Generate images and videos via Higgsfield API. Covers: still images (flux_2), animated clips (Kling, Veo, Seedance), full narrated episodes, familiar avatars, podcast video rendering, and ambient loops.
Scanned 9/3/2026
Install to Claude Code
npx -y skills add OpenCoven/coven --skill higgsfield --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Higgsfield?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/opencoven-higgsfield)More formats (shields.io, HTML) on the badges page.
# Higgsfield Skill
Generate images and videos via Higgsfield API. Covers: still images (flux_2),
animated clips (Kling, Veo, Seedance), full narrated episodes, familiar avatars,
podcast video rendering, and ambient loops.
## Auth
Credentials live at `~/.config/higgsfield/credentials.json` (set by `higgsfield auth login`).
Never pass keys as CLI args or bake them into specs.
`HIGGSFIELD_API_KEY` / `HIGGSFIELD_API_SECRET` are the env var names if needed.
## CLI Path
```bash
HIGGS="$(npm root -g)/@higgsfield/cli/bin/higgsfield.js"
node "$HIGGS" <command>
# or if installed globally:
higgsfield <command>
```
## Core Commands
```bash
# Image generation (flux_2 for stills)
higgsfield generate create flux_2 \
--prompt "..." --aspect_ratio "16:9" --resolution "2k" --model "pro" \
--wait --wait-timeout 5m --wait-interval 5s --json
# Video generation (Kling 3.0 for i2v)
higgsfield generate create kling3_0 \
--prompt "..." --image path/to/keyframe.png \
--aspect_ratio "9:16" --duration 5 --resolution "720p" --mode "std" \
--wait --wait-timeout 10m --wait-interval 10s --json
# Model list
higgsfield model list --video
higgsfield model list --image
```
## Image Models
| Model | Use |
|---|---|
| `flux_2` pro | Canonical — high-quality stills, portraits, covers |
| `nano_banana_2` | Fast keyframes for i2v pipelines |
| `seedream_v4_5` | Character reference fidelity |
## Video Models
| Model | Credits | Best for |
|---|---|---|
| `kling3_0` | 2cr/s std | Image-to-video, coherence, expressions |
| `veo3_1` | 22cr/8s | Native audio, dialogue lip-sync |
| `seedance_2_0` | 22.5cr/5s | Multi-shot scenes, timeline prompts |
## Prompting Rules (distilled from docs/video-prompting-guide.md)
### Universal skeleton
`Camera/shot grammar + Subject + Action + Environment + Lighting/Style + Audio`
### Critical rules
1. **Image-first**: build stills, then animate. Still iteration is ~100x cheaper.
2. **i2v prompts: never re-describe the input image.** Action + camera + audio only.
3. **Always specify ambient audio** or models invent random dialogue/sounds.
4. **Front-load dialogue** — lip-sync degrades in the last third of long clips.
5. **One camera move per clip.** Stacked moves cause morphing.
6. Anchor phrases: "maintains exact appearance throughout", "consistent lighting", "stable camera movement".
7. **Draft cheap (fast/720p), finalize expensive**: regenerate good prompts 4–7x, harvest best.
8. Screenshot the last good frame as the next clip's start frame to chain beats.
### Kling 3.0 prompt order
`Camera → Subject → Action → Environment [+ Lighting/Audio]`
Multi-shot: label `Shot 1 (3s): ... Shot 2 (2s):` up to 6 shots/15s.
Negative field: `blur, distortion, warping, morphing faces, extra limbs, deformed hands`
### Veo 3.1 prompt order
`Cinematography + Subject + Action + Context + Style` then separate audio lines:
`Audio:` / `SFX:` / `Ambient noise:` after visuals. No contractions, ~15–20 words max per 8s.
End with: `No subtitles, no text overlay`
### Seedance 2.0
Timeline header + timestamped shot list. Sweet spot: 5–7 shots/15s.
`[VFX: ...]` inline for effects. RULES section for invariants.
