Logo Semantic Fusion logo

Logo Semantic Fusion

Organization
ZSeven-W
logo-semantic-fusion

Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work on a logo, symbol, app icon, monogram, or wordmark. Do not use for generic logo creation, general identity systems, illustration, packaging, or type selection unless semantic fusion is a stated or clearly central requirement.

Overview

PublisherZSeven-W
Repositorycraft-skills
Skill namelogo-semantic-fusion
Stars
233
Forks
33
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ZSeven-W on GitHub. Read the source before you install it.

Installation

Install the Logo Semantic Fusion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ZSeven-W/craft-skills/tree/main/skills/logo-semantic-fusion
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ZSeven-W/craft-skills.git /tmp/craft-skills
mkdir -p .claude/skills
cp -r /tmp/craft-skills/skills/logo-semantic-fusion .claude/skills/logo-semantic-fusion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logo Semantic Fusion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Logo Semantic Fusion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Logo Semantic Fusion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Logo Semantic Fusion

Build one structurally shared idea. Do not force fusion when a simpler wordmark, single symbol, or broader identity system serves the brand better.

Route the references

Choose one mode

  • Design or redesign: Run the full workflow. Preserve stated redesign invariants before exploring.
  • Critique: Inspect the supplied artifact and report evidence, hard-gate failures, and the smallest useful revisions. Do not redesign unless asked.
  • Compare: Apply the same brief, scales, and hard gates to every candidate. Allow reject all; do not invent a new candidate unless asked.
  • Prompt-only: Produce a production prompt, constraints, and future acceptance tests. Do not generate an artifact or claim visual QA, similarity screening, or production readiness.

Run the applicability gate

Proceed with fusion concepting only when all are true:

  1. At least two distinct brand signals are worth encoding.
  2. A plausible shared contour, stroke, void, glyph skeleton, or module system can carry them.
  3. Fusion improves the brand idea without sacrificing required-script legibility, category trust, or recognition at the intended scale.

If any condition fails, state why and recommend a pure wordmark, single symbol, abstract mark, or identity-system route. In critique or compare mode, treat forced fusion as a finding instead of ending the review.

Establish the brief

Extract or reasonably infer:

  • name, pronunciation, audience, category, promise, and personality;
  • target contexts and smallest committed size;
  • required scripts, must-keep elements, and forbidden motifs;
  • color, background, accessibility, and delivery constraints.

State consequential assumptions. Do not improvise unfamiliar letterforms or characters: consult authoritative form references and require a fluent reader or native reviewer before calling the result production-ready.

Inventory semantics and screen prior art

Map up to three primary signals to visual structures and language structures. Describe geometry, not only nouns.

During early ideation, list provisional category clichés and mark the avoid map not screened; do not block concept generation on open-ended research. Before advancing a direction or claiming distinctiveness, review a bounded current sample of relevant-category marks and the closest structural analogues. Record sources, dates, recurring silhouettes, distinctive constructions, and unresolved gaps without implying an exhaustive search. If current screening is unavailable, keep the work not screened and make no originality claim. Treat this as design-direction screening, never trademark clearance.

Generate distinct concept families

Create at least six thumbnails across at least three families and three fusion methods. Make every family change both:

  • the semantic pairing or interpretation; and
  • the dominant silhouette or visual skeleton.

Count a change of method alone as a variant, not a new family. Name the shared structural move for every thumbnail. Reject pasted icons, complete-symbol overlaps, generic containers, explanation-dependent readings, and close imitation.

Prefer two signals in the core mark. Add a third only when the same geometry carries it.

Select before polishing

Score candidates from 1 to 5 on brand fit, fusion integrity, screened distinctiveness, smallest-size legibility, one-color reproducibility, and cultural/category safety. Use higher scores for better or safer outcomes.

Advance only when no score is below 3, fusion integrity and cultural/category safety are at least 4, and the mean is at least 3.8. A hard-gate failure in the review checklist overrides the score. Lock silhouette, fusion relationship, dominant angle, stroke or fill logic, and essential negative space before rendering.

Render in controlled passes

  1. Draw in black and white.
  2. Normalize weight, corners, curves, spacing, and optical balance.
  3. Remove or responsively replace details that fail at the smallest committed size.
  4. Add color only after monochrome works.
  5. Solve the wordmark lockup separately unless it is the fused mark.

Use image generation for isolated flat concept sheets, not precision masters. Rebuild selected geometry as clean vector paths when production precision matters.

Verify the actual artifact

Run the review checklist on the rendered output. Test required sizes, one color, reverse, grayscale, and silhouette. If the full mark fails at small sizes, create a responsive micro mark and define the switch point.

Do not report visual QA from a prompt, source file, or intention. If no artifact was inspected, state visual QA not run. Preserve locked invariants during local fixes, then rerun every applicable check.

Hand off proportionately

For production delivery, define clear space, minimum sizes, responsive variants, lockups, color variants, and basic vector/export requirements. Mark non-applicable items explicitly. For concept-only or prompt-only work, list these checks as pending rather than implying completion.

Deliver by mode

  • Design or redesign: Return the brief, applicability result, semantic inventory, avoid map, family matrix, scores, selected principle, artifact, QA result, and handoff status.
  • Critique: Return evidence-linked findings, hard-gate status, pass, revise, or reject, and minimal revisions.
  • Compare: Return a normalized score table, hard-gate status, and a winner or reject all.
  • Prompt-only: Return the prompt, negative constraints, and future acceptance tests. State that no artifact was inspected and visual QA and production readiness remain unverified.

Always distinguish design-direction similarity screening from jurisdiction- and class-specific trademark clearance.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Logo Semantic Fusion AI skill do?

Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work on a logo, symbol, app icon, monogram, or wordmark. Do not use for generic logo creation, general identity systems, illustration, packaging, or type selection unless semantic fusion is a stated or clearly central requirement.

Why use Logo Semantic Fusion on TypingMind?

Because you install it once and use it with any model. Logo Semantic Fusion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Logo Semantic Fusion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZSeven-W/craft-skills/tree/main/skills/logo-semantic-fusion. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Logo Semantic Fusion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Logo Semantic Fusion?

As many as you like. As long as a model supports skills, you can use Logo Semantic Fusion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Logo Semantic Fusion AI skill free?

Yes. It is published on GitHub by ZSeven-W under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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