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/qa-and-iteration.md
QA and iteration
Assume the first pass contains inconsistencies. Validate the set as a system and each asset as an individual deliverable.
1. Preflight before paid generation
The Brandkit state script's
get_statusaction was called in this turnOnly the state slots required by this output were loaded
Required approved slots exist for this output
Every logo use points to the exact approved state asset ID
Brand Lock exists and matches state revisions
Requested output list, quantity, format, and dimensions are explicit
Exact user copy is captured
Authoritative logo/reference has one reusable ID
Palette and typography decisions are resolved
Editable versus rendered deliverables are distinguished
Every generation call has a defined downstream purpose
Do not generate speculative extras. Do not treat todo completion, model preference, your own visual assessment, or generation success as user approval.
2. Set-level consistency matrix
Review all outputs together:
Asset | Logo | Palette | Type | Grid/spacing | Shape/device | Format | StatusAn asset passes only when it follows the same Brand Lock. Format-specific overrides must already be documented under applications; an unexplained difference is drift.
Check:
Exact brand/product spelling
Exact logo geometry and approved colorway
Palette role consistency, not only approximate hue
Approved display/body font pair, exact copy, and rendering fidelity
Grid, outer margin, gutters, and alignment
Borders, corner radii, shadows, shapes, and motif
Density, hierarchy, and composition
Exact copy and CTA
Dimensions/aspect ratio
Contrast and legibility
3. Evidence-based review
Review the outputs visually yourself (when your client shows them) for semantic checks:
What appears inconsistent?
Is the logo distorted?
Does hierarchy match the intended concept?
Do the assets feel like one system?
Use deterministic checks for measurable properties:
Image/page dimensions
Exact colors in editable sources
Text contents
Font references
Element positions and sizes
SVG attributes
PPTX object structure
Repeat seams
File existence and output size
Do not accept a vision model's statement as proof of exact hex, font, spacing, or radius.
4. Module-specific checks
Logo
Compare against authoritative source
New-logo selection contains exactly three editable SVGs
All three use identical Recraft model, palette, background, aspect, and quality parameters
Candidate prompts contain no text except explicitly requested monogram initials
Selected mark and later wordmark are optically balanced as one lockup
Check geometry, proportions, clear space, and small-size behavior
Typography
Verify the one approved font pair, source files, and Google Fonts links
Confirm the sample was rendered with the real fonts and approved colors
Check missing glyphs and required language coverage
Confirm recipient installation requirements
Editable templates
Render PPTX/SVG/HTML to previews
Check overflow, clipping, collisions, crop, contrast, and safe margins
Confirm text, logo, shapes, and image placeholders are separate editable objects
Search for placeholder/lorem text that should not ship
Re-render after fixes; do not declare success after an uninspected first pass
Mockups
Compare the Seedream result with every scene/product/logo reference
Confirm every output uses the user-approved aspect ratio
For text-bearing mockups, compare GPT Image 2 output with the exact Seedream base and verify only the controlled branding/text application changed
Verify placement, scale, alignment, clear space, color variant, and material application match the prompt exactly
When an existing photograph was supplied, verify only requested branded surfaces changed
Verify the selected color/black/white logo variant matches the material and production method
Check physical perspective, folds, occlusion, print, and reflections
Reject any corrupted or approximate logo
Confirm one specific art-directed idea tied to the brand/category
Reject generic marble/pedestal luxury scenes, arbitrary gradients, plastic sheen, excessive bloom, floating objects, meaningless props, and fake copy
Compare material/composition craft with the approved Brand Lock and supplied references without copying a specific reference
Social media graphics
Verify exact copy, spelling, punctuation, and requested line breaks
Verify platform/aspect ratio and requested output count
Compare logo geometry/color with the approved variant
Compare typography character with the approved specimen and font roles
Reject pseudo-text, invented copy/CTA, extra logos, or palette drift
For mockup-photo mode, confirm the base photo changed only on the target surface
5. Repair strategy
When an item fails:
Name the failed Brand Lock rule.
Decide whether the issue is generative or deterministic.
Keep the Brand Lock and all passing assets unchanged.
Regenerate/recompose only the failing asset.
Re-run its module checks and the set-level matrix.
Prefer a small deterministic correction over a full regeneration:
Re-typeset incorrect copy
Re-place the exact logo
Correct a color token
Resize/re-align an object
Replace one generated background
If the user asks to change a core rule, update the Brand Lock version first, list affected assets, and revise only those assets after confirmation.
When logo, typography, or palette approval changes, remove every invalidated downstream element from the active deliverable set. Rebuild and re-approve those elements; never silently keep assets tied to older revisions.
Changing the palette also discards a generated logo because its candidate generation used that palette. Changing typography does not invalidate the symbol mark; it invalidates typography-dependent lockups, templates, decks, and Brandbooks only.
6. Stop conditions
Stop and disclose a limitation when:
A generated logo cannot be reproduced faithfully as editable vector
An image model repeatedly corrupts the official logo
A custom font is unavailable or cannot be embedded
The requested native editor format is unsupported
Source files are too flattened to recover exact rules
Required copy, dimensions, or authoritative asset identity remains ambiguous
Do not hide these limitations behind a flattened preview.
7. Delivery manifest
Return:
Brand Lock version:
Concept:
Editable files:
Preview/final files:
Generated assets:
Required fonts:
Known limitations:
Variant names:Use stable descriptive names such as:
brand-carousel-4x5-v1.pptx
brand-carousel-4x5-v1-preview-01.png
brand-poster-a2-v1.svg
brand-mockup-tote-primary-v1.pngThe user should be able to request "revise carousel card 3" without rerunning unrelated work.
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.
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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
AutoGineer is not affiliated with or endorsed by Higgsfield AI.
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