AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Motion / animation context for the `use_figma` MCP tool — animating Figma nodes via manual keyframes, animation styles, easing, and timeline duration. Load alongside figma-use whenever a task involves adding, editing, or inspecting animation on a node.
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。
Motion context for the use_figma MCP tool. figma-use covers the foundational Plugin API rules — load both together.
Always pass skillNames: "figma-use-motion" (comma-separated alongside figma-use) when calling use_figma for motion work. Logging only.
Motion APIs are gated behind the metronome user feature flag. When the calling user doesn't have it, every motion property and helper referenced in this skill throws "<name>" is not a supported API.
Bail out fast on that error. Do not retry; tell the user motion isn't enabled for them and stop. Otherwise you'll burn calls and confuse the user with repeated identical failures.
Load this skill whenever a use_figma task involves:
manualKeyframeTracks, applyManualKeyframeTrack, removeManualKeyframeTrack).applyAnimationStyle, removeAnimationStyle, animationStyles).node.timelines / node.setTimelineDuration(id, seconds).Static design work (creating shapes, components, variables, layout) goes through figma-use alone — this skill is only for the time dimension.
node.manualKeyframeTracks — read/write manual keyframes (including fill, stroke, and effect tracks).node.applyManualKeyframeTrack(field, track) / node.removeManualKeyframeTrack(field) — add, replace, or remove one manual keyframe track without rewriting the whole object.node.animationStyles — read/write animation-style metadata applied to a node.node.applyAnimationStyle(styleId, presetData?) / node.removeAnimationStyle(id) — apply a discovered style and remove an applied style instance by its returned/read-back id.node.timelines — read-only timeline list for the containing top-level frame, with durations in seconds.node.setTimelineDuration(id, durationSeconds) — write the containing top-level frame timeline duration.node.animations — read-only resolved keyframe data (currently manual tracks only — see motion-patterns.md).figma.motion.figmaAnimationStyles() — read-only list of Figma's first-party animation styles.Authoring custom "figma:motion" preset module source code is out of scope. If the user wants a brand-new animation style, say so and stop; don't fabricate one.
Load these as needed based on what the task involves:
| Doc | When to load | What it covers |
|---|---|---|
| motion-patterns.md | Adding/editing motion animation | Manual keyframes, animated fills/strokes, applying animation styles, timeline duration |
| motion-easing.md | Setting animation easing | Keyframe easing objects, custom cubic/spring, HOLD, applying easing inside an animation style |
get_screenshot shows only the timeline's resting state, never motion. To check motion, export_video and sample frames — but it renders server-side and is slow and expensive (~10s to minutes), so make each render count.
Plan before rendering — cost scales with pixels × frames, so keep both no larger than the frames need:
constraint: { type: 'WIDTH', value: 320 }, quality: "low" — but text and small elements blur there, so raise WIDTH (768+) when you need to judge fine detail. Omitting constraint = full size (1x; server clamps to 10x / 4096px).fps: 5 covers a handful; 10 is an upper bound. Higher just bloats the render.Mechanics: export_video works only on a top-level frame whose children carry the animation (pass that frame, not the descendant you keyframed). It returns a jobId with status: "processing" — re-invoke with { fileKey, jobId } to poll. Then extract frames locally with ffmpeg -ss <t> -i anim.mp4 -frames:v 1 frame_<t>.png — extraction is free, so once you've paid for the render, mine it for every frame that tells you something rather than re-exporting. Without a frame extractor like ffmpeg, skip the export and reason about the keyframes instead.
Iterate until it's right. The export is a diagnostic, not a sign-off: if the frames are wrong (bad order, off timing, a missing element, a mask blanking the composite), fix the keyframes/styles and re-export. Read all the frames and batch every fix into one pass before re-rendering — every render carries real overhead, so make each one count instead of re-exporting after each small change.
Skip the export entirely for trivial or self-evident changes.
In addition to the figma-use pre-flight checklist, verify:
{ type: 'EASE_OUT', easingFunctionCubicBezier?: …, easingFunctionSpring?: … } shape — not internal scenegraph names like OUT_CUBIC.EASE_IN_AND_OUT (or EASE_IN_AND_OUT_BACK); never emit the invalid alias EASE_IN_OUT.setTimelineDuration; never shorten unless the user asked.TRANSLATION_X, TRANSLATION_Y, ROTATION, SCALE_X, SCALE_Y, SCALE_XY), not internal MOTION_* scenegraph names.figma-use Rule 15).ffmpeg) is available, verify via export_video + frame sampling — render small, low fps, iterate until right (see the Verifying the animation section above). get_screenshot shows only the resting state.This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
$CODEX_HOME/skills/figma-use-motion and restart Codex after major changes.Preferred MCP Server: Figma MCP Server
figma-use-motion outside its documented task boundary.Before claiming the figma-use-motion workflow succeeded:
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。
概要と使いどころ
AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and
日本語の概要は準備中です。原文の説明を表示しています。
12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
日本語の概要は準備中です。原文の説明を表示しています。