Best for
- User wants an image or video generated by AI — any model, any style
- "animate this photo", "make a video from an image", "img2vid"
- "create a UGC ad", "Marketing Studio video", "brand video with avatar"
MoizIbnYousaf/marketing-cli/skills/higgsfield-generate/SKILL.md
Use when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "
Decision brief
Submit jobs to any Higgsfield model. Wraps the higgsfield CLI. Covers generic image/video gen and Marketing Studio (branded ads, avatars, products).
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/MoizIbnYousaf/marketing-cli --skill "skills/higgsfield-generate"Inspect the Agent Skill "higgsfield-generate" from https://github.com/MoizIbnYousaf/marketing-cli/blob/f12fbcbe4929584697b309b9096c9427b0cfce8e/skills/higgsfield-generate/SKILL.md at commit f12fbcbe4929584697b309b9096c9427b0cfce8e. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Before any other command, make sure the CLI is installed and authenticated:
1. Pick a model. Practical defaults from production use:
1. Get product. - URL → higgsfield marketing-studio products fetch --url --wait (polls until import done) - Local images → higgsfield upload create ... then higgsfield marketing-studio products create --title "..." --image ... Capture product id. 2. Pick avatar. - Default: higgs…
Same as above but use marketingstudioimage model:
Route elsewhere if: - User wants to train a reusable face identity → higgsfield-soul-id - User wants a mode-enhanced product photoshoot → higgsfield-product-photoshoot - User just needs a one-off image without Higgsfield → image-gen (Gemini, free tier)
Permission review
The documentation includes network, browsing, or remote request actions.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | shThe documentation includes network, browsing, or remote request actions.
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | shEvidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 98/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 27 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Submit jobs to any Higgsfield model. Wraps the higgsfield CLI. Covers generic image/video gen and Marketing Studio (branded ads, avatars, products).
Route elsewhere if:
higgsfield-soul-idhiggsfield-product-photoshootimage-gen (Gemini, free tier)brand/voice-profile.md, brand/visual-style.md, and brand/creative-kit.md if present. Use brand context to inform model choice and prompt style where relevant. All files are optional — zero-context operation is fine.higgsfield account status. If not on $PATH, surface install command (see Optional dependency section). If session expired, prompt auth.This skill requires the @higgsfield/cli binary and a Higgsfield account.
Without the CLI installed, return a clear actionable error:
higgsfield CLI not found. Install with:
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
Then authenticate:
higgsfield auth login
Without an authed Higgsfield account, the CLI itself surfaces the auth prompt — no special handling needed in the skill.
Fallback for image generation only: if the user just needs a one-off image and doesn't have a Higgsfield account, route them to image-gen (Gemini, model gemini-3.1-flash-image-preview, free tier). Video generation, Marketing Studio, Soul Characters, and product modes have no fallback — they require Higgsfield.
Before any other command, make sure the CLI is installed and authenticated:
higgsfield is not on $PATH, install it:
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
higgsfield account status fails with Session expired / Not authenticated, ask the user to run higgsfield auth login (interactive, opens a browser) and wait for them to confirm before continuing.Skip both checks if higgsfield account status already prints account info.
--aspect_ratio 16:9) stay English.--wait to generate create so the command blocks until done and prints the result URL itself. Avoid the two-step create → wait pattern.Pick a model. Practical defaults from production use:
Image:
higgsfield-product-photoshoot instead. NOT this skill.higgsfield-soul-id) → Soul 2.0 for stills, Soul Cinema for cinematicVideo:
For the actual --model ID to pass to higgsfield generate create, run higgsfield model list --json | jq to map display names to IDs. See references/model-catalog.md for the full table.
Pass media inputs straight to flags. Media flags accept a local file path or a UUID. CLI auto-uploads paths and auto-detects job vs upload for UUIDs. No need to pre-upload. Each model declares accepted roles (image, start_image, end_image, video, audio) — see references/media-inputs.md.
Validate quickly. If unsure of params, run higgsfield model get <jst> --json once and pass only what's needed. Use schema defaults otherwise. The server returns adjustments for non-fatal coercions and a structured error for invalid declared-param values.
Submit and wait in one shot. higgsfield generate create <jst> --prompt "..." [media flags] [param flags] --wait. Blocks until terminal status and prints the result URL on stdout. Tunables: --wait-timeout 20m (default 10m), --wait-interval 5s (default 3s).
Deliver. Send the URL plus a one-line summary (model, duration if video).
To inspect or rerun later, higgsfield generate list --json and higgsfield generate get <id> --json work for retrospection. higgsfield generate wait <id> is still available if you ever need to rejoin a job started without --wait.
| Flag | Use for | Models that accept it |
|---|---|---|
--image <path-or-id> | reference image | most image models, seedance_2_0, veo3, marketing_studio_video |
--start-image <path-or-id> | first frame for image-to-video transitions | kling3_0, kling2_6, veo3_1, seedance_2_0, marketing_studio_video |
--end-image <path-or-id> | last frame for transitions | kling3_0, seedance_2_0, marketing_studio_video |
--video <path-or-id> | reference video | seedance_2_0 |
--audio <path-or-id> | reference audio (lipsync, soundtrack match) | seedance_2_0 (use this, NOT --generate-audio) |
Each flag accepts either a local file path (auto-uploaded) or a UUID. See references/media-inputs.md for the full table.
