Logo Designer logo

Logo Designer

Community
luongnv89
logo-designer

Generate professional SVG logos from project context: 7 brand variants plus a showcase HTML page. Use for product marks, wordmarks, icons, and favicons. Don't use for raster-only logos, illustrations, or full brand guidelines.

Overview

Publisherluongnv89
Repositoryskills
Skill namelogo-designer
Stars
124
Forks
18
Bundled files
7
LicenseMIT
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.

  • 7 bundled files

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

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Logo Designer 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.
  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/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/logo-designer .claude/skills/logo-designer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logo Designer 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 Designer 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 Designer 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 Designer

Design modern, professional logos by analyzing project context and generating SVG-based brand assets.

Environment Check

Before running this skill, verify:

  • You're in a project directory with a README or package.json
  • You have write access to create /assets/logo/ directory
  • The project directory is a git repository (optional, but recommended)

If any check fails, the skill will stop and ask for clarification.

Subagent Architecture

This skill uses an Explorer+Executor (A) + Review Loop (C) architecture:

Phase 1: Brand Research
  ↓ (brand-researcher agent)
Phase 2: Interactive Style Selection
  ↓ Main agent: confirm style selection with user
Phase 3: Generate All 7 SVGs
  ↓ (svg-generator agent)
Phase 4: SVG Validation
  ↓ (svg-reviewer agent)
Final Output: 7 SVG files in /assets/logo/ + brand-showcase.html + Design Rationale

Agents:

  1. agents/brand-researcher.md — Reads project files, produces structured brand brief
  2. agents/svg-generator.md — Generates all 7 SVG files (mark, wordmark, full, icon, favicon, white, black)
  3. agents/svg-reviewer.md — Validates SVG structure (viewBox, no rasters, all files present, correct names)

Key Insight: 7 SVG files generated inline is the single biggest context cost. Brand research across multiple project files adds to the burden. The reviewer acts as a quality gate to catch SVG structure issues before files are committed.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting files in an existing repository, sync the current branch with remote:

bash
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin
git pull --rebase origin "$branch"

If the working tree is not clean, stash first, sync, then restore:

bash
git stash push -u -m "pre-sync"
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"
git stash pop

If origin is missing, pull is unavailable, or rebase/stash conflicts occur, stop and ask the user before continuing.

Workflow

Phase 1: Project Analysis

Automatically analyze the current project to understand brand context:

  1. Detect product identity - Check these files in order:

    • README.md - Product name, description, tagline
    • package.json - Name, description, keywords
    • pyproject.toml - Project name and description
    • Cargo.toml - Package name and description
    • go.mod - Module name
  2. Find existing brand assets - Search for:

    • /docs/brand_kit.md, /.docs/brand_kit.md, brand_kit.md
    • /docs/prd.md, prd.md - Product requirements with brand info
    • /assets/logo/, /public/logo, /static/logo - Existing logos
    • Tailwind config for existing color palette
  3. Identify project type from codebase structure:

    • Developer/CLI/Open Source - .github/, CLI entry points, MIT license
    • SaaS/Productivity - Web app structure, auth, dashboard patterns
    • Startup - Lean structure, MVP patterns
    • Enterprise/B2B - Complex architecture, integrations
    • Consumer/Mobile - React Native, Flutter, mobile-first patterns
  4. Summarize findings before proceeding:

    Product: [name]
    Type: [Developer Tool / SaaS / Startup / Enterprise / Consumer]
    Purpose: [1-sentence description]
    Audience: [target users]
    Existing colors: [hex codes if found, or "None detected"]
    Assets found: [list or "None"]

Phase 2: Logo Design

Pick a style based on the detected project type (Developer/SaaS/Startup/Enterprise/Consumer), then design a minimalist, geometric mark that reads at every size from 16px favicon to hero banner. Use detected brand colors when present, else apply the Default Style Guide (dark base + neon-green highlights only — never as a fill). Confirm with the user how the product name should be cased in the wordmark before generating any text-bearing SVG.

See references/design-principles.md for the style table, full visual/color/typography rules, and the Default Style Guide palette with system status colors.

Phase 3: Deliverables

Generate the canonical mark first, then derive 6 sibling variants from its exact path data so every file shares the same geometry. The svg-generator agent must receive the literal d="" strings — do not paraphrase shapes.

Output structure:

/assets/logo/
├── logo-mark.svg          # Canonical 64×64 mark (source of truth)
├── logo-full.svg          # 320×72 mark + wordmark
├── logo-wordmark.svg      # 180×40 text only
├── logo-icon.svg          # 512×512 app icon (centered, padded)
├── favicon.svg            # 16×16 simplified 2-layer mark
├── logo-white.svg         # 320×72 white (for dark backgrounds)
├── logo-black.svg         # 320×72 black (for light backgrounds)
└── brand-showcase.html    # Self-contained brand identity page

After writing all 7 files, read back mark, full, icon, white, and black variants and verify d="" strings are identical (or correctly scaled via transform). Fix divergences before continuing.

