Higgsfield Brandkit
Higgsfield AIOfficialPublished by Higgsfield AI
- Skill
- Content generator
- Design and creative
- MIT
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Higgsfield-brandkit is a deterministic tool designed to create and extend visual brand systems. It manages the lifecycle of brand assets including logos, palettes, typography, and various marketing materials like posters, packaging, and presentation decks. The tool emphasizes consistency by locking authoritative assets and ensuring that generated outputs adhere to established brand constraints. It supports the creation of editable brandbooks and manages the state of approvals locally to prevent redundant work or unauthorized deviations from the brand identity.
What it does
- Generates and manages visual brand elements like logos, palettes, and typography.
- Creates marketing assets including social graphics, posters, packaging, and merchandise.
- Builds editable brandbooks and presentation decks in PPTX and PDF formats.
- Maintains a local state of approved assets to ensure consistency across all outputs.
- Imports and analyzes existing brand metadata for use in new designs.
- Enforces strict adherence to defined brand rules and constraints during generation.
When to use it
- When you need to create a new visual identity or brand kit from scratch.
- When you need to extend an existing brand system with new marketing materials.
- When you need to generate branded assets while ensuring they match official logo and color specifications.
- When you need to produce consistent, editable brand documentation like brandbooks or decks.
Advantages
- Ensures visual consistency by locking authoritative assets as the source of truth.
- Reduces manual effort by automating the generation of dependent brand assets.
- Provides a structured workflow for reviewing and approving brand elements.
- Supports deterministic outputs for specific brand requirements.
- Maintains local project state for efficient iteration and revision.
Usage examples
- “Create a brand kit for my new coffee shop.”
- The agent will initiate the brand identity workflow, starting with palette and logo development based on your input.
- “Apply our existing logo to a new set of social media graphics.”
- The agent will analyze your official logo and brand assets to generate consistent social media templates.
- “Design a brandbook for our company.”
- The agent will compile your approved logo, palette, and typography into an editable brandbook document.
- “Extend our current branding to include packaging designs.”
- The agent will use your existing brand lock to generate packaging concepts that align with your established visual identity.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/higgsfield-ai/higgsfield-brandkit/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip higgsfield-brandkit.zip -d .agents/skills
Documentation by Higgsfield AI
Higgsfield Brandkit
Build a coherent identity and its requested applications. Treat supplied brand facts and official assets as fixed constraints.
Bootstrap
Resolve
SKILL_ROOTto this skill's installed directory and create a durable project directory:BRANDKIT_WORKDIR="${PWD}/brandkit" BRANDKIT_STATE="${BRANDKIT_WORKDIR}/state.json" mkdir -p "${BRANDKIT_WORKDIR}"Read prerequisites. Check tools before the stage that needs them. Never install system packages without the user's permission.
If
higgsfieldis missing, install it only after permission:curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | shIf
higgsfield account statusfails with an authentication or workspace error, ask the user to runhiggsfield auth loginor select a workspace, then wait.Inspect live model contracts before paid generation:
higgsfield model get recraft_v4_1 --json higgsfield model get seedream_v5_pro --json higgsfield model get gpt_image_2 --json
CLI mapping
| Operation | Command |
|---|---|
| Discover a model | higgsfield model get <model> --json |
| Generate and poll | higgsfield generate create <model> ... --wait --json |
| Resume a job | higgsfield generate wait <job_id> --json |
| Upload a local asset | higgsfield upload create <path> --json |
| Import website metadata | higgsfield marketing-studio brand-kits fetch --url <url> --wait --json |
| Read/write approval state | python3 "$SKILL_ROOT/scripts/brandkit.py" state ... |
| Render review boards | python3 "$SKILL_ROOT/scripts/brandkit.py" preview ... |
| Inspect selected logo | python3 "$SKILL_ROOT/scripts/brandkit.py" logo-inspect ... |
| Export logo files | python3 "$SKILL_ROOT/scripts/brandkit.py" logo-export ... |
| Build a Brandbook | python3 "$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
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.
Read state. Run:
python3 "$SKILL_ROOT/scripts/brandkit.py" state \ --state-file "$BRANDKIT_STATE" --action get_statusLocal state is durable. Never paste, hand-edit, or recreate approvals when the state file exists.
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.