Flags pass through to model schema. Use higgsfield model get <jst> to discover.
higgsfield generate create gpt_image_2 --prompt "neon city at dusk" --aspect_ratio 16:9 --resolution 2k --wait
higgsfield generate create nano_banana_2 --prompt "anime character concept, expressive pose" --image ./ref.png --wait
higgsfield generate create seedance_2_0 --prompt "camera dollies in" --start-image ./first.png --duration 8 --wait
higgsfield generate create text2image_soul_v2 --prompt "..." --soul-id <soul_ref_id> --wait
For machine-readable output, add --json. With --wait --json you get the final job object array.
Stdin prompt: echo "..." | higgsfield generate create z_image --wait.
Branded image/video gen: avatars + products + ad-style modes. Use models marketing_studio_video and marketing_studio_image.
preset (browse higgsfield marketing-studio avatars list) or custom (uploaded photos via higgsfield marketing-studio avatars create).higgsfield marketing-studio products fetch --url ...) or created from uploaded images (higgsfield marketing-studio products create).higgsfield marketing-studio products fetch --url <url> --wait (polls until import done)higgsfield upload create <photo>... then higgsfield marketing-studio products create --title "..." --image <id>...
Capture product id.higgsfield marketing-studio avatars list and pick a preset matching the brand voice.higgsfield marketing-studio avatars create --name "..." --image <upload_id>.ugc. Other slugs: tutorial, ugc_unboxing, hyper_motion, product_review, tv_spot, wild_card, ugc_virtual_try_on, virtual_try_on. See references/marketing-modes.md.higgsfield generate create marketing_studio_video \
--prompt "..." \
--avatars '[{"id":"<avatar_id>","type":"preset"}]' \
--product_ids '[<product_id>]' \
--mode ugc \
--duration 15 \
--resolution 720p \
--aspect_ratio 9:16 \
--wait
Resolution is 480p or 720p. Aspect ratio: auto/21:9/16:9/4:3/1:1/3:4/9:16. --generate-audio true is supported here. Bump --wait-timeout 30m for longer ad runs.When the user gives a product URL and wants a marketing video in one go:
# 1. Trigger fetch (returns the product id and starts background scrape)
higgsfield marketing-studio products fetch --url https://shop.example.com/sneakers --wait
# 2. Generate the marketing video against the same URL — backend reuses the entity
higgsfield generate create marketing_studio_video \
--url https://shop.example.com/sneakers \
--mode ugc \
--duration 15 \
--aspect_ratio 9:16 \
--wait
Backend dedupes by URL, so repeated runs reuse the existing entity instead of re-fetching.
Same as above but use marketing_studio_image model:
higgsfield generate create marketing_studio_image \
--prompt "..." \
--aspect_ratio 1:1 \
--resolution 2k \
--wait
Missing required params: prompt → user gave no prompt; ask for it.Invalid values: aspect_ratio=99:99 (allowed: ...) → bad enum; pick from allowed.Unknown params: foo → schema doesn't accept that flag; check higgsfield model get <jst>.Session expired → higgsfield auth login.See references/troubleshooting.md for more.
| Anti-pattern | Why it fails | Instead |
|---|---|---|
Using higgsfield-generate for product photoshoots | Bypasses the mode-specific prompt enhancer — output is noticeably lower quality for product brand imagery. | Route to higgsfield-product-photoshoot any time a product + specific output format is involved. |
| Batching all context questions upfront | Users disengage when asked 5 things at once. Model choice and media inputs can often be inferred. | Ask at most one clarifying question before submitting. Pick a sensible default. |
Using --generate-audio with seedance_2_0 | The flag isn't accepted by Seedance — it silently breaks. | Use --audio <path> for Seedance lipsync/soundtrack; use --generate-audio true only for marketing_studio_video. |
| Printing raw job JSON to chat | Dumps internal IDs and noise. Users want a URL. | Always extract and present only the result URL plus a one-line summary. |
| Picking the wrong model then iterating blind | Costs money and time. | Read references/model-catalog.md first, pick the right model once. |
Load on demand:
references/model-catalog.md — picking the right model for the taskreferences/prompt-engineering.md — writing prompts that workreferences/media-inputs.md — image/video reference flowsreferences/troubleshooting.md — common errors and fixesreferences/marketing-avatars.md — preset vs custom avatarsreferences/marketing-products.md — URL fetch vs manual product createreferences/marketing-modes.md — every Marketing Studio modePorted from higgsfield-ai/skills — MIT License, Copyright (c) 2026 Higgsfield AI. Adapted for mktg's drop-in contract on 2026-05-05.
Upstream version: 0.3.0 Upstream commit: 1dcfe2687c3a9092232bac55c2b6b9ae3fc717d7
Drift detection: if the upstream skill changes, re-run mktg-steal https://github.com/higgsfield-ai/skills to evaluate the diff.
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