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://mp-staging.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/asset-analysis.md
Precise asset analysis
Analyze supplied assets before concepting or generation. The goal is a usable visual specification, not an impressionistic description.
1. Inventory and assign roles
Inspect every attachment and local path supplied in the conversation. If the user asks to reuse prior Higgsfield uploads, run higgsfield upload list --json. Build an inventory with:
id | filename/source | format | dimensions/pages | likely role | authorityUse one role per asset:
official-logologo-variantfont-filepalettebrandbooktemplaterepresentative-visualstyle-referencecontent-onlyunknown
Ask the user only when confusing two roles would change the output—for example, an official logo versus a visual reference. Never treat inspiration as an official asset without confirmation.
After confirming authority, immediately persist each official Essential Kit slot with lock_authoritative_logo, lock_authoritative_palette, or lock_authoritative_typography. Preserve the exact local path, URL, upload ID, or job ID plus the source summary and user_supplied origin. Do not wait for generated missing elements.
Inspirational references
For every style-reference or representative-visual, separate reusable taste signals from protected/distinctive design:
Reference:
User specifically likes:
Palette behavior:
Typography character:
Composition/grid:
Shape/material language:
Overall mood:
Do not copy:Ask what specifically inspires the user when they have not said. Offer palette, typography, composition, shape language, material, and overall aesthetic plus a free-text option.
If they do not answer, use the overall aesthetic/design-system character as a taste signal. Do not reproduce the reference's logo, distinctive layout, illustration, pattern, or artwork, and do not pass it into logo generation.
2. Evidence labels
Attach one evidence label to every extracted rule:
source-declared — explicit in SVG/CSS/PPTX/PDF/font metadata or written brand guidelines.
measured — deterministically calculated from source pixels or geometry.
visually-observed — clear to visual analysis but not available as source data.
inferred — a plausible interpretation or recommendation.
Keep exact values and inferred matches separate. Never call an inferred font, radius, grid, or color role exact.
3. Source precedence
Resolve conflicts in this order:
User's explicit correction
Official editable source or font file
Written brandbook rule
Repeated value across multiple official assets
Deterministic measurement
Visual observation
Inference
Record conflicts instead of averaging them away. A social campaign may intentionally use a different treatment from the core identity.
4. Raster image analysis
Read images semantically and compositionally yourself when your client shows them; analyze several related images together to detect repeated rules, chunking large sets into manageable groups.
For deterministic measurement, use the local file directly or download the user-authorized hosted asset into "$BRANDKIT_WORKDIR/assets". Use the installed tooling (ImageMagick, Python/Pillow) to measure:
Pixel dimensions, color mode, alpha, and resolution metadata
Dominant and repeated colors, with exact sampled hex values
Background versus foreground color candidates
Logo bounding box and clear-space ratios
Margins, gutters, column alignment, and repeated spacing
Border thickness and approximate corner radius
Text-block positions, alignment, scale relationships, and line counts
Repeated motif size, density, and rotation
Image crop, focal point, balance, and negative-space distribution
Download only the user's own Higgsfield media URLs or user-supplied public URLs; never fetch unrelated third-party content. Document URLs use the dedicated document route below.
For anti-aliased pixels, cluster near-identical colors rather than reporting hundreds of false palette entries. Ignore photographic colors when extracting the graphic palette unless the image clearly uses them as deliberate overlays or surfaces.
Font recognition from pixels is never exact. Describe anatomy first (grotesk/humanist/geometric/serif, width, contrast, terminals, x-height, weight), then list possible matches with confidence.
5. SVG, CSS, and token files
Parse source directly before rendering:
viewBox, width, height, and aspect ratioFill/stroke colors and opacity
Stroke widths, line caps, and joins
Paths, groups, transforms, masks, and clipping
Repeated geometry and spacing
font-family, weight, style, letter spacing, and text anchorsCSS variables and semantic token names
Corner radii, shadows, borders, and gradients
Render a preview and compare it with the source parse. Preserve the original file as the authoritative logo whenever possible; do not regenerate it.
6. PDF and PPTX
Analyze documents locally. Use a supplied local path directly, or download a user-authorized document URL into "$BRANDKIT_WORKDIR/assets", then inspect it with installed tooling.
For .pptx:
Unpack the OOXML (
unzip) when exact theme/font/shape data mattersRender slide thumbnails with headless LibreOffice (
soffice --headless --convert-to pdf, thenpdftoppm) for visual reading
Capture slide size, master/layout structure, theme colors, font families, weights, positions, shape geometry, borders, fills, radii, image crops, alignment, and recurring page types.
For PDF, use the Poppler tools:
pdftotextfor declared rules/copypdffontsfor embedded font familiespdftoppm/pdfimagesfor page/image evidence of visual hierarchy, palette, spacing, and composition; inspect the rendered pages locally
If the document cannot be parsed locally (corrupt, encrypted, or scanned without OCR value), report that and ask for page images or source files instead of hanging or improvising.
PDF geometry may be flattened or outlined. Mark recovered text/font data as source-declared only when the file exposes it; otherwise use measured or visually-observed.
7. Font files
For .otf, .ttf, .woff, or .woff2, inspect with available font metadata tools such as fc-scan or FontTools:
Family and style names
Weight and width classes
Italic/oblique status
Variable axes
Character/language coverage
License/name table metadata when present
Do not make a legal conclusion from metadata. Do not redistribute the file. For editable outputs, note that the recipient must install the font; PPTX does not reliably embed custom fonts.
8. Analyze the system, not only individual assets
After per-asset analysis, build a consistency matrix:
Property | Core/official rule | Repeated variants | Exceptions | ConfidenceCover:
Logo usage and exclusion zone
Palette and role frequency
Type roles and hierarchy
Grid, margins, gutters, and alignment
Border, corner-radius, and shadow language
Composition and focal hierarchy
Shapes, motifs, patterns, and textures
Photography/render treatment only when it appears in supplied materials
Format-specific exceptions
9. Output contract
Return analysis in this order:
Authoritative assets
Measured visual tokens
Typography findings
Layout and composition rules
Graphic devices
Format-specific exceptions
Unknowns/conflicts
Safe extension rules
Feed these findings into references/brand-lock.md. Do not generate until the lock distinguishes what must be preserved from what may be proposed.
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
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
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
AutoGineer is not affiliated with or endorsed by Higgsfield AI.
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