See references/svg-deliverables.md for full variant specifications, scale calculations, favicon simplification rules, and SVG requirements.

Phase 4–5: Documentation & Brand Showcase

After SVGs are written, output the design rationale (symbol meaning, color choices, typography reasoning), a hex-code color specification, and a Tailwind config snippet. Then generate /assets/logo/brand-showcase.html — a self-contained presentation page (hero, design concept, variant grid, palette, typography, dev reference, footer) and open it in the browser.

See references/brand-showcase.md for the full HTML page structure, design guidelines, and the documentation template.

Expected Output

For a CLI tool called "fastbuild", the skill produces:

/assets/logo/
├── logo-mark.svg          — 64×64 abstract "F" mark (canonical geometry)
├── logo-full.svg          — 320×72 mark + "FASTBUILD" wordmark
├── logo-wordmark.svg      — 180×40 text-only wordmark
├── logo-icon.svg          — 512×512 mark centered in rounded square
├── favicon.svg            — 16×16 simplified 2-layer mark
├── logo-white.svg         — 320×72 full logo in white (for dark backgrounds)
├── logo-black.svg         — 320×72 full logo in black (for light backgrounds)
└── brand-showcase.html    — self-contained brand identity presentation page

Design rationale summary presented to the user:

Product: fastbuild
Type: Developer/CLI Tool
Symbol: Abstract "F" from stacked horizontal speed bars
Colors: #0A0A0A base, #00FF41 neon green accent (borders and highlights only)
Typography: Inter Bold for wordmark

Edge Cases

  • No project context found: Ask the user for product name, product type, and one-sentence purpose before generating anything.
  • Existing brand colors detected: Use them instead of the default dark/neon-green palette; confirm with the user before proceeding.
  • User does not specify wordmark casing: Ask explicitly before proceeding (see references/design-principles.md for the casing rule and examples).
  • SVG geometry diverges across variants: Re-run the Phase 3 verification step (read back and diff d="" strings) and fix before finishing.
  • No git repository: Skip the branch and sync steps; write directly to the current directory and note this in the summary.
  • favicon.svg complexity: At 16×16 the full mark is unreadable — always simplify to 2 layers (outer + inner) and drop the middle detail layer; preserve proportional geometry.
  • User rejects the proposed style: Iterate on Phase 2 (style selection) until the user approves before generating any SVG files.

Acceptance Criteria

  • All 7 SVG files are written to /assets/logo/ with the correct filenames
  • logo-mark.svg is created first and its d="" path strings are used verbatim in all derived variants
  • Every SVG has a correct viewBox attribute and contains no embedded rasters
  • Monochrome variants (logo-white.svg, logo-black.svg) differ from logo-full.svg only in color, not geometry
  • favicon.svg is a simplified 2-layer version of the mark at 16×16
  • brand-showcase.html is written and opens correctly in a browser
  • Design rationale (symbol meaning, color choices, typography) is documented in the response
  • Color specification includes hex codes for all palette roles
  • Wordmark casing is confirmed with the user before any SVG containing text is generated

Step Completion Reports

After each phase, emit a ◆ [Phase] (step N of 4) status block listing per-check pass/fail and a final Result: line (PASS | FAIL | PARTIAL). Keep the agent's context budget tight by reusing the template — do not re-paraphrase it per phase.

See references/step-reports.md for the full report template, per-phase check lists, and a worked Analysis Summary example.

Notes

  • Always show logo previews on both light (#FAFAFA) and dark (#0A0A0A) backgrounds
  • Confirm wordmark casing before generating (see Phase 2 and Edge Cases)
  • If no project context is found, ask the user for: product name, type, and purpose
  • Prefer simplicity — a logo should be recognizable at 16x16 pixels
  • Consistency is non-negotiable: every variant must be visually recognizable as the same logo. The mark shape, number of layers, and accent elements must match across all files. The only things that change between variants are: color (monochrome), scale (favicon, icon), and presence of wordmark. If you cannot verify that paths match, the deliverable is incomplete.

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 Designer AI skill do?

Generate professional SVG logos from project context: 7 brand variants plus a showcase HTML page. Use for product marks, wordmarks, icons, and favicons. Don't use for raster-only logos, illustrations, or full brand guidelines.

Why use Logo Designer on TypingMind?

Because you install it once and use it with any model. Logo Designer 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 Designer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/logo-designer. 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 Designer?

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 Designer?

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

Is the Logo Designer AI skill free?

Yes. It is published on GitHub by luongnv89 under the MIT 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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