About higgsfield
higgsfield is a creative-generation skill for Higgsfield subscribers, connecting to image and video models through one authenticated endpoint. It covers model discovery, image generation, and video generation, including image-to-video chains.
Key features
- 01Explore available models and roles
- 02Generate single or batched images
- 03Generate video, including image-to-video
- 04Reuse uploaded media and prior outputs
Use cases
- 01Check model capabilities and accepted input roles
- 02Create image assets in batches
- 03Turn a still image into a video
- 04Run multiple generation jobs in parallel
MCP support
It connects to the Higgsfield MCP server using the subscriber account and provides tools for model exploration, media upload, and image and video generation.
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ robonuggets/ higgsfield-skill --skill higgsfieldAfter installing, type/higgsfieldin Claude Code to confirm it loaded.
Details
From the project READMEHiggsfield MCP
One connector, 30+ models. Subscription-billed in credits — no per-provider API keys.
When to use this skill
Only use this skill if the user already pays for a Higgsfield subscription. The whole value is access to 30+ models behind one auth, billed against credits they're already paying for. Don't push a non-subscriber to subscribe for one project.
Three spokes
The skill does three things — keep it scoped to these:
- Explore models — what's available, what role each model accepts
- Generate image — single or batched
- Generate video — including image→video chains
Install
Two ways to connect. Both end at the same place: an authenticated MCP your session can call.
Terminal (Claude Code):
claude mcp add --transport http --scope user higgsfield https://mcp.higgsfield.ai/mcp
Then run /mcp in your session and complete OAuth with the Higgsfield account that owns the subscription.
Claude Desktop app:
Settings → Connectors → Add custom connector → paste https://mcp.higgsfield.ai/mcp → sign in.
After auth — mandatory step:
select_workspace
Generation calls fail silently until a workspace is selected. Always run this once at the start of a session.
Heads-up: MCP tools may not appear in the running session after a fresh install — restart Claude Code / Desktop if /mcp shows higgsfield connected but no tools are visible.
No API keys to manage. No .env. The MCP handles auth.
ALWAYS quote credit cost before generating
Non-negotiable. Before any generate_image or generate_video call, quote:
- Model + settings
- Estimated credit cost (use the table below)
- Current
balance - Balance after the call
Wait for explicit "go" before firing. Credits ≠ free, and silent burn is the #1 way to ruin trust. Auto mode does not override this.
Cost table (measured, not from docs)
models_explore does NOT return credit costs — they're not in the API. Use this baked table:
| Model | Settings | Credits |
|---|---|---|
gpt_image_2 | 2K medium | ~3 |
nano_banana_2 | 2K | ~11 |
seedance_1_5 | 480p, 4s | ~2.4 |
seedance_1_5 | 480p, 12s | ~7.2 |
| Video at higher tiers | — | TBD (measure first run) |
Numbers will drift. Verify in the Higgsfield dashboard if a run feels off. Update this table when you measure new ones.
Plan-tier silently gates models
Higher-end models (e.g. seedance_2_0) are blocked on Starter and return a generic "Something went wrong" — no helpful error. If a model errors mysteriously, suspect plan gating before debugging the prompt. Suggest checking balance to infer the user's tier.
Workflow mechanics
Upload is 3 steps, not one. Wrap as a single helper if calling repeatedly:
media_upload→ returns a presigned PUT URL +media_idcurl -X PUTthe file bytes to that URLmedia_confirm({ media_id })→ marks it ready
Generation is async:
- Call
generate_imageorgenerate_video→ returnsjob_id - Poll
job_status({ job_id, sync: true })— usually resolves in 1–2 polls (~10–20s for images, longer for video)
Result fields: always pull rawUrl (PNG / source). minUrl is just the webp preview.
One ref, many gens — an uploaded media_id survives the session. Reuse it across calls instead of re-uploading.
Parallel jobs work fine — fire multiple generate_image calls and poll independently.
Reference passing
Schema is medias: [{ value, role }] (not reference_images). value accepts three forms:
media_id— frommedia_upload/media_confirmjob_id— output of a prior generation, used as input to the next. Killer feature for image→video chains — generate a still, feed itsjob_idstraight intogenerate_videowithout re-uploading.https://URL — any public image URL
Role taxonomy varies per model
Always run models_explore action=get for a model before crafting the call. Roles are not consistent:
| Model | Accepted roles |
|---|---|
| Seedance 1.5 | start_image, end_image |
| Seedance 2.0 | full set (image, start_image, end_image, video, audio) |
| Kling 3.0 | start_image only |
| Nano Banana Pro / GPT Image 2 | image |
Pass the wrong role and the call rejects.
Seedance 1.5 duration trap
If you omit duration, Seedance 1.5 silently defaults to 12 seconds — 3× the cost of a 4s clip. Always pass duration explicitly.
Pricing footnotes
- Plus plan: $39/month, 1000 credits — sweet spot for ad workloads
- Top-up packs ($5 / 100 credits) expire in 90 days — don't stockpile
- Verify exact per-call credit cost in the Higgsfield dashboard before committing to a workflow swap
Failure rules
- Don't silently fall back from ref-to-video to text-to-video — losing the visual anchor defeats the workflow. Surface the failure.
- Don't retry the same input on the same model when a content-policy check trips — propose changing the input or model.
- If the MCP errors out, check
/mcpto confirm the connection is still live. Subscription lapses break auth.
Models exposed
30+ behind one URL. Highlights:
| Category | Models |
|---|---|
| Image | Nano Banana Pro, GPT Image 2, Flux, Seedream |
| Video | Seedance 2.0, Sora 2, Veo, Kling, Hailuo |
| Higgsfield exclusives | Soul (character consistency), Cinema Studio |
Limits: images up to 4K, video up to 15 sec.