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/brand-lock.md
Brand Lock
The Brand Lock is the single source of truth for every requested graphic. Create it after intake and asset analysis, before paid generation.
It is not a new brand strategy document. It records supplied context, measured rules, and the minimum proposed visual decisions needed for this job.
Lock states
Mark each field as one of:
fixed— supplied or approved; never change without explicit permission.proposed— fills a missing visual rule; may be revised.not_applicable— intentionally absent.unknown— unresolved and unsafe to invent.
Also attach an evidence label from asset-analysis.md: source-declared, measured, visually-observed, or inferred.
Canonical shape
Use this structure internally. Keep it compact enough to copy relevant blocks verbatim into generation prompts.
{
"version": 1,
"brand_context": {
"name": "",
"offering": "",
"industry": "",
"positioning": "",
"audience": "",
"tone": []
},
"concept": {
"name": "",
"visual_premise": "",
"research_principles": [],
"reference_preferences": [],
"avoid": []
},
"visual_axes": {
"restrained_expressive": 50,
"geometric_organic": 50,
"familiar_experimental": 50
},
"authoritative_assets": {
"logo": {
"origin": "user_supplied | brandkit_generated",
"source": "",
"upload_or_job_id": "",
"variants": {
"color": "",
"black": "",
"white": ""
},
"status": "fixed"
},
"fonts": [],
"references": []
},
"palette": {
"origin": "user_supplied | brandkit_generated",
"primary": [],
"accent": [],
"neutral": [],
"semantic_roles": {},
"forbidden": []
},
"typography": {
"origin": "user_supplied | brandkit_generated",
"display": {},
"body": {},
"fallbacks": [],
"font_links": [],
"specimen_svg": ""
},
"layout": {
"grid": "",
"margins": "",
"spacing_scale": [],
"alignment": "",
"density": ""
},
"shape_language": {
"corner_radii": [],
"borders": [],
"shadows": [],
"forms": []
},
"graphic_devices": {
"motifs": [],
"patterns": [],
"textures": [],
"rules": []
},
"composition": {
"hierarchy": "",
"logo_placement": [],
"image_behavior": "",
"text_behavior": ""
},
"applications": {},
"unknowns": []
}For each exact token, retain its state, evidence, and source when ambiguity exists. Do not bloat every obvious field with metadata.
Required minimum
Before paid generation, the lock must contain the fields that the requested output actually uses:
Exact brand/product spelling
Requested deliverables and formats
Logo source when the output uses a logo, or explicit
not_applicablePalette behavior when the output uses color (exact colors when supplied)
An approved display/body typography system only when readable text appears
Composition/hierarchy and shape rules relevant to the requested asset
At least two concrete avoid rules
If a missing value would materially affect a paid generation, ask once. If it only affects a reversible layout detail, make a proposed decision.
Prompt lock block
For every image-model call, include one compact block:
[BRAND LOCK — DO NOT DEVIATE]
Brand spelling: <exact>
Authoritative logo: <reference index/id and preservation instruction>
Palette: <exact hex + role>
Typography: <exact font/style when rendered>
Layout: <grid/alignment/spacing>
Shape language: <radius/border/forms>
Graphic device: <motif/pattern rule>
Must preserve: <fixed invariants>
Never: <forbidden treatments>Repeat the block verbatim across related assets. Change only the asset-specific content, dimensions, and composition section.
Reference discipline
Keep one authoritative absolute local path or remote ID and reuse it everywhere.
Reference a prior generated output by its original job ID; do not download and re-upload it unless the receiving tool requires a file.
Label references by role in prompts:
Image 1: official logo,Image 2: approved base scene.State what each reference controls and what it must not control.
For an existing logo, require exact preservation of spelling, geometry, proportions, and colors. Prefer deterministic placement/compositing when the logo does not need to interact physically with the scene.
Per-format overrides
Store format differences under applications, not by changing core tokens. Examples:
A carousel may use tighter spacing than a deck.
A poster may use the display font at extreme scale.
A light kraft/paper/cardboard mockup uses the approved black logo.
A dark mockup uses the approved white reverse logo.
Embossing, debossing, foil, stamps, engraving, and one-color printing use an approved monochrome logo, never the full-color mark.
An override must name its format and cannot contradict a fixed core rule.
Project-local approval persistence
The working Brand Lock may contain draft concept language, but final approvals live only in the local "$BRANDKIT_STATE" file.
Call the Brandkit state script's
get_statusaction at the start of every Brandkit turn, then load only the module needed by the active stage.Lock user-supplied official slots immediately with the matching
lock_authoritative_*action and persist normalized axes withset_visual_axes.Save each generated selection immediately through
approve_logo,approve_palette, orapprove_typography.Save final downstream elements only through
approve_brandbook_elementafter user approval, with the exactrequired_slotsused.Do not duplicate Brandkit approval state in user memory, chat prose, or another state file.
Do not infer approval from generated recency, todo completion, model ranking, or your own visual assessment.
When the user revises one rule:
Save only the revised approved slot through the Brandkit state script.
Keep every unrelated approved slot unchanged.
Respect the returned list of invalidated downstream elements.
Identify actual dependents from their
required_slots.Regenerate/recompose only affected outputs and request approval 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
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- Downloads piped into a shell
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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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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download higgsfield-brandkit.zip