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Higgsfield Brandkit

OrganizationPopular
higgsfield-ai
higgsfield-brandkit

Create and extend complete visual brand systems through the Higgsfield CLI and bundled deterministic local tooling: palettes, SVG logo marks, typography, mockups, social graphics, packaging, signage, merchandise, posters, presentation decks, and editable PPTX/PDF brandbooks. Preserves official supplied assets, persists approvals locally, and regenerates only dependent outputs. Use when: "create a brand kit", "make a visual identity", "design a logo and brandbook", "apply this logo to branded assets", "make packaging or signage", or "extend our existing branding". Chain with higgsfield-generate for general image production and Marketing Studio brand-kits when importing website metadata for ads. NOT for unbranded image generation (use higgsfield-generate), product catalog photography (use higgsfield-product-photoshoot), website implementation (use higgsfield-websites), or native Figma/Canva/PSD/AI delivery.

Overview

Publisherhiggsfield-ai
Repositoryskills
Skill namehiggsfield-brandkit
Stars
1.2K
Forks
237
Bundled files
28
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.

  • 28 bundled files

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

  • Open source

    Published by higgsfield-ai on GitHub. Read the source before you install it.

Installation

Install the Higgsfield Brandkit 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/higgsfield-ai/skills/tree/main/higgsfield-brandkit
  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/higgsfield-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/higgsfield-brandkit .claude/skills/higgsfield-brandkit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Higgsfield Brandkit 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 Higgsfield Brandkit 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 Higgsfield Brandkit 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.

Higgsfield Brandkit

Build a coherent identity and its requested applications. Treat supplied brand facts and official assets as fixed constraints.

Bootstrap

  1. Resolve SKILL_ROOT to this skill's installed directory and create a durable project directory:

    bash
    BRANDKIT_WORKDIR="${PWD}/brandkit"
    BRANDKIT_STATE="${BRANDKIT_WORKDIR}/state.json"
    mkdir -p "${BRANDKIT_WORKDIR}"
  2. Read prerequisites. Check tools before the stage that needs them. Never install system packages without the user's permission.

  3. If higgsfield is missing, install it only after permission:

    bash
    curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
  4. If higgsfield account status fails with an authentication or workspace error, ask the user to run higgsfield auth login or select a workspace, then wait.

  5. Inspect live model contracts before paid generation:

    bash
    higgsfield model get recraft_v4_1 --json
    higgsfield model get seedream_v5_pro --json
    higgsfield model get gpt_image_2 --json

CLI mapping

OperationCommand
Discover a modelhiggsfield model get <model> --json
Generate and pollhiggsfield generate create <model> ... --wait --json
Resume a jobhiggsfield generate wait <job_id> --json
Upload a local assethiggsfield upload create <path> --json
Import website metadatahiggsfield marketing-studio brand-kits fetch --url <url> --wait --json
Read/write approval statepython3 "$SKILL_ROOT/scripts/brandkit.py" state ...
Render review boardspython3 "$SKILL_ROOT/scripts/brandkit.py" preview ...
Inspect selected logopython3 "$SKILL_ROOT/scripts/brandkit.py" logo-inspect ...
Export logo filespython3 "$SKILL_ROOT/scripts/brandkit.py" logo-export ...
Build a Brandbookpython3 "$SKILL_ROOT/scripts/brandkit.py" brandbook-build ...

Local image paths passed with --image are auto-uploaded. Keep HTML, SVG, PPTX, and PDF deliverables as local project files unless the user explicitly needs a hosted copy.

User-facing behavior

  • Match the user's language. Keep Design Brain reasoning, prompts, state mechanics, scripts, model lookup, and QA internals private.
  • Send at most one short status sentence per visible generation batch, then stay quiet until the result is ready.
  • Ask one compact set of only unresolved blocking questions. Never repeat facts or force a complete identity questionnaire for a partial task.
  • After each palette, logo, typography, or downstream review, stop and wait for ordinary user feedback.
  • Never infer approval from silence, successful generation, or your own preference.
  • Preserve exact user copy. Never invent positioning, values, claims, ingredients, prices, certifications, statistics, or regulatory content.

Core workflow

  1. Classify the request.

    • apply-existing: use supplied official assets without redesigning them.
    • extend-partial: create only missing slots required by the requested output.
    • create-identity: create a new logo or identity only when explicitly requested.
  2. Read state. Run:

    bash
    python3 "$SKILL_ROOT/scripts/brandkit.py" state \
      --state-file "$BRANDKIT_STATE" --action get_status

    Local state is durable. Never paste, hand-edit, or recreate approvals when the state file exists.

  3. Run intake and asset analysis. Read intake, asset analysis, state routing, and exact state payloads. Lock every user-declared official logo, palette, and typography slot immediately.

  4. Create the Brand Lock. Read Brand Lock. Record exact spelling, official assets, colors, fonts, layout/shape rules, requested outputs, and forbidden treatments.

  5. Require only the slots the output uses.

    • logo-only → palette + logo for a new mark; official logo alone for an existing mark
    • palette-only → palette
    • typography-only → typography
    • copy-free mockup/merch → logo; add palette only when color/application requires it
    • text-bearing social/packaging/poster/signage → logo + palette + typography
    • Brandbook/deck → logo + palette + typography
  6. Build missing foundation slots. Read Design Brain, concept boards, inline reviews, and only the needed palette, logo, or typography module.

