Logo Design Skill
Turn a brand brief into tested SVG logo concepts, then build a brand kit after you choose a direction.
- Source repo
- kaankiziltug/logo-design-skill
- Stars
- ★ 2.3k
- Last updated
- 7d ago
- License
- MIT
- Primary language
- HTML
- FA score
- 64/100 · Some gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code · Claude.ai
- Cost
- Free, no paid service needed
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- An independent designer needs to turn a new brand brief into logo directions and prepare a delivery kit after the client picks a concept.
- Not a fit if
- Teams expecting a complete brand kit without choosing a concept
- Users who only need general image generation, not SVG and identity work
- Source review
- 64/100 · Some gaps
What does this agent do, and when should you use it?
This logo-design skill guides Claude and other agents that support Agent Skills through a process from discovery to production-ready SVGs. It includes SKILL.md, on-demand design references, Python scripts, and a reference library of more than 1,400 real-world SVG logos. The workflow researches category examples, develops 8–12 concept directions, builds three, and tests them through audits and size and colour treatments. It stops at a concept checkpoint; colour, lockups, presentation boards, icons, and guidelines follow only after the user chooses a direction and asks for the kit. Install the skill folder in a supported agent’s skills directory; the scripts use Python’s standard library, while PNG/ICO export needs an available renderer.
The skill uses a supplied brief or stated assumptions for discovery and word mapping, then searches reference marks by technique or industry with scripts/search_library.py. It guides the agent to propose 8–12 concepts and build three as SVGs, then uses scripts/svg_audit.py to check structure, colours, complexity, angles, tiny details, and centring. scripts/concept_sheet.py assembles the concepts; scripts/preview_sheet.py creates tests for size, one-colour and reversed treatments, contexts, and competitor shelves. The checkpoint presents the concepts and a recommendation, then waits for the user to choose a direction. After approval, it can produce brand colours, lockups, presentation boards, icons, and guidelines, with scripts/render_png.py and scripts/export_variants.py available for rendering and export.
- An independent designer needs to turn a new brand brief into logo directions and prepare a delivery kit after the client picks a concept.
- A small product team wants to check whether an SVG mark remains clear at 16 px and in one-colour or reversed treatments.
- A brand consultant wants to search reference marks by industry and technique before designing, to study conventions and avoid look-alikes.
- A team with an existing mark needs an SVG audit, a test sheet, or exported favicon and app-icon variants.
How do you install or deploy this agent?
For Claude Code, install through the plugin marketplace:
/plugin marketplace add kaankiziltug/logo-design-skill
/plugin install logo-design@logo-design-skillAlternatively, clone the repository and copy the skill folder:
git clone https://github.com/kaankiziltug/logo-design-skill.git
cp -r logo-design-skill/skills/logo-design ~/.codex/skills/logo-designFor Gemini CLI, the documented personal directory is ~/.gemini/skills/logo-design; for other Agent Skills-compatible agents, follow their skills-directory documentation. Claude.ai and Claude Desktop can use logo-design.zip from Releases or a package created with python3 tools/package_skill.py; logo-design-lite.zip omits the SVG files. Scripts require Python 3.8+. PNG/ICO export requires one available renderer: cairosvg, rsvg-convert, Inkscape, a Chromium-based browser, or macOS Quick Look.
How do you use this agent?
Start a new session after installation and ask for a logo, wordmark, monogram, app icon, favicon, rebrand, or critique. For example:
Design a logo for Harbor, a savings app for first-time savers. It should feel calm and safe.You can also run scripts from skills/logo-design:
python3 scripts/search_library.py --technique negative-space --exemplary
python3 scripts/svg_audit.py my-logo.svg
python3 scripts/concept_sheet.py a.svg b.svg c.svg --names "A" "B" "C" --recommend 1 -o concepts.png
python3 scripts/preview_sheet.py my-logo.svg --refs-industry developer-tools -o preview.html
python3 scripts/render_png.py my-logo.svg --size 512 -o my-logo.png
python3 scripts/export_variants.py my-logo.svg --mono "#0F7C80" --icon-bg "#0F7C80" --web-iconsThe skill presents concepts and pauses at the checkpoint. It continues with the full brand deliverables only after you choose a direction and request the kit.
