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概要と使いどころ
Use when a project needs shared terminology, glossary updates, ADRs, clearer domain language, or when user language conflicts with existing code, ticket, board, project, workflow, or CONTEXT.md terms.
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 internal AI governance policies from scratch. Covers acceptable use, model selection, data handling, vendor contracts, compliance mapping, and board reporting.
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。
Complete community building, engagement, and monetization system. From zero to thriving community — launch strategy, member engagement loops, content programming, moderation frameworks, growth tactics, monetization models, and health metrics. Works for Discord, Slack, Telegram, Circle, forums, or any platform.
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。
Turn clients into long-term revenue engines. This isn't advice — it's a complete operating system with scoring models, templates, playbooks, and automation patterns that work for any B2B or B2C sub...
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。
Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier (with grounding) as primary method, web search as fallback. Use when a user wants to: check if AI models mention their brand, track AI citation changes over time, measure AEO content effectiveness, monitor competitor AI visibility, or audit their brand's presence in AI-generated answers. Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free (creates/refreshes content). This skill closes the loop by measuring results.
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。
Analyze what search queries Gemini uses when answering a prompt, by running it multiple times with Google Search grounding and reporting frequency distribution. Use when investigating AEO query patterns, understanding how AI models search the web for a topic, or studying the probabilistic nature of AI-triggered search queries.
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。
Create or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).
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。
Generate professional advertising images from product URLs using the Ad-Ready pipeline on ComfyDeploy. Use when the user wants to create ads for any product by providing a URL, optionally with a brand profile (70+ brands) and funnel stage targeting. Supports model/talent integration, brand-aware creative direction, and multi-format output. Differs from Morpheus (manual fashion photography) — Ad-Ready is URL-driven, brand-intelligent, and funnel-stage aware.
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。
Compare models with A/B testing for selection
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。
Other tools need perfect images. CellCog turns ideas into 3D models. From rough sketches, text descriptions, or product photos — production-ready GLB files for games, AR/VR, e-commerce, and 3D printing.
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, implement, and review XState v5 state machines and @xstate/react integrations in TypeScript. Use when modeling finite workflows, async actors, statecharts, guarded transitions, delayed transitions, actor systems, or machine-driven React behavior.
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。
案件预算和持续的 WIP/偏差监控。在案件设立时构建分阶段费用估算，按法域或工作流运行自下而上预算，计算应急准备金，并构建 AFA 安排（固定费用、上限费用、分阶段固定费用）。持续监控：对照预算的 WIP 跟踪、比例性评估（支出与进度）、带根本原因分析的偏差评述、完成预测、变现率监控、核销分析。触发词：'build a budget'、'fee estimate'、'what will this cost'、'WIP review'、'budget vs actual'、'how are we tracking against budget'、'we're over budget'、'realisation is poor'、'what's our ETC'、'budget for the German workstream'、'model the financial impact of this scope change'、'draft a fee adjustment'、'write-off analysis'、'how much contingency'、'AFA structure'、'fixed fee estimate'、'budget update'、'forecast to complete'。
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。
Delegate implementation to Codex or to a sandboxed Claude Code (`claude -p`), have a model from the other vendor review the diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants Codex or a sandboxed Claude to write code, mentions handing off / delegating implementation to one of those backends, asks for a cross-vendor review of delegated code, asks to work through a plan or spec unit-by-unit with an implementation backend doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer runs in the real environment behind backend-specific git and publication boundaries, must not be pointed at a full test suite by default, and its self-report is a claim rather than evidence. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the engineering-mode skill when it is installed.
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。
Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).
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。
AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports.
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 tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.
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 or extend a dygo Business App using the supported project layout, generators, metadata, SDK, and validation workflow. Use for general app work that is not primarily Entity modeling, access, hooks, Jobs, fixtures, or patches.
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。
Review a dygo Business App for framework conventions, model quality, access control, SDK boundaries, observability, and safe lifecycle behavior. Use for App audits and pre-merge reviews, not ordinary 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。
Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".
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 configure Microsoft Entra app registrations for secure authentication and authorization. Use this skill when users need OAuth, OpenID Connect, API permissions, app roles, or multi-tenant identity. Covers auth flows, credentials, API permissions, consent models, and zero-trust 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。
Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.
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 high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.
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。
Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, price options with ML models, and synthesize balanced news perspectives via the Helium MCP server.
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。