  7. Continue the original request as soon as its required slots are approved. Never ask the user to choose scope again.

  8. Load only the requested production module:

  9. QA and approval. Read QA and iteration. Repair only the failing output. Save a downstream element only after explicit approval with its exact foundation dependencies.

New identity sequence

1. Palette

Render 2–3 exact palette options as deterministic HTML using preview payloads. Show PNG screenshots plus editable HTML files and wait. Persist the selected palette with approve_palette before logo generation.

2. SVG logo marks

Read logo prompt enhancer. Produce exactly three distinct symbol-only mechanisms and one Recraft prompt for each. Write each long prompt to a file and submit separately:

bash
higgsfield generate create recraft_v4_1 \
  --model_type vector \
  --colors @"${BRANDKIT_WORKDIR}/logo-colors.json" \
  --background_color '#F7F7F5' \
  --aspect_ratio 1:1 \
  --resolution 2k \
  --wait --json < "${BRANDKIT_WORKDIR}/logo-candidate-1.txt"

Use the returned SVG URLs directly for review. After selection, inspect the exact SVG without altering it:

bash
python3 "$SKILL_ROOT/scripts/brandkit.py" logo-inspect \
  --source "<selected Recraft SVG URL or absolute local path>"

Persist the exact job ID, SVG URL, name, palette revision, and returned canonical geometry fingerprint with approve_logo.

3. Typography

Propose 2–3 unique display/body pairs using supplied fonts or verified Google Fonts. Render the real brand name and sample copy through the preview script. Persist only the selected pair with approve_typography.

Interactive flows always stop for palette, logo, and typography selections. Explicit no-question mode may choose and persist a palette, but it still shows all three SVG logo candidates and stops for the user's logo selection; exact brand marks are never self-approved.

Consistency invariants

  • Reuse the same approved logo source everywhere. Never redraw an official or selected SVG when deterministic placement/export is possible.
  • A generated logo depends on the palette revision used to create it. Changing that palette invalidates the generated logo and its dependents; changing typography does not invalidate the symbol mark.
  • Changing a foundation slot invalidates only downstream elements that list that slot in required_slots.
  • Copy the same Brand Lock values into every related generation prompt: exact hex, font roles, shape language, placement, clear space, composition, and forbidden treatments.
  • Use Recraft V4.1 vector mode only for new logo marks.
  • Use Seedream as the primary photoreal mockup generator. Use GPT Image 2 only for the controlled stage that adds readable text or exact graphic details.
  • Use local deterministic SVG/PPTX/HTML construction for exact copy and editable layouts. Do not ask an image model to fake editable files.
  • Do not promise native Figma, Canva, PSD, AI, or EPS files.

Deterministic scripts

Create JSON input files under "$BRANDKIT_WORKDIR"; never interpolate user text directly into shell arguments.

bash
python3 "$SKILL_ROOT/scripts/brandkit.py" preview \
  --input "$BRANDKIT_WORKDIR/reviews.json" \
  --output-dir "$BRANDKIT_WORKDIR/reviews"

python3 "$SKILL_ROOT/scripts/brandkit.py" logo-export \
  --input "$BRANDKIT_WORKDIR/logo-export.json" \
  --output-dir "$BRANDKIT_WORKDIR/logo"

python3 "$SKILL_ROOT/scripts/brandkit.py" brandbook-build \
  --state-file "$BRANDKIT_STATE" \
  --input "$BRANDKIT_WORKDIR/brandbook.json" \
  --output-dir "$BRANDKIT_WORKDIR/brandbook"

For logo export, load logo export payloads. For Brandbooks, use the bundled builder only; never substitute an improvised PowerPoint or PDF generator after a deterministic contract failure.

Failure policy

  • Retry a failed Recraft or image-generation request once with the same locked concept and corrected contract. Stop after the second equivalent failure.
  • If preview or logo export fails twice, report the concrete error; never replace it with ad-hoc SVG rewriting.
  • If the Brandbook template, font, or conversion contract fails, stop immediately. Do not produce a visually different fallback and call it canonical.
  • If exact typography or official-logo fidelity cannot be preserved, disclose the limitation instead of claiming completion.
  • Never expose raw auth tokens or credentials in files, logs, or chat.

Delivery

For Brandbooks, follow the strict response contract in brandbook: PPTX link/path, PDF link/path, and font-install warning only.

For other outputs return:

  1. The requested visual files and previews.
  2. A compact Brand Lock summary.
  3. Editable versus flattened format labels.
  4. Required-font/import limitations.
  5. Stable variant names for targeted revisions.

Reference index

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 Higgsfield Brandkit AI skill do?

Create and extend complete visual brand systems through the Higgsfield CLI and bundled deterministic local tooling: palettes, SVG logo marks, typography, mockups, social graphics, packaging, signage, merchandise, posters, presentation decks, and editable PPTX/PDF brandbooks. Preserves official supplied assets, persists approvals locally, and regenerates only dependent outputs. Use when: "create a brand kit", "make a visual identity", "design a logo and brandbook", "apply this logo to branded assets", "make packaging or signage", or "extend our existing branding". Chain with higgsfield-gener...

Why use Higgsfield Brandkit on TypingMind?

Because you install it once and use it with any model. Higgsfield Brandkit 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 Higgsfield Brandkit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/higgsfield-ai/skills/tree/main/higgsfield-brandkit. 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 Higgsfield Brandkit?

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 Higgsfield Brandkit?

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

Is the Higgsfield Brandkit AI skill free?

Yes. It is published on GitHub by higgsfield-ai 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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