ip-as-logo-skill: An Agent Skill for Rounded IP Mascot Logos

What ip-as-logo-skill actually constrains

Most image generation for brand characters fails in the same way. The model returns something detailed, half-illustrated and impossible to redraw at 32 pixels. ip-as-logo-skill is a response to that failure mode. It is an Agent Skill, meaning it ships as instructions an AI agent loads rather than as a library you import, and its instructions narrow the output space hard: one dominant silhouette built from roughly 4 to 7 large basic shapes, three semantic colors by default (two IP base colors plus one background color), thick rounded forms, and no sharp or fragile details.

The intended user is a designer or founder working inside an agent that already has a strong image model attached. The README lists Codex, Coze, Doubao, YouMind, Manus, Gemini Apps and Replit Agent as supported agents, and states that the skill follows the open Agent Skills format so it is not tied to one vendor. If you do not have any of those agents, the project points to ipaslogo.com, a searchable library backed by Cloudflare R2 and Supabase, where ready-made logos are offered free for commercial use.

The design target is a mascot, not a mark. The README is explicit that clocks, locks, industrial tools, measuring instruments, vehicles, abstract machines, fantasy artifacts and obscure creatures are not default company mascots. In open-ended batches it asks for 95 to 100 percent familiar animals, with non-animal subjects limited to a small minority that has a direct product connection.

The three-directions-then-six-candidates workflow

The mechanism is a staged conversation, not a single prompt. When the subject is open, the skill proposes three concise directions and ties each to a product attribute or brand promise. When the user already names a subject, it proposes three controlled design treatments of that subject instead. It then proposes generating six independent images and proceeds after agreement, or immediately if the user has already explicitly authorized six outputs.

The default batch maps variants to composition. If the user accepts all three directions, the batch is A1, A2, B1, B2, C1 and C2, where the first variant of each direction emerges from the lower-left and the second from the lower-right. If the user picks one direction instead, odd-numbered variants use the lower-left and even-numbered variants the lower-right. Either path produces the same three-left, three-right split. User instructions that reject the proposed quantity or distribution override the default.

Composition is bounded too. Every default candidate emerges from the lower-left or lower-right rather than the center or bottom-center, and fills roughly 85 to 95 percent of the square so the character stays visually dominant. The README says the skill does not prescribe exact edge contact or a fixed crop, so bottom or side cropping is allowed as a way to strengthen the corner emergence but is not mandated. Each result is a separate full-resolution square asset, never a six-image contact sheet. Compatible agents may generate the six in parallel with subagents up to the runtime's available concurrency, using additional waves when needed.

Installing ip-as-logo-skill and generating a first mascot

The README gives one install path, through the Agent Skills CLI. 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.

npx skills@latest add s1dashu/ip-as-logo-skill

Add --global when you want a personal installation available across projects rather than one scoped to the current directory.

npx skills@latest add s1dashu/ip-as-logo-skill --global

After installation, the skill loads when your agent starts. You do not call it directly; you ask for an image in ordinary language. The README gives this example prompt.

Create a very simple, cute rounded ghost IP character on a solid deep navy background.

What you should see next is not an image. The skill first checks for a supported top-tier image model, then presents three concise directions and proposes six images. It proceeds once you agree, or immediately if you already authorized six outputs. When the user accepts all three directions, the batch is A1, A2, B1, B2, C1 and C2, three emerging from the lower-left and three from the lower-right.

Color, background and the prompt-writing rules

Two rules in the README are worth reading closely, because they are where the skill departs from how people normally prompt image models.

The first concerns color. The default is exactly three semantic colors: two IP base colors plus the background. The skill no longer reserves any fraction of the candidate set for two-color images. A two-color image is produced only when the user explicitly asks for one, and it then uses background-colored negative space for facial marks rather than introducing a third color. When the user supplies no palette, the skill lowers background saturation so the result reads as muted and controlled while staying clearly chromatic. The README frames the alternative as vivid, gray or muddy, which is a fair description of what happens when you let a model pick background saturation on its own.

