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/mockups.md
Mockups
Create believable applications of the Brand Lock. Preserve the approved logo and colors; do not let the scene generator invent branding.
Before planning, require only an approved logo through the Brandkit state script's get_logo action. Read approved palette/visual axes when available or when color/application decisions need them. Require typography only when readable text must appear. Use the exact approved logo path, URL, upload ID, or job ID returned by state everywhere; never substitute a newer generation or a recreated mark.
Mockups are rendered images, not fully editable layered documents. If the user needs editable source artwork, use that asset's dedicated reference (for example packaging.md or social-templates.md) first.
Plan the application from brand input
Use the approved Brand Lock, user brief, persisted visual axes, preferences, and uploaded references before choosing mockup objects, materials, framing, or art direction.
Choose applications people in that category credibly use.
Extract useful composition/material cues from user-provided references.
Never recreate a reference's exact scene, layout, or branded object.
Each mockup needs one clear art-directed idea tied to this brand. “Put the logo on a generic object” is not a concept.
Anti-slop rules
Avoid the common synthetic/generic look:
Arbitrary gradients or neon glows unrelated to the Brand Lock
Plastic sheen on every material
Floating products and physically meaningless props
Excessive bloom, haze, depth of field, or cinematic lighting
Generic marble/pedestal “luxury” staging
Fake microcopy, pseudo-labels, invented claims, or decorative UI
Impossible folds, embossing, reflections, print edges, or scale
Too many props competing with the branded surface
Warped logos or a different visual device in every mockup
Prefer believable materials, restrained lighting, purposeful negative space, specific environments, and one focal branded application.
Required Seedream route
Use Seedream as the primary mockup generator. Soul models are forbidden. GPT Image 2 is allowed only as the conditional text/detail application stage below.
Before generation, run higgsfield model get seedream_v5_pro --json. If that id is absent, inspect higgsfield model list --image --json and use the highest/newest Seedream tier it returns. Follow the live schema; request the highest supported resolution/quality tier and use the same Seedream model for every mockup in the set.
Ask for aspect ratio
Before submitting any mockup job, ask which ratio the user wants unless their current request already states it. Offer supported choices such as 1:1, 4:3, 3:4, 16:9, 9:16, 3:2, and 2:3; validate the answer against the live model schema. For several mockups, use one ratio for the set unless the user assigns ratios per item. Lock the selected ratio across every stage.
Existing photograph
When the user explicitly supplies the exact photograph to mock up, pass it as Image0 and the selected logo variant as Image1. Use Seedream for symbol-only applications; use GPT Image 2 when the final image contains readable text. Preserve subject, camera, lighting, materials, folds, shadows, perspective, crop, background, and selected ratio.
Logo variant routing
Call the Brandkit state script's get_logo action and use one of its exact approved variant assets:
Full-color logo: smooth surfaces and production methods that credibly support accurate multicolor printing.
Black monochrome logo: light kraft paper, natural cardboard, pale fabric, stamps, dark-ink screen printing, engraving masks, and light uncoated stock.
White monochrome logo: dark paper, dark boxes, dark fabric, reverse marks, light-ink screen printing, and dark signage.
Embossing, debossing, foil, laser engraving, and one-color printing always use a monochrome variant. Never send the full-color mark as the application reference for those processes.
Only after the user confirms a mockup whose physical production requires one-color/reverse artwork, run python3 "$SKILL_ROOT/scripts/brandkit.py" logo-export with include_monochrome: true and the approved color SVG when the required variant is absent. Never pre-generate monochrome assets for future mockups. Never ask Seedream to invent/recolor them or use manual SVG edits.
Seedream prompt contract
Pass references with repeated CLI --image flags; Image0 is the first flag, Image1 the second, and so on. For a new scene, pass the exported PNG of the selected logo variant as Image0; add approved product/artwork references afterward. For an existing photograph, use the photograph as Image0 and logo PNG as Image1. State each role explicitly. Local PNG/JPG paths auto-upload. Never pass an SVG path as an image reference.
[CREATE ONE FINISHED BRANDED MOCKUP]
<specific object/application, credible setting, camera, material, lighting,
composition, and one brand-specific art-direction idea>
[AUTHORITATIVE LOGO]
<ImageN> is the exact approved <full-color/black/white> logo. Preserve its
spelling, silhouette, geometry, proportions, internal negative space, and exact
color. Do not redraw, simplify, crop, stretch, outline, or add effects.
[PLACEMENT LOCK]
Target surface: <exact object panel/face/material>.
Position: <exact alignment and location, e.g. horizontally centered, upper
third, optical center aligned to panel>.
Scale: logo occupies <specific proportion> of the target surface while keeping
<specific clear-space margin>.