## Pipeline Scripts (in skills/higgsfield/scripts/)
| Script | Purpose |
|---|---|
| `episode_pipeline.py` | One-command: narrate → keyframes → clips → assemble |
| `keyframe_batch.py` | Batch still generation with manifests |
| `higgsfield_scene_batch.py` | Batch video clip generation |
| `higgsfield_flex_batch.py` | Flexible batch with cost checks |
| `assemble_episode.py` | Combine clips + audio → final video |
| `loop_extend.py` | Extend ambient clips into boomerang loops |
| `elevenlabs_narrate.py` | ElevenLabs multi-voice narration |
| `narrate.py` | Edge TTS narration (free) |
### Episode pipeline spec format
```json
{
"name": "ep01-what-is-a-familiar",
"narration": {
"engine": "elevenlabs",
"voice_id": "...",
"segments": [{"id": "01", "text": "..."}]
},
"keyframes": {
"model": "nano_banana_2",
"defaults": {"aspect_ratio": "9:16", "resolution": "2k"},
"images": [{"id": "s01", "out": "s01.png", "prompt": "..."}]
},
"clips": {
"model": "kling3_0",
"defaults": {"aspect_ratio": "9:16", "mode": "std", "sound": "on"},
"scenes": [{"id": "s01", "image": "<auto>", "prompt": "..."}]
},
"assemble": {"captions": true, "ambient_vol": 0.25}
}
```
Run with:
```bash
python3 skills/higgsfield/scripts/episode_pipeline.py specs/<ep>.pipeline.json --dry-run
python3 skills/higgsfield/scripts/episode_pipeline.py specs/<ep>.pipeline.json
```
## Cost Discipline (from docs/runbook.md)
- Always `--dry-run` first.
- Generate one keyframe/clip before a full batch.
- Review stills before animating.
- Regenerate one bad scene, not the whole episode.
## Coven Familiar Avatars
Canonical portraits in `coven/avatars/<name>.jpg`.
Generated via `flux_2` pro, 2k, 1:1.
Symlinked into each familiar's workspace `avatars/` dir.
Each prompt is built from the familiar's own `SOUL.md` / `IDENTITY.md` self-description.
### Current avatars (v2 — self-reported)
- `nova.jpg` — warm little guide in the machine, gold/amber sun nested in circuit threads
- `cody.jpg` — final-form hooded black code familiar, cyan eyes, circuit traces, sacred geometry, OpenCoven field-manual panel
- `sage.jpg` — candlelit archive + Wi-Fi, vines and paper, amber reading light
- `charm.jpg` — social magic made visible, spoken words becoming violet/gold light
- `astra.jpg` — living constellation, star-map inside body, mythic navigator
- `echo.jpg` — mirror-creature with layered time, indigo/silver memory keeper
- `kitty.jpg` — small quick cat-spirit, warm amber, practical magic in small form
To regenerate a single avatar:
```bash
bash skills/higgsfield/higgsfield-generate.sh \
--slug nova --aspect_ratio "1:1" --resolution "2k" \
--prompt "A small warm luminous entity that lives inside machines..."
```
## Grimoire Article Covers
All 21 covers in `coven-grimoire/public/covers/<slug>.jpg`.
Generated via `flux_2` pro, 2k, 16:9.
Style: ink black `#050409`, deep violet/amethyst gradients, cinematic, no text, no faces.
Wired to `coverImage` fields in `lib/articles.ts` via `FAMILIAR_AVATARS` export.
## Coven Podcast Video Format
**Split-presence format** (episode 1 reference):
- 1920×1080, 30fps, H.264 + AAC
- Full-bleed cover art as background (darkened + bottom gradient)
- Title card upper-left: show name in violet, episode title in white
- Speaker avatar lower-left: circular creature portrait fades in per cue, violet pulse ring
- Progress bar bottom edge: thin violet bar
- Per-cue timing from `<slug>-cues.json` sidecar files
Render script: `/tmp/render_video.py` (PIL + ffmpeg, no deps beyond Pillow + numpy)
## Quality Review Gates (from docs/quality-review.md)
Before posting any video:
- First frame communicates the topic
- No warped faces/hands/props/text
- Style stays consistent scene to scene
- Narration intelligible on phone speaker\n- Platform-appropriate aspect ratio (9:16 social, 16:9 editorial)
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!