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muapi-ai-fight-scene

@samuraigpt · 收录于 1 周前 · 上游提交 3 周前

Generate a high-cut-density action / fight scene by first composing a 16-cell storyboard image, then driving Seedance 2.0 image-to-video off that storyboard. Stacks GPT-Image-2 (character sheet + storyboard), Nano-Banana-2 (environment concept), and Seedance 2.0 i2v.

适合你,如果你需要快速生成电影级动作打斗视频片段。

/ 下载安装
muapi-ai-fight-scene.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add samuraigpt/generative-media-skills/muapi-ai-fight-scene
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- samuraigpt/generative-media-skills/muapi-ai-fight-scene
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify samuraigpt/generative-media-skills/muapi-ai-fight-scene
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 a1c4c98 · 表述以原文为准
它做什么

安装后,Claude 会引导你描述角色、环境和动作脚本,先生成角色设定图和场景概念图,再合成一张 16 格分镜故事板,最后用 Seedance 2.0 将故事板转为一段高剪辑密度的打斗视频。

什么时候触发

当你要求生成打斗场景、动作序列或分镜转视频,并提供了角色描述、环境描述和动作脚本时触发。

装好后可以这样说
Claude 会要求你补充动作脚本等细节,然后逐步生成。
Claude 会先让你描述角色和环境,再制作故事板并转视频。
技能原文 SKILL.md作者撰写 · MIT · a1c4c98

AI Fight Scene Generator

Generate a high-cut-density action / fight scene by first composing a 16-cell storyboard image, then driving Seedance 2.0 image-to-video off that storyboard.

The core idea: action tension comes from cut density, not single-shot quality. Forcing the video model to follow a pre-drawn 4×4 storyboard grid gives you 16 distinct shots in a 15-second clip — landing punches, reverse angles, ECUs, whip-pans — that no t2v prompt could choreograph on its own.

Inputs

| Name | Type | Required | Default | Description | |:---|:---|:---|:---|:---| | character_description | text | yes | — | Full physical description of the fighter(s). Asymmetric details (eye colour, scar side, holster on left hip) help the model preserve identity across panels. | | environment_description | text | yes | — | The scene setting — e.g. "cyberpunk wet back-alley, neon kanji signage, Stray-game aesthetic, rain on chrome." | | action_script | text | yes | — | The action beat — prose or numbered beats. E.g. "Hero is cornered → blocks first punch → counter-elbow → throw opponent into trash cans → finisher." | | style_direction | text | no | cinematic action film, anamorphic lens, high contrast, motion blur on hits | Aesthetic / look tags applied to every frame. | | duration | int | no | 15 | Final video length in seconds. The storyboard's 16 cells map roughly 1 shot per second at default. | | aspect_ratio | text | no | 16:9 | Output aspect — 16:9 cinematic, 9:16 vertical, 1:1 square. |

Steps
Phase A — Character Sheet

Generate a clean turnaround-style character sheet using muapi image generate (model=gpt-image-2-text-to-image):

  • Prompt: Character reference sheet of {{character_description}}. Three views — front, 3/4, profile — on a neutral grey backdrop. Studio lighting, full body, no text overlays, photoreal. Asymmetric identifying details preserved on the correct side. {{style_direction}}.
  • Aspect ratio: 3:2

Present the character sheet and confirm identity details look right before proceeding. This image becomes reference #1 for later phases.

Phase B — Environment Concept

Use muapi image generate (model=nano-banana-2) to design the scene/world:

  • Prompt: Wide establishing shot of {{environment_description}}. No characters in frame — environment only. Strong perspective lines, depth, atmospheric haze. {{style_direction}}. Production-design concept art.
  • Aspect ratio: {{aspect_ratio}}

Nano-Banana-2 is chosen here for its reasoning-driven composition — it's better than text-to-image-only models at producing locations with believable spatial logic (chokepoints, cover, sightlines) that an action scene can use. Present for approval. This becomes reference #2.

