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概要と使いどころ
Generate templated responses for common legal inquiries and identify when situations require individualized attention. Use when responding to routine legal questions — data subject requests, vendor inquiries, NDA requests, discovery holds — or when managing response templates.
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When you want to integrate an external tool, API, MCP server, or service into a project — the wizard walks you through auth, config, env vars, client wrapper code, example usage, and an optional smoke test. Scoped to Next.js and Rails projects. Interactive Q&A — starts with the tool name, asks structured questions until the integration is specified, then scaffolds files. Examples — Stripe, Kit, Sanity, Notion, Neon, Supabase, Resend, Postmark, Twilio, Anthropic, OpenAI, Vercel Blob, custom internal APIs. For MCP servers, also handles the .mcp.json wiring. Triggers on "/toolify," "integrate X," "add X to this project," "wire up X," "set up the X integration," "hook up X," "connect X," "add MCP for X." Part of the -ify trifecta (skillify / toolify / loopify). NOT for adding new SKILL.md files — that's skillify. NOT for cron/agent loops — that's loopify.
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Use *before* starting work in a domain you don't know well, to surface the "unknown unknowns" — the things you don't even know to ask about — and learn just enough to prompt and decide well. Implements the "blind spot pass" pattern from Anthropic's Fable "finding your unknowns" field guide. Triggers when you say "I'm new to this", "I don't know what to ask", "teach me before we start", "blind spot pass", "unknown unknowns", "find my unknowns", or the Korean "내가 뭘 모르는지 알려줘" / "이 분야 처음인데" / "먼저 가르쳐줘" / "블라인드 스팟", or when you hand off a task while admitting you're a non-expert in that field (color grading, video editing, legal, tax, finance, design, an unfamiliar codebase, etc.). Do NOT use for simple factual questions, domains you already know, or directly-actionable work — answer those directly or route to planner / architect.
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。
Reusable approval-loop pattern for fanning out lead personalization across parallel Claude Code Task sub-agents. Shows the user 1 sample personalization, collects feedback, runs 10 more, approves, runs 10 more — stops when 2 consecutive rounds have zero edits, then scales to the full list. ALWAYS uses Claude Code Task tool sub-agents — never an external Anthropic/OpenAI API key. Use when any skill needs per-lead custom variables (situation lines, value lines, CTAs).
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。
Run a large-scale language migration with the six-step process: create the map and the rules, stress-test the rules, translate everything, compile, run it, match behavior. Use when the user wants to migrate, port, or rewrite a codebase from one language to another ("migrate this to Rust", "port our Python CLI to TypeScript", "should we rewrite this in Go?"), or asks for a migration feasibility assessment. Not for incremental JS→TS adoption — that needs no machinery — or single-file conversions.
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。
将需求拆解为结构化任务清单，生成长时运行 Agent 的任务管理系统（基于 Anthropic Effective harnesses 方法论）。当用户需要管理多会话开发任务、跟踪功能完成进度、或要求"拆解任务""任务管理""项目规划"时自动触发
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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。
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
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 should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my 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。
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
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。
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell 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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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。
Adapts an existing K-12 lesson (math, ELA, science, or social studies) for students at different proficiency levels (below / at / above grade level). Load this skill BEFORE asking the teacher any clarifying question about the lesson, tiers, or student levels. Triggers on explicit asks to differentiate, tier, or scaffold a lesson, and on implicit signals like "my students are at different levels". Produces 1 teacher-facing differentiation plan + 3 student-ready tier documents as editable Word documents in a single output turn, rendered from one material-source JSON via bundled scripts (shared content is written once so tiers cannot drift). Uses the Learning Commons Knowledge Graph when connected; works without it. This skill adapts a lesson the teacher brings or names. Not for creating a new lesson from scratch — a new-lesson request that asks for differentiated or leveled materials is k12-lesson-plan-creation's job, one package. Not for grading, rubrics, assessment feedback, or quizzes.
