@s1dashu: 最近做了一个新的 Logo Skill,效果挺不错的: https://github.com/s1dashu/ip-as-logo-skill… 背景是:最近发现 grok bot, coze, workbuddy, 豆包, kiro 等…
摘要
这是一个用于AI代理的Logo技能,专注于生成简洁可爱的IP形象Logo,遵循特定设计原则以提升辨识度和适合作为App图标。
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缓存时间: 2026/08/20 04:58
最近做了一个新的 Logo Skill,效果挺不错的: https://github.com/s1dashu/ip-as-logo-skill…
背景是:最近发现 grok bot, coze, workbuddy, 豆包, kiro 等等产品,其实都是在将产品 IP 形象直接作为产品的 Logo,Logo 辨识度极高,同时增强 IP 记忆,感觉挺不错的。
所以最近我的所有产品 logo 都有一个对应的可爱小 IP,并且直接使用 IP 作为 Logo,Vibe 的产品多了,逐渐就形成了这个Skill。
这个 skill 的核心:
- Logo First,IP Second:首先保证它是一个简洁、清晰、易识别的 Logo,其次才是一个可爱的 IP 形象,避免复杂插画感。
- 极简构成:只保留最有辨识度的轮廓与五官,用尽可能少的形状完成设计,缩小后依然清楚。
- 圆润、浑厚的线条:避免尖角、细线和锐利结构,让形象更加友好、稳定,也更适合作为 App Icon。
- Flat-first 超轻拟物:整体仍以平面设计为主,只加入非常微妙的阴影、压痕和明暗层次,增强质感,但不会变成厚重的 3D 图标。
- 克制的色彩系统:支持单色或双色 IP;通常 IP 内不超过两种颜色,加上纯色背景,整张 Logo 最多控制在三种主色以内。
- 高占比人格化构图:IP 从左下角或右下角探出,占据较大的画面比例;通过简单的眼睛、嘴巴和姿态建立性格与记忆点。 一句话概括:把一个可爱的小 IP,压缩成一个精致的产品 Logo。
s1dashu/ip-as-logo-skill
Source: https://github.com/s1dashu/ip-as-logo-skill
IP as Logo
ip-as-logo is a compact Agent Skill for generating extremely simple, cute, company-ready IP mascots. It prioritizes lovable character appeal, bold rounded silhouettes, strict complexity limits, oversized corner composition, and a solid named background color.
It follows the open Agent Skills format and is designed to work with any compatible AI agent, rather than being tied to a specific agent product.
You can also browse the free IP as Logo Skill website, a searchable library backed by Cloudflare R2 and Supabase.

Don’t have Codex, Doubao, Coze, or Workbuddy? Visit our website to download ready-made logos for free. Every logo is free for commercial use.
What it guides
- One dominant silhouette built from roughly 4–7 large basic shapes
- Three semantic colors by default: two IP base colors plus one background color
- Three proposed directions followed by six independently generated candidates after user approval
- Familiar, broadly appealing animals as the default open-ended subject; objects, machines, fantasy artifacts, and obscure creatures require a clear product reason
- Context-aware, clearly separated subject and background colors with barely-there neo-skeuomorphic depth, described without percentages or prescribed gradient and shading formulas
- Thick, rounded forms without sharp or fragile details
- A 75–85% close crop that visibly peeks or rises from the lower-left or lower-right, with paired identifying features preserved
- Extreme simplification, cute baby-like appeal, and removal of nonessential lines and details
- One named solid background color filling the square, without image-mode language in the generation prompt
- Image-only generation prompts that never reveal logo, brand-mark, app-icon, or icon-asset use
- One-pass batch generation that preserves and delivers every returned image without filtering or automatic retries
Install
Install the complete skill with the Agent Skills CLI:
npx skills@latest add s1dashu/ip-as-logo-skill
The installer detects the repository’s root SKILL.md, lets you choose a supported coding agent, and installs the complete ip-as-logo directory, including its supporting assets. Use --global for a personal installation available across projects:
npx skills@latest add s1dashu/ip-as-logo-skill --global
Agent compatibility
Supported agents include Codex, Coze, Doubao, YouMind, Manus, Gemini Apps, and Replit Agent. This skill only supports agents with built-in image-generation capabilities that can return generated images as assets.
Use
Ask your AI agent for an IP mascot image, for example:
Create a very simple, cute rounded ghost IP character on a solid deep navy background.
The skill does not ask for a color-mode choice by default. Every default candidate uses three semantic colors: two IP base colors plus one background color. It no longer reserves any fraction of the candidate set for two-color images. A two-color image is generated only when the user explicitly requests it, and then uses background-colored negative space for facial marks rather than introducing a third color.
When the user already names an IP subject, the skill proposes three controlled design treatments of that subject. When the subject is open, it proposes familiar animal mascots first and ties each to a product attribute or brand promise. In open-ended batches, 95–100% of candidates should be familiar animals; non-animal subjects are limited to a small minority with a direct product connection, never used merely to manufacture novelty.
Large batches create variety within commercially plausible animal mascots through species or breed, ear and muzzle proportions, expression, lower-left versus lower-right emergence, crop, silhouette, and secondary color organization. Clocks, locks, industrial tools, measuring instruments, vehicles, abstract machines, fantasy artifacts, and obscure creatures are not default company mascots.
If the skill runs inside a product repository, it inspects relevant read-only context before asking questions. If product context is insufficient, it asks one consolidated round of background questions. Once context is sufficient, it always presents three concise directions and proposes generating six independent images. It proceeds after the user agrees, or immediately when the user has already explicitly authorized six outputs.
When the user accepts all three directions, the default batch contains two variants per direction: A1, A2, B1, B2, C1, and C2. When the user selects one direction, the skill generates six controlled variants of that direction. If the user rejects the proposed quantity or distribution, their replacement instructions take precedence.
Compatible agents may generate the six candidates in parallel with subagents up to the runtime’s available concurrency, using additional waves when needed. Codex can use ImageGen when available; other agent environments may use any configured image generator. If no generator is available, the skill asks the user to provide or enable one instead of pretending that an image was generated. Every result is a separate full-resolution square asset, never a six-image contact sheet.
When the user does not supply a palette, the skill favors clearly chromatic but restrained backgrounds rather than neon color or muddy gray. It keeps the normal design to exactly three semantic colors: two IP base colors plus the background. The generation prompt names the intended solid background color directly and avoids terms such as opaque, alpha, or transparency that may distract the image model from the desired visual result.
Although the project is named ip-as-logo, the prompt sent to the image generator describes only the requested square character image. It never calls the result a logo, brand mark, app icon, or icon asset, and it does not prepend use-case metadata that reveals those purposes.
Generation is intentionally treated as a creative draw. Each requested candidate is generated once and delivered as returned. The skill does not inspect transparency, block outputs, classify candidates as compliant or non-compliant, or automatically retry results because of their background, colors, composition, gradients, shading, or dimensionality. Users can explicitly request another draw or a refinement after reviewing the batch.
Repository structure
SKILL.md
assets/ip-as-logo-wall.webp
README.md
LICENSE
The skill itself intentionally consists of a single instruction document. The repository also includes the showcase image above, but no scripts, style references, or generation dependencies.
Model behavior
Image-generation models are stochastic and may interpret individual constraints differently. The skill preserves and returns every result without validation gates, transparency checks, automatic rejection, automatic retry, or silent repair.
License
MIT
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