media-higgsfield-assets

media-higgsfield-assets is a skill for Claude Code from modu-ai/moai-cowork. It costs 293 tokens per session (2,035 once invoked), scanned A, original, Apache-2.0.

A Higgsfield workflow for creating or processing media beyond ordinary image and video generation. It covers 3D GLB models, rigging and animation, audio, video analysis, upscaling, reframing, outpainting, and background removal.

In plain words
What is it for?
Use it to make 3D assets, add sound effects or narration, assess a video's hook and retention, enlarge images, change their framing, extend scenes, or remove backgrounds.
Why use it?
It brings several common asset-making and cleanup jobs into one workflow instead of treating them as separate tasks. The available options depend on the live Higgsfield catalogue.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the moai-media plugin — 14 skills, 2 agents shipped together

Good fit Use it to make 3D assets, add sound effects or narration, assess a video's hook and retention, enlarge images, change their framing, extend scenes, or remove backgrounds.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/modu-ai/moai-cowork/media-higgsfield-assets
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add modu-ai/moai-cowork --skill media-higgsfield-assets
Clone the repo
git clone --depth 1 https://github.com/modu-ai/moai-cowork

Made for: Claude Code.

Or install moai-media, the plugin that ships this one along with the rest of its 14 skills, 2 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for media-higgsfield-assets

README.md
[![agentmods](https://agentmods.dev/badge/skills/modu-ai/moai-cowork/media-higgsfield-assets/github.svg)](https://agentmods.dev/skills/modu-ai/moai-cowork/media-higgsfield-assets)
Your own site
<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/media-higgsfield-assets"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/media-higgsfield-assets/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for media-higgsfield-assets

Your own site · 80×15
<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/media-higgsfield-assets"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/media-higgsfield-assets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 293 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,035 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00293 $0.02035
Opus 5 $0.00147 $0.01018
Sonnet 5 $0.00059 $0.00407
Haiku 4.5 $0.00029 $0.00203

Measured 8d ago against content hash 07b293b81131, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

media-higgsfield-assets scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/moai-media/skills/media-higgsfield-assets/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Higgsfield 에셋·후처리 (media-higgsfield-assets)

moai-media | 3D · 오디오 · 영상 분석 · 후처리 (코어: media-higgsfield-core)

개요

media-higgsfield-imagemedia-higgsfield-video가 "새 이미지·영상 만들기"를 담당한다면, 이 스킬은 그 바깥의 네 영역을 담당한다: 3D 에셋, 오디오, 완성 영상 분석, 그리고 이미 있는 에셋의 후처리.

호출 계약·비용 프리플라이트·namespace 해석은 코어를 따른다:

  • 호출 계약: ../media-higgsfield-core/references/call-schema.md
  • 라이브 조회: ../media-higgsfield-core/references/catalog-protocol.md
  • 잡·비용·리드백: ../media-higgsfield-core/references/job-lifecycle.md

트리거 키워드

3D, GLB, 메시, 리깅, 스켈레톤, 3D 모델링, 텍스처, PBR, 효과음, SFX, 앰비언스, 배경음악, BGM, 내레이션, TTS, 음성 합성, 보이스, 바이럴 예측, 훅 점수, 영상 분석, 업스케일, 4K, 리프레임, 아웃페인팅, 배경 제거, 누끼

네 영역과 진입점

영역 하는 일 상세
3D 이미지·텍스트 → GLB 메시, 리깅, 애니메이션 references/3d.md
오디오 효과음·앰비언스·음악·TTS references/audio.md
분석 완성 영상의 주의·훅·리텐션 점수 references/analysis.md
후처리 업스케일·리프레임·아웃페인팅·배경 제거·모션 아래 §후처리

워크플로우

코어의 REQ-010 흐름을 그대로 따른다. 영역만 다를 뿐 순서는 같다.

  1. 의도 → 영역·후보 좁히기. 위 표에서 영역을 고르고, 해당 참조 파일로 후보 모델을 좁힌다. 파라미터를 단정하지 않는다.
  2. 라이브 조회. models_explore(action:'get')로 실제 제약을 가져온다. 3D·오디오는 모델별 파라미터 편차가 이미지·영상보다 크므로 이 단계를 건너뛰면 거의 실패한다.
  3. 비용 프리플라이트. get_cost: truecredits 확인. 3D의 텍스처·리깅·애니메이션은 각각 추가 비용이므로, 옵션을 켠 상태의 비용을 확인한다.
  4. 승인 게이트. 크레딧이 나가기 전에 멈춘다 — 코어 §유료 생성 승인 게이트를 그대로 따른다. 프롬프트 전문·모델·입력 미디어·옵션·개수·adjustments·견적 크레딧을 보여주고 승인을 받는다. 3D에서 텍스처·리깅·애니메이션 옵션을 켰다면 옵션별 추가 비용을 각각 보여준다 — 합계만 보여주면 어느 옵션이 비싼지 알 수 없다.
  5. 생성. 승인된 값으로만 호출.
  6. 폴링·리드백. job_status로 완료까지. adjustments가 있으면 사용자에게 보고한다.

후처리 (기존 에셋 변형)

새로 만들지 않고 이미 있는 에셋을 바꾸는 경우다. 각 작업에는 전용 도구가 있으므로, 같은 결과를 생성 모델로 재현하려 하지 않는다 — 전용 도구가 더 싸고 결과가 안정적이다.

요청 전용 도구
해상도 키우기 (이미지) 이미지 업스케일
해상도 키우기 (영상) 영상 업스케일
캔버스 넓히기 / 크롭 해제 아웃페인팅
영상 비율 변경 (가로↔세로) 리프레임
배경 제거 / 투명 배경 배경 제거
모션 이식 / 리캐스트 / 퍼펫 모션 컨트롤

입력은 코어 규칙과 동일하게 media_id 또는 이전 잡의 job_id로 전달한다. 날것의 URL은 거부된다.

출력 형식

## Higgsfield 에셋 생성 결과
- 영역: [3D | 오디오 | 분석 | 후처리]
- 모델·도구: [models_explore로 확인한 실제 id]
- 적용 옵션: [텍스처·리깅·애니메이션 / 포맷·샘플레이트 / 등]
- 비용: [get_cost가 반환한 credits]
- Job ID / 결과 URL: [job_status completed]
- 서버 조정(adjustments): [있으면 그대로 보고]

Read the full file on GitHub · 106 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 106 lines · 293 tokens per session scan A 07b293b81131

Subscribe to this mod's changes

media-higgsfield-assets is a skill published in the GitHub repository modu-ai/moai-cowork (300 stars, last pushed 9d ago), licensed Apache-2.0. It adds 293 tokens to every session and 2,035 once invoked, about $0.0015 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.