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概要と使いどころ
Turn a request for a report or a dashboard widget into something buildable: the question it answers, the metric and its definition, where the data comes from, the query shape, and what it will not answer. Use when a new report or dashboard is requested, when an existing one needs a metric added or changed, when a widget is asked for in a sentence and needs pinning down, or when two reports disagree about the same number.
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Write the SQL or MongoDB query that answers a question and keeps answering it: the right grain, no fan out, parameterised window, checked against something known. Use when a number is needed that no report carries, when an existing query returns something that looks wrong, when a query is slow or expensive, or when somebody asks for a one off pull.
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 up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).
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 monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.
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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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。
CEO/founder-mode scope review. Challenges the scope of any plan, workflow, or feature before execution. Four modes: SCOPE EXPANSION (dream bigger), SELECTIVE EXPANSION (cherry-pick worthwhile expansions), HOLD SCOPE (maximum rigor within current scope), SCOPE REDUCTION (strip to essentials). Use before executing any multi-step workflow or when questioning whether the scope is right. Prevents scope creep and missed opportunities. Use proactively when the user defines a plan, creates a workflow, or says "is this enough?" or "am I thinking too small?".
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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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。
Opinionated house conventions for NestJS backends — module boundaries and layering, provider wiring and injection scopes, guards/interceptors/pipes/filters, DTOs and validation, domain exceptions, config, and the Nest 11 / Express 5 behaviour that compiles cleanly and fails at runtime. Use whenever the code is NestJS (`@nestjs/*` in package.json, or `@Module`/`@Injectable`/`@Controller`/`@Cron` in the file), including when the question sounds like plain TypeScript, an OOP refactor, a code review, or an ops problem — the Nest-specific answer differs from the generic one. Typical asks: scaffolding a module, adding a controller, provider, or endpoint, wiring an enhancer, a boot-time DI or circular-import failure, a response leaking entity fields despite `@Exclude()`, a cron job firing on every replica, upgrading NestJS, or reviewing a Nest diff. Not for plain Express or Fastify, Angular, frontend, SQL, or CI.
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。
Discover which AI prompts and topics matter for a brand's Answer Engine Optimization (AEO) using only free tools. Crawls a website, analyzes the brand's positioning, generates prioritized prompts people ask AI assistants, and audits existing content coverage — all without paid APIs. Use when a user wants to: find what questions people ask AI about their industry, discover AEO opportunities, research prompts for content creation, audit a site's AI visibility, or build an AEO content strategy. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning 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。
Answer ads operations questions quickly for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads 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。
Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations
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。
Automatically assess task complexity and adjust reasoning level. Triggers on every user message to evaluate whether extended thinking (reasoning mode) would improve response quality. Use this as a pre-processing step before answering complex questions.
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 and implement adaptive testing systems using Item Response Theory (IRT). Use when working with computerized adaptive tests (CAT), psychometric assessment, ability estimation, question calibration, test design, or IRT models (1PL/2PL/3PL). Covers test algorithms, stopping rules, item selection strategies, and practical implementation patterns for K-12, certification, placement, and diagnostic assessments.
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。
Verify and write proofs using the Acorn theorem prover for mathematical and cryptographic formalization. Use when working with Acorn proof files (.ac), verifying theorems, formalizing mathematical or cryptographic protocols, or writing proofs in the Acorn language. Triggers on: (1) Creating or editing .ac files, (2) Running acorn verify commands, (3) Formalizing math or crypto proofs, (4) Questions about Acorn syntax or standard library.
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 synthetic code context for LLM coding tasks. Automatically use this before making code changes, fixing bugs, refactoring, or answering questions about the codebase. Provides execution paths, relevant files, symbols, tests, and code snippets.
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。
Uses the local pruner indexer to gather relevant code context before making changes, debugging, refactoring, or answering questions about this repository.
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。
Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.
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。
Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cross-team coordination. Ask two core questions - Why? (YAGNI check) and Simpler? (KISS check) - to ensure clarity before coding.
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 daily standup/meeting update generator. Use when user says 'daily', 'standup', 'scrum update', 'status update', 'what did I do yesterday', 'prepare for meeting', 'morning update', or 'team sync'. Pulls activity from GitHub, Jira, and Claude Code session history. Conducts 4-question interview (yesterday, today, blockers, discussion topics) and generates formatted Markdown update.
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 signal-based reactive state management in Angular v20+. Use for creating reactive state with signal(), derived state with computed(), dependent state with linkedSignal(), and side effects with effect(). Triggers on state management questions, converting from BehaviorSubject/Observable patterns to signals, or implementing reactive data flows.
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。
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
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。
Ask Claude, ChatGPT, and Gemini a question and capture the full trace behind the answer (the search queries each engine ran, the pages it retrieved, and the sources it cited, span by span). Use when asked to "trace an AI answer", "see what AI engines cite", "check AI citations", "which sources does ChatGPT/Claude/Gemini use", "why does the AI recommend X", or for any GEO (Generative Engine Optimization) measurement.
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。
One-shot GEO visibility check for a brand using Xpoz. Runs the buyer questions that matter through Claude, ChatGPT, and Gemini, and reports where the brand appears, who wins instead, and which surfaces the answers are assembled from. Use when asked to "check my AI visibility", "does ChatGPT recommend us", "GEO audit", "where do we show up in AI answers", or "who wins our category in AI answers".
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。
Reference guide for using Xpoz effectively. Load this skill whenever working with Xpoz MCP tools, SDKs, or CLI — it ensures correct query syntax, optimal field selection, proper pagination, and best practices for every Xpoz interaction. Covers authentication, query syntax (boolean operators, date filtering), response modes (fast/paging/CSV), field selection, tracking setup, and all platform tool references (Twitter, Instagram, Reddit, TikTok). Use for ANY Xpoz-related work, not just explicit best-practices questions.
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。