The second concerns the prompt text itself. The generation prompt names the intended solid background color directly and avoids words such as opaque, alpha or transparency that the README says may distract the image model from the desired visual result. More unusually, although the project is named ip-as-logo, the prompt sent to the 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. That is a deliberate separation between what you intend to do with the file and what the model is asked to draw, and it is the kind of detail that is easy to lose when you write your own prompts.

Where ip-as-logo-skill breaks down

The hard dependency is the most obvious limitation. The README states that the agent must have a top-tier image model, preferably GPT Image 2, or Seedance 5.0 Pro, Nano Banana Pro (Gemini Image Pro) or Nano Banana 2 (Gemini Image Flash). If none is available, you must enable a suitable tool or provide its API key. The skill checks for a supported model before generation and asks the user to enable one or supply a key when necessary.

It also states that the skill never falls back to SVG, and that another image model may be used only with explicit user consent, with no guarantee of equivalent quality. So if your pipeline needs vector output, this is the wrong tool by design, and no amount of prompt tuning will change that. If you are running a weaker model and hoping the skill's constraints will compensate, the README does not claim they will.

The generation loop is another boundary. The README describes generation as intentionally treated as a creative act, with one-pass batch generation that preserves and delivers every returned image without filtering or automatic retries. That means a batch with two or three unusable candidates is a normal outcome you handle yourself, not something the skill repairs. There is also no typography anywhere in the described workflow. Nothing in the README suggests the skill places a wordmark, and since the prompt is forbidden from mentioning logos at all, you should not expect a finished lockup from one run.

How it differs from prompting an image model directly

The nearest alternative is not another skill. It is writing the image prompt yourself, or using a general-purpose prompt library for mascot and character art. The difference is where the constraints live. With a hand-written prompt, the constraint set is whatever you remember to type that day, and it drifts between sessions and between team members. With ip-as-logo-skill, the constraints are a file the agent loads every time: shape count, three-color default, lower-corner emergence, 85 to 95 percent fill, the ban on naming the output a logo in the prompt, and the fixed three-left three-right split.

A second alternative is the project's own website, ipaslogo.com. That is a different trade: you get ready-made logos that the README says are free for commercial use, but you give up control over the subject and the direction. The skill is for when you need a specific character for a specific product; the site is for when any competent mascot will do.

A third comparison is a conventional vector design tool. Those give you exact geometry and clean export, which the skill explicitly does not promise, since it never falls back to SVG. What they do not give you is six candidate directions in one pass. The honest framing is that the skill trades precision for volume and consistency, and you should pick it only if that trade matches your stage.

Maintenance, licence and what a fork costs you

The repository is not archived, and the last push was on 2026-08-22. The project is MIT licensed, which permits commercial use, modification and redistribution provided the copyright notice and permission notice are included. That matters here because the README also states that logos downloaded from ipaslogo.com are free for commercial use, so both the skill and its outputs are positioned for company work. This is a description of the licence text, not legal advice; if you are embedding the skill in a product, read the LICENSE file in the repository root yourself.

The upgrade surface is small. The top-level entries are .gitignore, LICENSE, README.md, SKILL.md and assets, and there are no retrieved releases, so there is no versioned changelog to track. In practice you upgrade by re-running the install command, which pulls the current SKILL.md and its assets. Because the skill is instructions rather than code, a change to the wording of SKILL.md can alter your output style without any API change, which is the real maintenance cost: re-checking a sample batch after you update. The other cost is per-generation. Six full-resolution square images is six model calls, and the README offers no caching or deduplication, so budget accordingly before you run large batches.

Editorial conclusion

Adopt ip-as-logo-skill if your agent already has access to GPT Image 2, Seedance 5.0 Pro, Nano Banana Pro or Nano Banana 2, and you want a consistent house style rather than free-form image prompting. Do not adopt it if your only available model is a mid-tier one, if you need SVG output, or if you want a logo with typography, since the skill is explicit that it never falls back to SVG and that the prompt never describes the result as a logo. Verify two things first: that your agent can reach one of the named image models, and that a six-image batch at full square resolution fits your generation budget, because the skill treats generation as a creative act and does not filter or retry results automatically.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. Project website
4. README
5. s1dashu/ip-as-logo-skill on GitHub