Create the Brand Lock. Read Brand Lock. Record exact spelling, official assets, colors, fonts, layout/shape rules, requested outputs, and forbidden treatments.
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
Build missing foundation slots. Read Design Brain, concept boards, inline reviews, and only the needed palette, logo, or typography module.
Continue the original request as soon as its required slots are approved. Never ask the user to choose scope again.
Load only the requested production module:
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:
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:
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.
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:
The requested visual files and previews.
A compact Brand Lock summary.
Editable versus flattened format labels.
Required-font/import limitations.
Stable variant names for targeted revisions.
Reference index
Prerequisites — stage-specific local dependencies and install commands.
Intake — minimal questions and input routing.
Asset analysis — official/reference classification and measurement.
State routing and state payloads — persistent approvals.
Brand Lock — canonical visual constraints.
Design Brain — private art direction.
Concept boards, preview payloads, and inline reviews — selection stages.
Logo, logo prompt enhancer, and logo export payloads — SVG generation and deterministic variants.
Palette and typography — foundation slots.
Mockups, social graphics, posters/banners, packaging, signage, and merchandise — applications.
Presentation decks and Brandbooks — editable documents.
QA and iteration — preflight, repair, approval, and delivery manifest.
Files
- ATTRIBUTION.mdAdded by AutoGineer878 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md12.4 KiB
- agents/openai.yaml231 B
- references/asset-analysis.md7.5 KiB
- references/brand-lock.md6.1 KiB
- references/brandbook.md4.8 KiB
- references/brandkit-design-brain.md8.0 KiB
- references/concept-boards.md7.1 KiB
- references/handoff.md3.7 KiB
- references/inline-widgets.md2.7 KiB
- references/intake.md3.3 KiB
- references/logo-export-payloads.md709 B
- references/logo-prompt-enhancer.md9.5 KiB
- references/logo.md10.1 KiB
- references/merchandise.md1.0 KiB
- references/mockups.md10.9 KiB
- references/packaging.md1.3 KiB
- references/palette.md1.0 KiB
- references/posters-banners.md1.1 KiB
- references/prerequisites.md2.9 KiB
- references/presentation-deck.md1.6 KiB
- references/preview-payloads.md2.9 KiB
- references/qa-and-iteration.md6.8 KiB
- references/signage.md1001 B
- references/social-templates.md4.3 KiB
- references/state-payloads.md3.1 KiB
- references/typography.md4.3 KiB
- scripts/brandkit.pyScript54.8 KiB
- scripts/build_brandbook.pyScript36.9 KiB
- scripts/render_brandbook_pdf.pyScript5.2 KiB
references/concept-boards.md
Essential Kit selection
The visual concept is not a separate abstract direction. It is the combined result of the user's selected palette, logo mark, and typography.
Each generated element remains draft only until the user selects that element. Approved palette, logo, and typography slots persist independently.
Input analysis
Build options from:
Brand/product brief
Audience, positioning, and tone supplied by the user
User preferences and comments
Uploaded references, screenshots, logos, fonts, colors, or existing assets
Invite uploads before starting. Ask what the user specifically likes about each reference: palette, typography, composition, shape language, material, or overall aesthetic. If they give no explanation, use the overall character as inspiration without copying distinctive elements.
Step 1 — Palette options
For every interactive new-identity flow that needs a logo, start here and complete this review before Step 2. Intake phrases such as “pastel,” “blue,” or “warm neutrals” are preferences used to build the options, not a selected palette. Skip this review only when the user supplied an authoritative palette or explicitly selected exact palette colors, or when the user requested auto/no-question mode. In auto/no-question mode, build one exact palette from the brief and call approve_palette before Step 2; continue only after the state script confirms success. Never merely describe that palette as locked.
Create 2–3 considered palette options from the brief and references. Each option must include:
Named flat colors with exact hex values
Background and text colors
Optional gradients only when relevant
One-sentence rationale
Small usage examples
2–3 rough logo mechanism seeds showing what the palette could support
logo_ideas is mandatory for every palette option. The preview script rejects a palette review that omits them.
Render all options in editable HTML with python3 "$SKILL_ROOT/scripts/brandkit.py" preview, then show them per inline-widgets.md (local board PNG screenshots plus editable HTML paths). Do not generate image-model duplicates.
