GitHubのプルリクエストに付いた未解決レビューを確認し、妥当な修正、個別返信、必要に応じたプッシュまで進めるスキルです。
- PRの未解決レビューを整理したいとき
- 妥当な指摘を修正して返信したいとき
- レビューの質問にコード変更なしで答えたいとき
参照動画から人物の動きを3Dアニメーション化し、キャラクターへの適用やループ設定、用途に合う形式での書き出しを進めるスキルです。
Use when turning a reference video into 3D character animation through the Scenario MCP: markerless motion capture from a phone clip or a generated performance, auto-rigging an uploaded FBX, GLB, or OBJ character and applying the captured motion, raw motion on a Mixamo or MetaHuman skeleton to retarget yourself, in-place locomotion, looping clips, facial capture, or FBX exports for Blender, Maya, Unreal, Unity, or Roblox. Keywords: mocap, video to motion, video23d, animation from video.
実行スコア 未検証
インストール方法を見るScenario MCPを使い、参照動画に映る人物の動きを3Dアニメーションにします。アップロードしたFBX、GLB、OBJのメッシュを自動リギングして動かす方法と、Mixamo風またはMetaHumanの標準スケルトンに動きだけを出力する方法を選べます。入力動画やキャラクターをアップロードし、長さやサイズを確認してからモデルを選び、スキーマに沿って入力欄や出力形式を設定します。ループ、ゲーム向けのその場歩行、フレームレート、向きの軸なども指定でき、完成後は足の接地や向き、ループのつながりを確認します。
スマートフォンで撮ったダンスや演技をキャラクターに適用したいとき、既存リグを保ったまま動きだけ取り出したいときに向いています。Blender、Maya、Unreal、Unity、Roblox向けの書き出しを選びたい場合にも使えます。
動画の長さ・容量・縦横寸法にはモデルごとの上限があります。出力形式や入力欄もモデルごとに異なるため、実行前にスキーマの確認が必要です。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Video-to-motion members watch the people in a clip and return 3D animation: either your character
mesh, auto-rigged and moving, or the motion alone on a standard skeleton. They are the video23d
capability, and they differ more than their shared name suggests: which character input they take,
which skeleton comes back, and which file formats they write.
Siblings: scenario (connection, scope, upload, jobs_wait), scenario-3d (making or rigging the
mesh first), scenario-video (generating a reference performance when no footage exists), and
scenario-video-editing (trimming and resizing the clip). If a sibling skill named here is missing
from your available skills, load it with skill_get (Scenario MCP) or ask the user to install it
(https://github.com/scenario-labs/skills#install); unattended, proceed from tool schemas and flag
the gap.
| Step | Call | Watch for |
|---|---|---|
| Upload inputs | upload_asset with kind: "video", then kind: "3d" for a mesh | The mesh is optional on every member |
| Fit the clip | asset_get, then trim or resize per scenario-video-editing | Duration, size, and even-dimension limits per member |
| Pick a member | recommend with capability: "video23d" and the user's own words | Read next_step per the scenario skill |
| Read the inputs | model_schema_get | Each file field's kind, the output enums |
| Price, then run | model_run with dry_run: true, then wait: false | The video field carries cost_impact |
| Wait and inspect | jobs_wait, then asset_display | Facing, foot contact, and frame count |
Three outcomes, and members cover different ones. Settle it with the user first (unattended: a character whose existing rig the project depends on takes motion only; otherwise animate the mesh).
recommend ranks the members on measured cost and latency and names which take a character. Ask it
with the user's words, including the outcome above, rather than with a bare capability.
Field names repeat across members with different meanings, so read each field's kind before
filling it:
characterFile takes a 3D mesh; another member's characterFile takes a reference
image of the character, while its mesh field is characterModel. The field's kind (3d or
image), not its name, says which asset it takes.fbx-blender,
fbx-unreal, fbx-roblox, Maya scene files), one a plain glb or fbx (its schema names fbx
for Unity), and one exposes no format field at all. Pick the engine target in the run:
asset_download's format conversion is not documented to keep the animation.moveInPlace for game locomotion driven by code, loop for
cycles, the frame rate field (named frameRate, fps, or targetFps), keyframe cleanup, and the
forward and up axes when the motion arrives turned or lying down.numPeople) or capture the face; both carry cost_impact.max_duration and max_size when exposed by the schema. An absent cap is not a promise of
unlimited input. Fix an over-cap clip with the video editing tools before pricing.scenario-video: a single full-body
character on a plain background, a locked-off camera, and the action described beat by beat. The
generated clip is then the input like any other.The user has a phone clip of a dance and a character GLB, and wants an Unreal-ready loop.
upload_asset the clip (kind: "video") and the GLB (kind: "3d") per the scenario skill.asset_get the clip: read the duration and dimensions. Trim to the eight seconds that loop with
the video cut tool, and resize to even dimensions if a candidate member requires it.recommend with capability: "video23d" and a prompt such as "apply the dance in this video to
my uploaded character mesh, looping, exported for Unreal". Follow next_step per the scenario
skill: present ask_user options; on proceed, take its pick without asking.model_schema_get on the pick. Put the GLB in the field whose kind is 3d. Set the Unreal
output target if the format enum has one, loop: true, and moveInPlace: true if the game moves
the character itself.model_run with dry_run: true and quote the price. Then run with wait: false: these jobs
take minutes. Retire it with jobs_wait, re-calling with pending_job_ids on timeout.asset_display the result, or open it in a DCC. Check the facing, that the feet meet the ground,
and that the loop point does not pop. If facing is wrong, check forward and up when the
schema exposes them before blaming the capture. A preview link alone is not a visual check.asset_download with no format, and hand over the file with the skeleton it uses.characterFile is a mesh on one member and an image on another.job_get instead of jobs_wait, or re-running a job that only timed out.まだレビューはありません。使ってみた感想をお寄せください。
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GitHubのプルリクエストに付いた未解決レビューを確認し、妥当な修正、個別返信、必要に応じたプッシュまで進めるスキルです。
PRの差分だけを読んで、Conventional Commits形式のsquashメッセージを作成し、リポジトリの規約で検証して表示・コピーするスキルです。
プルリクエストの差分を読み直し、リポジトリのテンプレートに沿って、現在の変更内容を反映した説明文に更新します。
ScenarioをMCP経由でエージェントにつなぎ、画像・動画・3D・音声などを生成する手順や、モデル選び、素材の受け渡し、実行状況の確認を支援します。
テキストや画像から3Dモデルを生成し、形状や質感の編集、UV展開、リギング、アニメーションの適用までScenario上で進めます。
テキストや画像、複数写真、動画から歩き回れる3D空間やGaussian splatを生成し、プレビューして最終データを書き出します。