higgsfield · aeonfun — Claude Code skill: what it does, how to install
Generates images and video through the Higgsfield MCP server across 100+ models. Connects text-to-image, text-to-video, and image motion control tools.
npx skills add aeonfun/aeon --skill higgsfield -a claude-codeWhat higgsfield does
This skill connects your agent to the mcp.higgsfield.ai/mcp endpoint to access more than 100 generative models. It parses prompts for text-to-image, text-to-video, and image motion control using syntax like animate: <image-url> | <motion prompt>. The handler also recognizes inline flags for aspect ratio (--ar 16:9, --ar 9:16), duration, and model selection.
Every run spends real balance from your connected Higgsfield account. Because generation costs are irreversible, the skill enforces a hard cap of 2 outputs per run even when --n asks for more. Generation runs asynchronously: the agent polls task status up to roughly 20 times before returning temporary signed asset URLs or logging a timeout.
Static API keys are not supported. Running any generation tool requires an active OAuth connection established through the dashboard MCP panel.
What it helps with
- generate images from text prompts
- synthesize AI video clips from text descriptions
- animate source images with custom motion prompts
- cap account balance spend during automated media generation
Example prompts for the agent
- image: retro control room with glowing dials --ar 16:9
- video: drone shot moving through morning fog over pines --seconds 4
- animate: <image-url> | slow forward push with drifting snow
How to install higgsfield
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill higgsfield -a claude-code2Manually: put the files in place
~/.claude/skills/higgsfield/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/higgsfield/
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill higgsfield -a codex2Manually: put the files in place
.agents/skills/higgsfield/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill higgsfield -a cursor2Manually: put the files in place
.cursor/skills/higgsfield/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install aeonfun/aeon higgsfield2Manually: put the files in place
.github/skills/higgsfield/SKILL.md1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill higgsfield -a gemini-cli2Platform CLI
gemini skills install https://github.com/aeonfun/aeon --path skills/higgsfieldManually: put the files in place
.devin/skills/higgsfield/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill higgsfield -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about higgsfield
How do I install higgsfield in Claude Code?
Run npx skills add aeonfun/aeon --skill higgsfield -a claude-code.
What does the higgsfield skill do?
It interfaces with the Higgsfield MCP server to trigger text-to-image, text-to-video, and image-to-video jobs across 100+ models.
Can I authenticate with a static API key?
No, static API keys cannot be used. Authentication requires completing the OAuth Connect flow inside the dashboard MCP settings.
Source text
SKILL.md · MIT
---
name: higgsfield
description: Generate images and video through the Higgsfield MCP - text-to-image, text-to-video, and image-to-video with motion control across 100+ models. Generation draws real credits from the connected Higgsfield account; OAuth Connect via the dashboard MCP panel.
metadata:
title: Higgsfield
mode: read-only
category: productivity
var: ""
tags:
- content
- media
- mcp
mcp:
- higgsfield
capabilities:
- external_api
- writes_external_host
- sends_notifications
---
> **${var}** — the generation request. **Required.** Prefix picks the mode:
> - `image: <prompt>` (or a bare `<prompt>`) → text-to-image
> - `video: <prompt>` → text-to-video
> - `animate: <image-url> | <motion prompt>` → image-to-video (motion control)
>
> Optional trailing hints are honoured when the server supports them: `--ar 16:9` / `--ar 9:16` (aspect ratio), `--seconds N` (video duration), `--n K` (output count, capped below), `--model <name>`. If empty, log `HIGGS_NO_PROMPT` and exit cleanly — **no notify**. This skill spends credits, so it never fires on a blank/default run.
Generate visual media through the **Higgsfield** MCP server (`mcp.higgsfield.ai/mcp`): text-to-image, text-to-video, and image-to-video with motion control, across Higgsfield's library of 100+ generative models. **Every generation consumes real credits from the operator's Higgsfield account** — spend is irreversible, so the run is prompt-gated and bounded.
## Detection & auth
The server is wired by the dashboard MCP panel's one-click **Connect** (OAuth, Authorization Code + PKCE with `offline_access`; tokens stored as `MCP_HIGGSFIELD_TOKEN` + `MCP_HIGGSFIELD_OAUTH`, refreshed each run by `scripts/mcp-oauth-refresh.sh`). Its tools surface as `mcp__higgsfield__*` — discover them from the server; the tool descriptions are the source of truth, don't assume a fixed list or invent model names.
