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。
This skill helps agents use Figma's use_figma MCP tool in the FigJam context. Can be used alongside figma-use which has foundational context for using the use_figma tool.
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。
This skill contains FigJam-specific context for the use_figma MCP tool. The figma-use skill provides foundational context for plugin API execution via MCP as well as the full Figma plugin API for more advanced use-cases that are not described here.
Always include figma-use-figjam in the comma-separated skillNames parameter when calling use_figma for FigJam operations. If this skill was loaded via an MCP resource, you MUST prefix the name with resource: (e.g. resource:figma-use-figjam). This is a logging parameter used to track skill usage — it does not affect execution.
FigJam URL is
figma.com/board/.... Do NOT callfigma.createPage()in FigJam — it throwsTypeError: figma.createPage no such property 'createPage' on the figma global object.createPage()is a Design-file API only (figma.com/design/...). FigJam files have a single implicit page; organize content with sections instead (see create-section).
get_figjam is the inspection tool for FigJam files. It returns the full node tree as XML, including IDs of pages, sections, stickies, connectors, and other nodes you need to reference in subsequent use_figma calls.
get_figjam upfront before writing any use_figma code that needs to reference existing nodes (page IDs, section IDs, etc.). Don't try to discover IDs by running an inspection script — console.log output from use_figma is not returned to the agent (see figma-use Critical Rule #4). Only the return value comes back.get_metadata does NOT work on FigJam files — it is design-mode only and will fail immediately with "unsupported for FigJam files".get_screenshot requires a valid nodeId — passing an empty nodeId returns "invalid nodeId" error. Get IDs from get_figjam first.return an ID from a previous use_figma call and need it now, call get_figjam rather than re-running an inspection script.Load only the references your task needs — but when you do need to load multiple, issue all reads in a single parallel tool-call batch, not sequentially across turns. For a typical board-creation task, that means a single message containing reads for plan-board-content plus the 3-4 specific node-type references you'll use.
The Figma MCP tools (use_figma, get_figjam, get_screenshot, get_metadata, create_new_file, whoami) often appear as deferred tools that require ToolSearch to load their schemas before they can be called. Load all schemas in a single ToolSearch call using the select: syntax instead of one call per tool:
ToolSearch query="select:use_figma,get_figjam,get_screenshot,get_metadata,create_new_file"
Six sequential ToolSearch calls is six round trips before any work happens. One batched call is one round trip.
Every FigJam text mutation (sticky/shape/label/table cell/connector text, standalone text nodes) follows the same recipe as Design files: load font → await → mutate → return affected IDs. Skipping the load throws Cannot write to node with unloaded font "<family> <style>". See figma-use → gotchas.md → Canonical text-edit recipe. FigJam-specific note: sublayer defaults vary (sticky → Inter Medium, shape → Inter Medium, connector → invalid until set), so always load from node.text.fontName rather than hardcoding { family: 'Inter', style: 'Regular' }.
upload_assets is the ONLY supported way to add images to a FigJam file. Do NOT use figma.createImage() or figma.createImageAsync() from inside use_figma — they are unsupported as image-upload entry points in FigJam. Call upload_assets with the FigJam fileKey; the tool returns single-use upload URLs that you POST raw image bytes to, and the image is committed and placed automatically. Pass nodeId (with count: 1) to attach the upload to an existing FigJam node as a fill; omit nodeId to drop the image onto the board as a new layer.
For the full request/response shape, see figma-use → api-reference.md → Images.
hex/255 notation rule and the h() helperThis skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
$CODEX_HOME/skills/figma-use-figjam and restart Codex after major changes.Preferred MCP Server: Figma MCP Server
figma-use-figjam outside its documented task boundary.Before claiming the figma-use-figjam 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。