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概要と使いどころ
Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.
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。
CRO analysis for landing pages evaluating headline clarity, CTA placement, trust signals, mobile friction, and conversion killers. Use when uploading a landing page screenshot, pasting a URL for review, requesting conversion rate optimization feedback, or auditing page effectiveness prioritized by impact. Platform: Google and Meta.
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。
Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.
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。
Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.
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。
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
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。
Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern detection
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。
Two-stage paper screening - abstract scoring then deep dive for specific data extraction
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。
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。
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。
Use parallel subagents for large-scale paper screening and deep dive analysis
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。
Introduction to literature search & review skills - systematic paper finding, screening, extraction, and citation traversal
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。
给 AI 短剧写剧本：把 novel-outline 的分集梗概落成结构化的场次 + 节拍流（动作节拍与台词行交替）， 台词逐句带说话人与语气，时长逐集按语速确定性折算。产出 script.json + Markdown + 单页评审报告 （时长仪表 / 分集剧本 / 场次总表 / 按角色聚合的台词本，含导出 JSON）。 剧本管戏，分镜管拍——本 skill 不分镜头、无镜号、不写生成提示词，那些是下一层分镜 skill 的活。 10 道质量门全部由脚本确定性检查（每集时长 ±15%、单句 ≤35 字、说话人合法、钩子悬念落纸、 钩子具象前 3 拍内兑现（hookBeat 认领冷开场）、每场至少一个动作节拍、爽点认领、 角色/场景/光照/道具对账上游……）。 有 outline.json 就用 seed 预填每集骨架；给 --art 连光照状态一起对账。 零依赖、零 API key，用当前会话额度。 Use when asked to 写剧本、出剧本、台词、场次、写戏、screenwriting for AI short drama。
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。
Turns a thread, skill, spreadsheet, or Claude/ChatGPT project into a polished, visual Agent-Native app: populated domain screens with the in-app agent working behind contextual controls. Use when a user invokes `/turn-into-app`, or asks to turn a workflow, skill, spreadsheet, or project into an app, UI, workbench, or dashboard, including from Claude or ChatGPT on the web.
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。
Open and operate Agent-Native workspace apps through Dispatch MCP, with inline app surfaces, browser-session authentication, and current screen state.
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。
Render a target GameObject from a chosen camera angle with optional layer-based isolation, configurable background (solid/skybox/transparent), multi-light setup via JSON, and Composite (2x2 Front/Right/Back/Top) mode. Returns a PNG image. When isolated=true, inactive children may briefly fire OnEnable — see the body for side-effect notes.
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。
Use the Browser Automation BB plugin to inspect and automate persistent browser pages in an explicit desktop or local headless session. Use for browser navigation, snapshots, clicking, forms, and verification screenshots.
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。
Starter: one-screen provider health summary — availability, auth method, version drift, and cost posture for every seatable provider
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。
Reverse-engineer design systems, tokens, and components from live products or screenshots
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。
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
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。
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work.
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。
Research online purchases, prices, and shop trust. Use for buying advice, subscriptions, or shopping photos, links, and checkout screens.
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。
Automates Apple-platform apps (iOS, tvOS, macOS) and Android devices. Use when navigating apps, taking snapshots/screenshots, tapping, typing, scrolling, extracting UI info, collecting logs/network/perf evidence, or planning agent-device CLI commands.
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。
Use XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP tools.
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。
Best practices and example-driven guidance for building SwiftUI views and components, including navigation hierarchies, custom view modifiers, and responsive layouts with stacks and grids. Use when creating or refactoring SwiftUI UI, designing tab architecture with TabView, composing screens with VStack/HStack, managing @State or @Binding, building declarative iOS interfaces, or needing component-specific patterns and examples.
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。
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
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。