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。
**MANDATORY prerequisite** — you MUST invoke this skill BEFORE calling the `get_design_context` Figma MCP tool. You MUST trigger this skill whenever the user wants to implement, build, port, or code up a Figma design as code. Example prompts (not exhaustive) are 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'design to code'. This skill provides critical instructions and steps to the agent on how to correctly implement Figma designs in code and must NOT be skipped.
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。
Use this skill to turn a Figma design into code in a target codebase. This is the read-FROM-Figma direction: pull design context out of Figma with get_design_context, then adapt it into the project's real stack. For the reverse direction — building or updating a design in Figma from code — use figma-generate-design instead.
This skill owns the workflow for design-to-code. Parameter mechanics (nodeId / fileKey / branchKey extraction, URL parsing, format/query options, response shape) live on the get_design_context tool description itself — follow them there.
Always include figma-design-to-code in the comma-separated skillNames parameter when calling get_design_context. If this skill was loaded via an MCP resource, you MUST prefix the name with resource: (e.g. resource:figma-design-to-code). This is a logging parameter used to track skill usage — it does not affect execution.
get_design_context on the target node before writing any code. It is your primary tool — a single call returns reference code, a screenshot, and contextual hints.get_metadata or get_screenshot as a substitute. Use them only to orient (e.g. picking a node) or to validate, not in place of get_design_context.Apply the hints in this order — earlier sources override later ones:
Images and icons come back as <img> elements whose src is a remote asset URL (https://.../api/mcp/asset/...). Apply these rules as you write the code:
<svg>/<path>, never author your own icon file, never drop an icon or leave a placeholder — you don't have the real vector data, so anything you draw is wrong.src for an immediate render, but it expires in ~7 days — so for code you'll commit, download-and-commit the exact asset bytes, or wire a dynamic content image to the project's data source (API, CDN, or props). Never a file whose contents you authored.size-[24px], overflow-clip) with BOTH width and height set, and size the leaf <img> to fill it (100% or fixed px) — never auto, which blows the image up to its intrinsic size.get_design_context error, STOP and read the message before retrying.node-id (a file-only URL), ask the user for a node-specific URL — You MUST NOT guess or pass an empty nodeId.get_design_context can still provide context.This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
$CODEX_HOME/skills/figma-design-to-code and restart Codex after major changes.Preferred MCP Server: Figma MCP Server
figma-design-to-code outside its documented task boundary.Before claiming the figma-design-to-code workflow succeeded:
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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。