What are this agent's strengths and limitations?
- A defined user-choice checkpoint lets the client select a concept before the full kit is produced.
- The library contains 1,400+ classified SVG logos searchable across mark types, techniques, geometry, and industry.
- Standard-library Python scripts cover SVG audits, concept and test sheets, presentation boards, and icon-variant exports.
- The testing workflow includes 16 px, one-colour, reversed, contextual, and competitor-shelf checks.
- The workflow works best with an agent that can view images, since it renders and checks its drafts.
- PNG/ICO export requires an additional renderer: cairosvg, rsvg-convert, Inkscape, a Chromium-based browser, or macOS Quick Look.
- Logos in the reference library are their owners’ trademarks and are excluded from the MIT license; users must follow their separate terms.
- Full-kit production waits for the user to choose a concept at the checkpoint, so it does not suit unattended end-to-end runs.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Logo Design Skill This agent | 64 · Some gaps | Agent plugin / skillFree | ★ 2.3k | 7d ago | HTML | Codex · Claude Code · Claude.ai |
| Designer Skills Pack | 38 · Major gaps | Agent plugin / skillFree | ★ 2.9k | 1mo ago | Markdown | Claude Code |
| Taste-Skill: AI Frontend Aesthetic Upgrade | 31 · Major gaps | CLIFree + model costs | ★ 93k | today | JavaScript | ChatGPT · Codex · Claude Code |
| DESIGN.md Specification | 80 · Good | CLIFree | ★ 28k | 2mo ago | TypeScript | — |
How does FollowAgents rate this agent?
Why each dimension lost points
The README describes dependency-free Python tools and a clear pause after concepts: the full kit proceeds only after the user chooses a direction and asks for it. The workflow grants only contents: read and disables persisted checkout credentials, but this covers CI rather than the skill’s actual file access. The supplied evidence does not explain user-file access, external calls, handling of sensitive briefs, or recovery from mistakes, so those criteria lose points. The library is described as real-world and intended to avoid copying, but no specific sources or attribution are shown.
The README presents a coherent brief-to-research-to-concepts-to-testing workflow with a checkpoint, and the workflow schedules cross-platform smoke scripts across Python versions. This supports internal consistency and modest dependency availability. The files do not show concrete failure messages or recovery behavior, and a badge and workflow configuration do not establish that runs succeeded, so these scores are not full marks.
The repository targets Claude, Gemini CLI, Codex CLI, and other Agent Skills environments, and presents examples across many industries and stages from concepts to a brand kit. It also notes that image-viewing ability improves the workflow. Trigger conditions, task exclusions, and per-agent compatibility limits are only lightly specified, reducing boundary and trigger-precision scores.
Clear sections, a process diagram, detailed installation paths, a cross-agent installation table, and many examples support strong information architecture and install notes; an MIT license is supplied. The provided README excerpt ends mid-example, and the evidence for FAQs, known limitations, changelog/version history, and maintenance or update ownership is thin. Folder paths provide some naming evidence, but no versioning policy is shown.
The stated deliverables include SVGs, test sheets, presentation boards, icons, and guidelines, while examples describe concept choices and revisions across industries. This supports useful output and clear marginal value. Dependency-free tools may reduce cost, but there is no evidence quantifying time, model costs, output quality, or maintenance burden, so cost-benefit is not fully established.
The workflow, examples, and CI configuration provide some traceability for product claims, and the examples distinguish recommendations from client choices. The supplied evidence consists only of the README, MIT license, and workflow; it lacks the skill instructions, tool implementations, and test results for independent corroboration. The README calls the examples end-to-end runs, but static evidence cannot verify those results, so successful execution is not inferred.
- This review uses only the supplied static files. The skill instructions, tool source, test results, and reference-logo sources were not provided, so actual file operations, error handling, compatibility, and example runs cannot be verified.
- Before use, decide what brief information may be shared and verify the sources, licenses, and similarity of reference logos; the supplied material contains no specific attribution list.