データ分析、機械学習、LLMアプリ、プロンプト設計（9,028 件）
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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。
Builds reactive UIs on Convex subscriptions: useQuery patterns, optimistic updates, pagination that stays live, presence, and avoiding subscription churn. Use when wiring the frontend to Convex, when data does not update live, or when a page rerenders too much.
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。
Builds AI agents on the Convex agent component: threads, messages, tools that call queries and mutations, streaming, RAG with vector search, and workflows for multi step jobs. Use when adding a chat assistant, tool calling agent, or retrieval feature to a Convex app.
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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
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。
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
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 when red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses
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。
State-first design pass — inventories and implements all non-happy states (loading, empty, error, partial, conflict, offline) before the happy path, and refactors impossible boolean state to proper state machines. Use when starting a new screen, reviewing an existing one for edge-case gaps, or when the user says "handle the error state" / "add loading states" / "what happens when data is missing". Invoke when the user asks for unhappy on their UI, or mentions 'unhappy' alongside design / UI / frontend 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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Get option chain data including calls and puts with strikes, bids, asks, volume, open interest, and implied volatility. Use when user asks about options, option prices, calls, puts, or option chain for a specific expiration date.
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。
Get option chain data from Interactive Brokers for equities, ETFs, and futures (FOP), including calls and puts with strikes, bids, asks, volume, implied volatility, and model Greeks. Use when user asks about options using IBKR data, futures options (NQ/ES/CL/GC...), or needs real-time option quotes from their broker. Requires TWS or IB Gateway running locally.
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。
Get fundamental financial data including financials, earnings, and key metrics. Use when user asks about financials, earnings, revenue, profit, balance sheet, income statement, or company fundamentals.
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。
Check whether a NYC building is landmarked or in a historic district using LPC data. Use when the user asks "is this building landmarked", whether a site sits in a historic district, or whether LPC review applies. NYC only.
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 structured FF&E specs from a list of product URLs into a schedule. Use to pull or bulk-import product-page data; not for PDF catalogs.
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。
Normalize supplied FF&E information and record accepted document-job inputs with exact product tags and source hashes. Use for intake or corrections; reusable library saves and schedule adoption go to their record owners.
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。
Clean a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product data.
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。
Compile NYC landmarks, DOB, ACRIS, HPD, and BSA findings into one property due-diligence report. Use for a full report; use individual NYC skills for one dataset.
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。
Parse a SIF (Standard Interchange Format) input into a readable preview and optionally append canonical product rows to the project's CSV library. Use when asked to convert or inspect SIF dealer data. For the reverse direction use /as:csv-to-sif.
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 responsive, readable charts with Recharts using correct data mapping and composition. Use whenever creating charts with Recharts — LineChart, BarChart, AreaChart, PieChart, wrapped in ResponsiveContainer with a fixed-height parent. Not for charts built with other libraries.
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。
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
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 an interactive PR review walkthrough as an HTML page. Fetches PR data via gh API, categorizes files into core vs mechanical changes, adds reviewer annotations, and renders diffs with moved-code detection. Use when the user pastes a GitHub PR URL and asks for a review, walkthrough, or summary, or says "review this PR".
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 when streaming durable workflow updates to a UI in real time — live order status pages that animate as steps complete, AI agent token streaming from a function to the browser, log tailing for long-running jobs, or human-in-the-loop approval flows that publish a prompt and wait for a user reply. Covers Inngest v4 native realtime: defining typed channels, publishing from inside step.run, minting subscription tokens via server actions, and consuming the stream from React/Next.js client components.
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 when adding cross-cutting concerns to durable functions — structured logging or tracing across all functions, error tracking with Sentry, payload encryption for sensitive data, dependency injection of clients (DB, Stripe, etc.) into function handlers, custom telemetry, or behavior that should apply uniformly across many functions. Covers Inngest middleware lifecycle, creating custom middleware, dependencyInjectionMiddleware, @inngest/middleware-encryption, @inngest/middleware-sentry, and custom middleware patterns.
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。