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Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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Models what a competitor will do next and what happens if you respond - best response, equilibrium, and the difference between a rival who optimises perfectly and one who does not. Use before a price change or price war, when a competitor launches something, when deciding whether to match a rival's move, when analysing a market with few players, or when someone asks "what will they do if we do this".

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 past decisions into calibrated judgment - scoring old predictions against what happened, assigning credit for delayed results, and separating a bad decision from a good decision that lost. Use during a retrospective, postmortem, quarterly or monthly review, when reviewing decisions taken 30+ days ago, when attributing a result to a cause, or when someone claims to have called something.

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。

Universal desktop application controller for Windows. Automate any desktop app — send messages, click buttons, type text, take screenshots, and interact with native Windows applications. Use this skill when the user asks to: "control my computer", "automate desktop", "send a message on [any chat app]", "click on [something]", "type into [app]", "take a screenshot", "操控电脑", "自动化桌面", "给某某发消息", "打开某某软件", "截个屏", or any task involving interacting with Windows desktop applications. WeChat triggers: "send a WeChat message", "message someone on WeChat", "打开微信发消息", "给某某发微信", "微信发送", "微信发消息", "用微信发", "帮我发微信". Email/Web triggers: "打开邮箱", "发邮件", "send email", "open browser", "操控浏览器", "网页操作". Supports: WeChat, WeCom, DingTalk, Feishu/Lark, QQ, Telegram, Slack, Teams, any browser-based web app, and any other Windows desktop application. Inspired by OpenAI's playwright-interactive skill, combining code-based automation with visual screenshot feedback loops. ROUTING: For web/Electron apps (email, Slack, browser), prefer Playwright with CSS selectors for speed and precision. For native desktop apps (WeChat, QQ, DingTalk), use Win32 API automation.

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Explain a topic like I'm a 5 year old, as a single HTML artifact built from big pictures and very few words. Use this whenever the user asks for a dead-simple explanation of how something works, says "ELI5", "explain like I'm five", "簡単に説明して", "図で説明して", "初心者向けに", "素人にもわかるように", wants to explain a concept to a non-technical manager, customer, or family member, or asks for a visual explainer — even if they don't say "ELI5" explicitly.

Author standardized AI cheatsheets in the awesome-cheatsheets format

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Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.

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。

Interactive English conversation practice partner for Japanese learners. Use this skill whenever the user wants to practice speaking English, role-play a real-life situation (hotel check-in, restaurant, airport, business meeting, technical discussion, code review, job interview, daily small talk, etc.), discuss a technical article in English, or asks how to phrase something naturally in English. Trigger on requests like 'let's practice English', 'I want to talk in English', 'role play', 'pretend you are a ___', 'discuss this article in English', 'how do I say X in English'. 英会話練習・ロールプレイ・英語表現の質問のためのスキル。「英会話したい」「英語で話そう」「ロールプレイ」「ホテルのチェックイン練習」「この記事について英語で話したい」「xxって英語でどう言う？」などのリクエストで起動する。ユーザーは音声入力で会話する想定。会話前に想定問答・頻出表現・単語を提示し、会話を開始、最後に添削まで行う。

- Entry point for open-source intelligence, investigation and verification work. Use when asked to investigate, research, verify, vet, check out, look up, background-check, trace, attribute or find someone or something; when a request involves due diligence, KYC or KYB, counterparty or vendor risk, sanctions and PEP screening, AML, fraud, business email compromise

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Get external links to your repos through other people's curated surfaces: awesome-lists, tool catalogs and vendor showcases. Measures each list's merge queue before submitting, so nothing goes into a dead queue. Triggers: /borrowed-audience, /ba, borrowed audience, get external links, submit to awesome lists.

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。

URL・画像・テキスト要件からAIコーディングエージェント向けのDESIGN.mdを生成するスキル。「example.comをDESIGN.md化して」「このサムネをDESIGN.md化して」「クールでポップなLP用のDESIGN.mdを作って」といったリクエストで使用する。Claude Code / Cursor / Stitch に渡せば一貫したUIを生成できる、getdesign.md / awesome-design-md 準拠の9セクション構成のデザイン仕様書を出力する。CSS解析・画像目視抽出・要件生成の3パターンに対応。

Audit, grade, rewrite, and issue funny evidence-backed tickets for generic AI-slop patterns in prose, social posts, UI/design, and code. Use when asked to de-slop writing, humanize AI copy, grade a draft, review a landing page or generated interface, remove vague claims and repetitive rhetoric, inspect AI-written code for generic abstractions and happy-path-only logic, or ticket someone for a specific quality offense. Do not use to determine whether AI authored something.

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Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes,

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When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.

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。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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。

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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。

Navigate the gap between what you can see and what you can make. The Ira Glass gap. Use when a designer is frustrated by the distance between their taste and their output, when mentoring someone through a creative plateau, or when someone is considering quitting because their work doesn't match their vision.

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。

Generates IEEE-format LaTeX technical reports from project requirements and codebase evidence, using academic prose, pseudocode-only explanations, BibTeX references, and IEEEtran formatting.

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Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.

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。

Vendor (copy-import) a single external Claude Code skill into your own skills repo correctly — with license compliance and full provenance/attribution. Deterministic, idempotent primitive: given an upstream ref (owner/repo/path@sha or a GitHub URL) it fetches the skill, detects and gates on the license, copies the folder, injects a provenance frontmatter block (license + metadata: author/source/upstream_commit/vendored_at/provenance), ships the upstream LICENSE, adds a ## Credits section, and records a CREDITS.md ledger entry. Use when the user asks to "vendor a skill", "import a skill", "clone a skill", "borrow someone's skill", "add an external skill with attribution/credit", or スキルを取り込む / vendoring / 他人のスキルを自分のリポに取り込む. This is the import primitive that a skill-scout/curator orchestrator calls; it does NOT discover or evaluate — it correctly imports ONE skill you already chose.

Use this skill whenever helping someone build, design, or troubleshoot layouts in Elementor Pro V4 using the visual editor. Triggers on ANY mention of Elementor, page builder, Flexbox widget, Div block, section layout, widget configuration, responsive breakpoints, dark mode, scroll animations, Interactions tab, Variables Manager, Class Manager, Custom Code snippets, or any visual design work inside the Elementor panel. Also trigger when someone asks how to position elements, set spacing, apply typography, configure dark mode, prevent FOUC, or build scroll-triggered effects in Elementor. ALWAYS use this skill before suggesting any Elementor V4 implementation — it contains critical V4-specific panel structure, common AI mistakes to avoid, and production-tested patterns.

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Build circular genome visualizations using circlize (R), pyCirclize (Python), or Circos (Perl CLI) with ideogram tracks, multi-data tracks (scatter, histogram, heatmap), chord/link arcs for interactions, and explicit circos.clear() between plots. Covers when circular is appropriate vs when Cartesian wins (Cleveland-McGill 1984), karyograms, and chromosome adjacency in chord diagrams. Use when adjacency on the circle conveys meaning — chromosome-level overview, structural variants, Hi-C interactions, cross-genome comparisons.

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。

Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq -> MiXCR analyze preset -> VDJtools/immunarch depth-normalized diversity and overlap -> figures; 10x paired VDJ -> MiXCR 10x preset or Cell Ranger -> scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR -> exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -> somatic hypermutation makes exact clonotypes wrong -> Immcantation distToNearest/findThreshold clonal clustering, germline reconstruction, SHM, Dowser lineages); selecting the MiXCR 4.x preset by chemistry and activating its license; downsampling to equal depth before comparing diversity or overlap; and optionally annotating antigen specificity.

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。