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組織再編やツール移行、戦略転換を進めるため、告知から研修、定着確認までの流れを整理します。変化への反発や不安、変更が重なることによる疲れへの対応も検討します。

• ツール移行の告知と研修を計画したいとき
• 組織再編の説明文や社員向けFAQを準備
• 戦略転換とチームへの影響を説明したいとき

顧客の継続率と解約要因を分析し、顧客層ごとの支援配分や担当体制、採用順序を設計します。スタートアップの顧客維持戦略とCS組織づくりを支えます。

• 継続率と解約理由を見直したいとき
• 顧客層別の支援配分を決めたいとき
• CS担当者の必要人数を見積もりたいとき

本や記事、PDFを読み込み、著者の主張と根拠、前提や反論を区別して整理します。知識マップや思い出して説明する練習を通じて、理解の抜けも確認するスキル。

• 記事の主張と根拠を確かめたいとき
• 概念の関係を知識マップにしたいとき
• 思い出して説明し理解の抜けを探したいとき

C#とWinUI 3によるWindowsデスクトップアプリの作成・改善を支援します。開発環境の確認から画面設計、実装、ビルドと起動確認、不具合の調査まで扱います。

• WinUI開発環境の確認と準備
• Windowsアプリを新規作成したいとき
• 画面遷移やテーマ、可変幅レイアウトの改善

ChatGPT内で使うアプリのひな形作成や改修、不具合調査を支援します。公式資料とサンプルを基に、外部機能を呼び出すサーバーと操作画面を組み立て、動作確認まで進めます。

• 公式例からアプリのひな形を作りたいとき
• ツール設計とサーバー・画面の接続
• 既存Apps SDKアプリの改修や調査

JetBrains向けチャット画面の改修を支援するスキル。会話カードの表示やテーマ対応、状態管理、部品の再利用、更新時の動作テストの実装方針を示します。

• 会話カードの表示や開閉を改修したいとき
• エディターのテーマに表示を合わせたいとき
• セッションの状態やイベントを追加したいとき

Analyze and design pricing strategies including pricing models, competitive pricing analysis, willingness-to-pay estimation, and price elasticity. Use when setting prices, evaluating pricing models, preparing for a pricing change, or comparing freemium vs paid approaches.

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。

新製品の構想を、顧客・提供価値・成長方針などの戦略９項目と、費用・収益の事業モデルに整理します。実現に必要な仮説と、その検証方法までまとめるスキルです。

• 新製品のアイデアを整理したいとき
• 顧客への価値と競合との差を考えたいとき
• 費用・収益・価格設定の整理

製品や機能の収益化案を3〜5種類出し、顧客との相性、採算の見込み、リスクを比較します。支払い意向を確かめる小さな実験を設計し、先に試す案を絞り込むスキル。

• 新製品の収益モデルを探したいとき
• 定額課金と従量課金の比較
• 支払い意向を確かめる実験の設計

Set up OpenWork for a team - sign up for OpenWork Cloud, create the organization, invite teammates, share models through the AI Gateway, and share a skill. Use when a team or company wants OpenWork with shared skills, models, or policies.

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。

Reproduce a known chat state in the running desktop on demand (model provider errors and retries, Code Mode and Den script failures, parallel tool steps, app creation, sequential commands) and check how it renders. Use when fixing or designing chat error, retry, tool-step or app-card UI.

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。

Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with this.helpers / the $helpers global, working with dates using DateTime, troubleshooting Code node errors, choosing between Code node modes, or doing any custom data transformation in n8n. Always use this skill when a workflow needs a Code node — whether for data aggregation, filtering, API calls, format conversion, batch processing logic, or any custom JavaScript. Covers SplitInBatches loop patterns, cross-iteration data, pairedItem, and real-world production patterns. Also use when asked why a Code node or workflow is slow, which execution mode is faster, or how to cut per-item overhead on large datasets. EXCEPTION — for the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode, a tool attached to an AI Agent), use the n8n-code-tool skill instead; it has a different runtime contract.

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。

Configures Google Cloud API Gateway to act as a remote Model Context Protocol (MCP) server (Public Preview) that exposes REST APIs as agent tools over HTTP (POST /mcp). Use when migrating OpenAPI 2.0 specs to OpenAPI 3.x for MCP, annotating specs with x-google-api-management.mcp and x-google-mcp-tool, securing tools/list with JWTs or API keys, connecting Google ADK (McpToolset) or Apigee API hub and Agent Registry, or troubleshooting MCP JSON-RPC errors (-32601, -32602, -32000). Do not use for Apigee X proxy bundles, outbound LLM model routing, or GKE Gateway API.

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。

Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to BigQuery on Google Cloud. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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。

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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。

Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine). Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).

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。

Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.

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。

SDFormat/SDF model and world authoring, validation, and simulator handoff. Use for `.sdf` files, SDFormat XML, models, worlds, links, joints, poses, frames, inertials, visual/collision geometry, mesh URIs, sensors, lights, physics, plugins, includes, Gazebo, static SDF review, or simulator-specific metadata. Do not use for signed-distance-field geometry. Open and visually review existing SDF files in CAD Viewer.

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。

Drive OpenAI's Codex CLI (`codex exec`) as a non-interactive coding sub-agent from inside Claude Code. Load WHENEVER you want to delegate a coding/analysis/refactor task to Codex, get a second opinion / adversarial review from another model, fan out parallel agents across files or worktrees, or run a long mechanical job while you stay the planner. Covers the exact `codex exec` flags, sandbox tiers, output capture, JSON/schema modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.

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。

Structural analysis of complex systems — Iceberg model, Causal Loop feedback diagrams, archetype matching, Meadows leverage points, and concept maps — grounded in the premise that behavior is generated by structure. USE WHEN systems thinking, causal loop, feedback loops, archetypes, leverage points, iceberg model, fix the system, why does this keep happening, recurring problem, second-order effects. NOT FOR incident causal chains (use RootCauseAnalysis).

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。

Execute any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. CLI used only for YouTube transcript (-y) and URL fallback (-u). Two workflows: ExecutePattern, UpdatePatterns. USE WHEN fabric, fabric pattern, run fabric, update patterns, threat model, analyze claims, improve writing, review code, mermaid, STRIDE, sigma rules. NOT FOR multi-agent investigation (Research) or content-adaptive extraction (ExtractWisdom).

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。

Operate a LangBot instance through its built-in MCP (Model Context Protocol) server. Use when an AI agent needs to manage LangBot — list/create/update/delete bots, agents, pipelines, models, knowledge bases, MCP servers, and skills — over MCP instead of raw HTTP. Covers the /mcp endpoint, API-key auth (web-UI lbk_ keys and the config.yaml global key), the tool surface, and client configuration. Triggers on "langbot mcp", "manage langbot via mcp", "langbot /mcp", "langbot 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。

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.

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。

飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。

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。