- **No `mcp__higgsfield__*` tool callable** → the server isn't connected (or its secrets are missing, in which case the workflow logged a `::warning::` and skipped MCP). Log `HIGGS_NOT_CONNECTED`, notify once pointing the operator at the dashboard → MCP → Connect Higgsfield, and exit. Don't try to reach the API with curl — there is no static key.
- **Tools exist but return 401/invalid-token** → the OAuth refresh failed (rotating refresh tokens need `GH_SECRETS_PAT` — see `docs/mcp-oauth.md`). Log `HIGGS_AUTH_STALE`, notify the operator to re-connect the server once in the dashboard, and exit. Don't retry the same call more than twice.
- **Payment-required / insufficient-credits errors** → log `HIGGS_NO_CREDITS`, notify the operator to top up their Higgsfield account, and exit with any partial output already returned (clearly marked partial).
## Steps
### 1. Parse the request
From `${var}`, resolve:
- **Mode** — image / video / animate (from the prefix; default `image` when none given).
- **Prompt** — the descriptive text. For `animate:`, split on `|` into the source image URL and the motion prompt.
- **Params** — aspect ratio, duration, count, model from the `--` hints. Only pass params the chosen tool actually accepts (read its schema); drop the rest silently.
Pick the model/tool that fits the mode. When several fit, prefer the tool's default or the one the server marks recommended — don't guess an exotic model.
**Spend budget:** **one** generation per run by default; `--n K` may request more only up to a hard cap of **2** outputs total per run. Never loop "one more" generation beyond the cap. This is a hard limit (STRATEGY: stay within configured spend limits).
### 2. Generate
Call the generation tool with the resolved prompt + params. Higgsfield generation is **asynchronous** — most tools return a job/prediction id rather than the finished asset. If the server exposes a status/result tool, poll it until the job reports complete, **failed**, or you hit a bound of ~20 polls (stop and report a timeout rather than polling forever). If the tool blocks until done and returns assets directly, use that.
- Submit as the **final substantive action** of the run (fail-closed: parsing, budget checks, and log prep happen first, so a generation failure surfaces in this run).
- One retry at most on a transient error; never re-submit a job that already succeeded (that double-charges).
- Capture the server's response verbatim: job id, status, output asset URL(s), and any cost/credit figure it returns.
### 3. Collect output
Gather the finished asset URL(s) and the model actually used. If the job failed or timed out, capture the server's error/status — **never** fabricate an asset URL or claim a generation that has no URL back.
### 4. Notify
This skill is on-demand — a completed run always notifies. Deliver via `./notify -f` (ordinary Markdown), **exactly one `./notify` call per run** (each call overwrites `$AEON_PENDING_DIR/.pending-higgsfield.md`, the chain artifact `consume:` steps and the feed read — a second ping would clobber the result):
- **Success:** the mode + model used, the prompt (trimmed), and each output asset as a clickable URL. Include the credit/cost figure if the server returned one, and the job id. Severity `success`.
- **Failure / refusal / no-credits:** exactly what happened (auth stale, no credits, content rejected, timeout) and the one action the operator can take. Severity `warn`.
Note assets may be time-limited signed URLs — say so and suggest the operator save anything they want to keep.
### 5. Log
This skill is `read-only`, so the workflow's read-only guard writes its `### higgsfield` log entry from your captured output; a self-written entry would be a duplicate. Don't append to `memory/logs/` yourself - put this record in your **final output**:
```
### higgsfield
- Request: <${var}, truncated>
- Result: HIGGS_OK | HIGGS_NO_PROMPT | HIGGS_NOT_CONNECTED | HIGGS_AUTH_STALE | HIGGS_NO_CREDITS | HIGGS_FAILED
- Mode: image | video | animate | model: <name> | outputs: N (cap 2)
- Assets: <url(s) or "none">
- Cost: <credits/USD if returned, else "unknown">
```
## Constraints
- **Credits are real and irreversible.** One generation per run by default, ≤2 outputs total, ever. A `${var}` asking for a batch is capped, not honoured in full — say what was capped in the notify.
- **Content policy.** Refuse prompts for a real, identifiable person's likeness without a clear consent signal in the request, sexual content involving anyone who could be a minor, or other content the platform disallows — log `HIGGS_FAILED` reason=`content-refused`, notify why, and exit. When Higgsfield itself rejects a prompt, relay its reason; don't retry with a reworded prompt to route around a safety refusal.
- **Every asset URL traces to a tool response.** Never estimate, guess, or reconstruct an output that the server didn't return.
- The operator owns every generation this agent triggers — when the request is ambiguous about what to make, refuse and ask rather than spend credits on a guess.
© aeonfun · MIT · aeonfun/aeon
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