Phase C — 16-Cell Storyboard

Compose the action onto a single 4×4 storyboard image using muapi image edit (model=gpt-image-2-image-to-image):

  • Reference Images: the character sheet from Phase A and the environment plate from Phase B.
  • Prompt: ``` Compose a 4×4 storyboard grid (16 numbered cells) for the following action sequence: {{action_script}}

CHARACTER (use reference image 1 identity throughout, asymmetric details preserved): {{character_description}}

LOCATION (use reference image 2 spatial layout): {{environment_description}}

Each cell labels: SHOT # (1–16) · SIZE (WIDE / MS / CU / ECU) · CAMERA-MOVE arrow (push, pull, whip, dolly, crash-zoom, handheld) · 1-word RHYTHM note (BEAT / IMPACT / RECOVERY / RESET).

Vary shot size aggressively — never two WIDEs in a row. Land every IMPACT on a CU or ECU. Hand-drawn comic-book ink-and-wash style, monochrome with selective red accents on hits. Numbered cells, clear gutters between panels.

Aesthetic: {{style_direction}}. ```

  • Aspect ratio: 1:1 (square works best for a 4×4 grid)

Present the storyboard to the user. Confirm:

  • The 16 shots read clearly
  • Identity stays consistent cell-to-cell
  • Cut density / shot-size variation looks aggressive enough

If a panel reads poorly, regenerate just the storyboard with that cell's note bolded ("CELL 7 must be an ECU on the right fist").

Phase D — Storyboard → Video (Seedance 2.0)

Hand the storyboard to muapi video from-image (model=seedance-v2.0-i2v):

  • Reference Image: the 16-cell storyboard from Phase C.
  • Prompt: ``` Generate a {{duration}}-second action sequence that strictly follows the 16-cell storyboard reference image, cell-by-cell, top-left to bottom-right.
  • Honour each cell's labelled SHOT SIZE and CAMERA-MOVE — match cuts to the storyboard's rhythm notes.
  • Strong cinematic feel and shot language. Exaggerated dynamics. Hits land hard with motion blur and impact frames.
  • Camera language: anamorphic, handheld where the storyboard calls for it, locked-off where it doesn't.
  • Native audio: impact sfx on every IMPACT cell, footsteps, fabric/Foley, restrained low score under the action.

Action being rendered: {{action_script}}. Aesthetic: {{style_direction}}. ```

  • Duration: {{duration}} (default 15)
  • Aspect ratio: {{aspect_ratio}}

After generation, present the final video. If the cut density feels too low or shots don't match the storyboard, regenerate Phase D first (cheaper than rebuilding the storyboard) with the prompt emphasising "strict cell-by-cell adherence" more aggressively.

Notes
  • Why the storyboard image and not a text storyboard? Seedance 2.0 i2v anchors its motion plan to the visual reference. A grid of 16 drawn cells gives it 16 visual targets to hit — text descriptions of shots get averaged into mush.
  • Asymmetric character details matter. Without something like "scar over the right eyebrow" or "leather glove on the left hand only", identity drift between cells is the #1 failure mode.
  • Use seedance-2.0-i2v-480p to draft. Cheaper preview pass before committing to the full-res seedance-v2.0-i2v run.
  • For longer fights, chain two runs: first run uses storyboard A (cells 1–16, beats 1–15s); second run uses storyboard B (cells 17–32, beats 15–30s) with the last cell of A as a continuity anchor in B's first cell.
  • Language: Both English and Chinese prompts work in all four models, so the storyboard cell labels can be in either language.
Trigger Keywords

fight scene, action sequence, storyboard to video, cut density, cinematic action, combat choreography, seedance 2 storyboard

Pipeline at a Glance
character_description ──► [GPT-Image-2 t2i]   ─► character sheet ──┐
                                                                    │
environment_description ─► [Nano-Banana-2 t2i] ─► environment plate ┼─► [GPT-Image-2 i2i] ─► 16-cell storyboard ─► [Seedance 2.0 i2v] ─► 15s action video
                                                                    │
action_script + style_direction ───────────────────────────────────►┘

Notes for the Executing Agent
  • This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
  • For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
  • Phase C uses TWO reference images (character sheet + environment plate). When calling gpt-image-2-image-to-image, pass them as a list under images_list (or the model's documented multi-ref field).
  • Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.
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