Orientation: align to <panel edge/seam/baseline>; follow surface perspective
without changing logo proportions.
Color: use the supplied <full-color/black/white> variant exactly. State why it
contrasts correctly with the material/background.
[PHYSICAL APPLICATION]
Render the logo using <credible print/emboss/foil/engraving/ink behavior>.
Respect folds, grain, perspective, occlusion, reflections, scale, and
manufacturing limits.
No extra logos, pseudo-text, invented labels, warped marks, floating print,
unrelated props, arbitrary gradients, plastic sheen, or generic luxury staging.The prompt must contain concrete placement, scale, alignment, clear-space, color-variant, and material-application instructions. “Place the logo on the bag/box” is insufficient.
Use --wait --json and retain the final job ID and result URL. Do not download and re-upload the same result unless a receiving stage requires a local file.
Typical one-stage call:
higgsfield generate create seedream_v5_pro \
--image "$BRANDKIT_WORKDIR/logo/approved-logo-2048.png" \
--prompt "<complete Seedream prompt contract>" \
--aspect_ratio 3:4 \
--resolution 2k \
--wait --jsonReplace ratio and resolution only with values confirmed by the live schema.
Conditional text/detail route
If the final mockup contains any readable text—wordmark, brand name, tagline, packaging label, signage, product copy, or interface text—do not ask Seedream to render it:
Seedream creates the same-ratio scene with the target surface blank and no logo, letters, pseudo-text, or invented graphics.
Submit GPT Image 2 with the Seedream job ID as the first
--imageand approved logo/artwork PNG as the second--image.The GPT prompt preserves Image0's camera, crop, objects, lighting, material, folds, shadows, perspective, and background exactly.
State the exact literal text, logo variant, placement, scale, alignment, clear space, color, and physical print/application behavior.
Keep the ratio identical to the user-approved ratio.
higgsfield generate create gpt_image_2 \
--image "<seedream-job-id>" \
--image "$BRANDKIT_WORKDIR/logo/approved-logo-2048.png" \
--prompt "<exact controlled text/detail application prompt>" \
--aspect_ratio 3:4 \
--resolution 4k \
--wait --jsonUse GPT Image 2 only for this controlled second stage. If there is no readable text, keep the one-call Seedream route.
Deterministic compositing
Prefer deterministic placement over generative editing when:
The target is a flat poster, screen, card, sign, or front-facing package
The source logo already has transparency
No physical deformation, folds, reflections, or occlusion are required
Use image/SVG tooling to scale and place the official logo exactly. Preserve clear space and color. Add masks/perspective only when they can be controlled reliably.
Use Seedream directly when the branding must interact with:
Fabric folds
Curved packaging
Embossing/debossing
Foil, print texture, reflections, or surface wear
Occlusion and realistic perspective
If Seedream corrupts the logo, retry once with stronger placement, geometry, and color constraints while keeping the same references. If it fails again, stop and use deterministic compositing when possible.
Mockup-specific guidance
Packaging
Use the actual dieline/package proportions when supplied.
Preserve material, closure, label area, and required legal/copy regions.
Do not invent claims, ingredients, certifications, or regulatory text.
Apparel/merch
Use Seedream for the finished branded person/garment mockup.
Define print/embroidery location, size, and material behavior.
Preserve the person and garment between variants.
Signage/environment
Respect viewing distance, perspective, mounting, and lighting.
Use the correct approved logo version for background contrast.
Device/screen
Treat the screen graphic as a separate editable asset from its dedicated module when possible, then composite it into the device.
Do not ask GPT to invent interface copy that should be exact.
Variant discipline
For several mockups:
Lock one base scene per mockup family.
Vary only the requested application or colorway.
Keep camera, lighting, material, and composition fixed for comparison sets.
Do not generate a new person or environment for every colorway.
Mockup QA
Official logo matches the reference exactly
No misspelling, extra glyph, warped geometry, or invented mark
Correct colorway and sufficient contrast
Physical application follows folds/perspective/material
No floating print, impossible reflections, or duplicated graphics
One brand-specific art-directed idea; not a generic logo-on-object scene
Materials, props, lighting, and setting are credible for the industry
No arbitrary gradients, plastic sheen, bloom, fake microcopy, or generic luxury staging
When an existing photograph was supplied, every non-target scene element is unchanged
Product/package proportions match supplied references
All mockups use the same Brand Lock
Rendered output is clearly labeled as non-editable unless a separate editable overlay/template is also delivered
After QA, ask the user to approve the final mockup or set. Only then save it with the Brandkit state script's approve_brandbook_element action and required_slots: ["logo"]; add palette/typography only when the mockup actually used them. Generated or model-praised mockups are drafts until that approval.
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
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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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Download ZIP
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