Every palette review object must set stage: "palette". Omit display_font, body_font, logo_svg, headline, and body; logo_ideas must contain 2–3 entries. The palette board must not display a typography specimen, placeholder font pairing, or wordmark. Typography appears only after the user selects a logo.
Load inline-widgets.md. Screenshot every returned board, inspect it, show each option's PNG inline when supported, and provide the absolute HTML path. For 2–3 options, stack the complete option blocks vertically in one message.
After showing the assets, send a normal assistant message:
Take your time reviewing the palettes. Reply with the option you prefer and any colors you want changed, added, or removed.
Do not use a structured question tool or a multiple-choice list. Wait for the user's ordinary next message.
When the user selects a palette, immediately call the Brandkit state script's approve_palette action with that exact palette and the selection message as approval evidence. Continue only if the original request needs another missing slot or output.
Step 2 — Logo mark options
Enter this step only after Step 1 produced a user-selected and persisted palette, or after an authoritative/explicitly selected palette was already available. Never treat general color preferences from intake as palette selection.
For this entire internal sequence, send at most one short user-facing status: “I’m generating three logo options now.” After it, send no other process/status text until the finished review. Do not mention Design Brain, mechanisms, enhancer prompts, model lookup, Recraft parameters, or intermediate validation.
Using the selected draft palette plus Brandkit Design Brain:
Design Brain returns exactly three original logo mechanisms.
Apply
logo-prompt-enhancer.mdonce per mechanism to produce its enhanced prompt.Pass each enhanced prompt separately to
higgsfield generate create recraft_v4_1 --model_type vector --wait --json, with the selected logo color subset in--colorsand selected background in--background_color. Use the one, two, or three logo colors the concept needs; never pad to three. Exceed three only when the user explicitly requested more.Use Recraft's returned SVG URLs directly. Do not pass them through the HTML preview script.
Show all three per the logo review in
inline-widgets.mdwith direct SVG links. This review is mandatory in interactive and explicit auto/no-question modes; bare SVG URLs are not a review.
Ask the user to review and comment. In explicit no-question mode, show the same review without an additional questionnaire, but still stop for the user's logo selection. Do not self-select, treat your own visual assessment as approval, or start typography before the user responds.
When the user selects a logo, run logo-inspect --source <selected SVG URL or absolute path>, then immediately call the Brandkit state script's approve_logo action with the exact Recraft SVG asset, name, returned canonical fingerprint, and any explicitly requested variants. A logo-only request is complete at this point; typography is not mandatory.
Step 3 — Typography options
Using Design Brain with the selected draft palette and selected Recraft SVG:
Propose 2–3 suitable font pairs.
Use user-uploaded licensed fonts when supplied; otherwise use verified Google Fonts and include official download links.
Render the real brand name and short sample copy through
python3 "$SKILL_ROOT/scripts/brandkit.py" preview.Show the selected mark next to each wordmark treatment so type/mark cohesion is visible.
Use one contrasting
text_colorfor both display and body samples; it must not equal the background color.Return a visible preview and downloadable HTML. Present both through the typography review in
inline-widgets.md.
Every typography review object must set stage: "typography" and include display_font, body_font, logo_svg, and one shared contrasting text_color.
Ask for feedback in a normal message and wait. Do not use a choice list.
When the user selects a type pair, immediately call the Brandkit state script's approve_typography action with the exact approved font metadata and specimen.
Optional combined Essential Kit HTML
Render a combined board only when all three slots are approved and the user requested a full identity/Brandkit or asks to see them together. Otherwise show the available individual review boards. Use python3 "$SKILL_ROOT/scripts/brandkit.py" preview with one stage: "essential" review containing the approved logo, palette, and type slots.
Publish the returned HTML board per inline-widgets.md (inline PNG + full-screen/editable HTML links). This board is a presentation view, not another approval gate. Do not ask the user to approve the system again.
Continue the original request
Once the slots required by the first-message deliverables are approved, load those output modules and continue. Never force missing unrelated slots or ask the user to choose scope again.
Requirements and compatibility
Written for: Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Claude Code frontmatter, Needs network access, Needs Python
Security
What our scanner noticed· Medium risk
- Instructions that use the network
- Downloads piped into a shell
- Scripts that your agent may run
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Attribution and licence
- Published by
- Higgsfield AI
- Publisher
- Higgsfield AI (Official)
- Source
- higgsfield-ai/skills · higgsfield-brandkit · commit 2700040
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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