/agent-skills --skill image-designgit clone --depth 1 https://github.com/renky1025/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/renky1025/agent-skills/image-design)<a href="https://agentmods.dev/skills/renky1025/agent-skills/image-design"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/image-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/renky1025/agent-skills/image-design"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/image-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00203 | $0.01785 |
| Opus 5.5 | $0.00081 | $0.00714 |
| Sonnet 5.5 | $0.00041 | $0.00357 |
| Haiku 4.5 | $0.00020 | $0.00178 |
Grade A, and why
image-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-design: 高级质感图片设计
Purpose
把一句模糊的图片需求,翻译成可直接投喂给绘图工具的专业级提示词。技能遵循摄影学五大 核心维度,让画面从"塑料手办感"变成"有光线逻辑、有机位、有材质"的真实影像。它产出的是 提示词(可选直接调用 ImageGen 出图),不负责工程化规格或多图一致性。
核心公式,每一张高质量 AI 图片的提示词都应覆盖这五个维度:
[主体描述] + [构图位置] + [光线逻辑] + [相机视角] + [风格与质感]
When to Use
- 用户要生成图片或写图片提示词,且目标是有质感、写实、专业的效果。
- 用户觉得生成的图"太假/太塑料/像手办",需要诊断并改写提示词。
- 需要指定机位、镜头、光线、胶卷质感等摄影属性。
- 触发词:生成图片、写图片提示词、AI 绘图、写实照片、电影感、image prompt、photorealistic。
When NOT to Use
- 需要工程化规格出图、精确文字排版、透明底素材、UI 原型、角色一致性 -> 交给
spec-image。 - 需要咨询风格封面 / 方法论信息图 -> 交给
mckinsey-cover。 - 需要内容型信息卡片(多模块、主题配色)-> 交给
infocard。 - 用户只想直接出图、不需要提示词文本,且已有明确画面描述 -> 可直接走 ImageGen。
Workflow
- 理解需求:明确主体、情绪、用途与画面比例;缺关键信息时先问一句。
- 按五维构建:依次填充主体描述与构图位置、光线逻辑、相机视角、风格与质感。
每个维度具体的英文词库见
## References中对应文件。 - 整合提示词:把五维拼成一段完整提示词,风格材质词放在后半段作为润色;
需要固定画幅时以
--ar收尾。 - 解释设计逻辑:用一两句告诉用户每个选择为什么这么写(尤其是光线与机位)。
- 可选出图:用户想"看到效果"时,把提示词直接投给 ImageGen,不要另作改写。
- 迭代:结果不满意时只改一个维度(通常先改光线或机位),不要整段重写。
Decision Rules
- 要"最自然、最可信的纪实感" -> eye-level + 35mm + f2.8。
- 要"最高级的人像广告" -> low-angle + 85mm + f1.8。
- 要"最电影的环境人像" -> eye-level + 35mm + f2.8 + cinematic depth。
- 要"最戏剧的艺术风格" -> high-angle + 50mm + f5.6。
- 画面太干净/太完美 -> 加入不完美词(
Candid shot、Slightly shaky、film grain)。 - 选风格 -> 胶片感与 3D 渲染感二选一;导演/艺术家风格一次只借一种。
- 写光线 -> 描述光的逻辑(方向、亮暗、色温对比),不写"warm lighting"这类形容词。
- 需要机位 -> 镜头与角度一起写。
- 用户要成品图 -> 用 ImageGen 执行;只要文本 -> 只交付提示词并解释。
Constraints
- 五维必须覆盖,至少要有主体描述与构图/机位,不得只写一句形容词堆砌。
- 胶片风格与 3D 渲染风格不得混用。
- 一次只借用一种核心美学风格,禁止堆砌多个导演/艺术家风格。
- 风格与材质词放在提示词后半段,不前置抢占主体。
- 禁止用情绪空话替代可描述的具体细节;能具体就具体。
- 不虚构用户未提供的品牌、艺术家作品细节或版权角色。
Verification
交付前逐项确认:
- 五维齐全(主体、构图、光线、机位、风格质感),无明显缺项。
- 光线写的是逻辑(方向/光比/色温),不是单一形容词。
- 镜头与角度成对出现。
- 风格只借一种核心美学;胶片与 3D 渲染未混用。
- 已向用户解释关键选择的设计逻辑。
- 若调用 ImageGen,投喂的提示词与应用户确认的版本一致。
Output
- Outcome:一段可直接使用的完整图片提示词;用户要求出图时,另附 ImageGen 生成的图片。
- Done when:提示词覆盖五维、已解释设计逻辑、用户可复制即用;出图场景下图片已展示。
- Evidence:提示词文本本身;五维各自的对应片段;若出图,图片路径与所用提示词。
好坏对照范例见
examples/master-vs-amateur.md。
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · -134 lines · +101 tokens per session aff9225e8ce9
- 1mo ago First seen · 237 lines · 102 tokens per session scan A 4423ffd44b39
image-design is a skill published in the GitHub repository renky1025/agent-skills (11 stars, last pushed 15d ago), licensed MIT. It adds 203 tokens to every session and 1,785 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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