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。
Creates a Check for Understanding (CFU) for a math topic or standard: 1–3 targeted formative items whose distractors come from documented student misconceptions, plus a teacher guide routing each likely response to a specific next instructional step. Load BEFORE asking any clarifying question about grade, standard, or topic. This skill is for math; for other subjects, help as you normally would and don't mention the skill to the user. Triggers on asks to create, write, generate, or design a check for understanding, formative check, quick check, or exit ticket for a math concept or standard, and on implicit signals like "I need to see who got it before I move on." Not a quiz, test, or practice set — a CFU is read for what it reveals, not scored, so a numbered quiz or a bare answer key is a different job. Does not create a lesson (k12-lesson-plan-creation, which brings its own exit ticket), tier one (k12-lesson-differentiation), or prepare a teacher to teach one (k12-lesson-prep).
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。
Creates a lesson plan, student-facing materials, and observation template. Load this skill BEFORE asking the teacher any clarifying question about grade, subject, topic, standard, or timing. Use when a K-12 teacher needs a math, ELA, science, or social studies lesson built from scratch — even if grade, subject, or topic isn't yet stated. Do NOT load for grading, a rubric, assessment feedback, a quiz, or a standards lookup — answer those directly. Triggers on explicit requests (lesson plan, mini-lesson, unit plan, daily plan) and implicit teacher intent: "I'm teaching long division to 5th graders," "need to teach photosynthesis tomorrow." Core signal: teacher needs new instructional content created. A new lesson that asks for differentiated, tiered, or leveled materials is still ONE planning request — this skill produces those materials inside the lesson package; do not also invoke k12-lesson-differentiation. Not for differentiating an existing lesson (use k12-lesson-differentiation) or passage rewrites.
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。
Helps a teacher prepare to teach an existing K-12 lesson (math / ELA / science / social studies) — a prep partner that thinks through the lesson's key student task with the teacher and leaves a short teacher-only prep note. Triggers on asks to internalize, prepare for, get ready to teach, or understand a lesson, including implicit ones ("help me get ready for tomorrow's lesson"), and right after another skill built a lesson, which it can follow. Not for building or adapting a lesson, grading, rubrics, assessment feedback, or generating quizzes or student-facing material, and not for single-fact lookups a sentence would answer ("what standard is this?", "how long is this lesson?").
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。
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
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 this skill to get documentation for third-party APIs, SDKs or libraries before writing code that uses them to ensure you have the latest, most accurate documentation. This is a better way to find documentation than doing web search. This includes when a user asks for tasks like "use the OpenAI API", "call the Stripe API", "use the Anthropic SDK", "query Pinecone", or any other time the user asks you to write code against an external service and you need current API reference. Fetch the docs with chub before answering, rather than relying on your pre-trained knowledge, which may be outdated because of recent changes to these APIs. Be sure to use this skill when the user asks for the latest docs, latest API behavior, or explicitly mentions chub or Context Hub. Ensure `chub` is available, run `chub --help`, then follow the instructions there.
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。
将任意文本、网页或 URL 生成符合 Claude/Anthropic 设计语言的 HTML 信息卡片，通过 Playwright 截图为 PNG。 支持 14 种格式：平台封面（公众号、视频号、B站、抖音）、图文内容卡（小红书、教程、对比分析）、 社交分享卡（金句、数据、方形）、长文编辑排版（Broadsheet、Feature、Reader、Digest）。 当用户提到「信息卡、卡片、封面、图文笔记、排版、截图、生成图、内容卡」时使用本技能。
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。
Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.
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。
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
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。
Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).
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 billable ICD-10-CM diagnosis codes from a clinical note the way a professional coder builds the claim. Use when users say "code this encounter", "assign ICD-10 codes", "what diagnosis codes apply", "code this chart", or when turning clinical documentation into claim-ready diagnosis codes.
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 plain text from a document file - PDF, DOCX, XLSX, PPTX, RTF, or plain text/markdown/HTML. Use when a binary document needs to be turned into text, for example a contract PDF or an EHR DocumentReference attachment. Other skills (fhir) invoke scripts/extract.ts directly; the contracts MCP server bundles its own copy (servers/documents/src/extract.mjs) so its bundle stays self-contained — port fixes to both.
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。