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Build generative UI with OpenUI — any LLM provider, any backend language. Scaffold, integrate, validate.

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。

Add a new benchmark to sgl-eval by vendoring its NeMo-Skills dataset module and registering it in `_TABLE`. Use when the user asks to "add <benchmark>" / "support <benchmark>" / "register <benchmark>" inside the sgl-eval repo.

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 fantasy character names in zh-TW with original spelling and etymology, across human, elf, dwarf, orc/beastfolk, and halfling naming traditions. Use when the user needs a name for a character, NPC, party member, villain, shopkeeper, or monster, asks to think of, suggest, or rename such a character, names entities for a MUD/TRPG/game world or worldbuilding document, or writes lore, quests, presets, or seed data that require in-world personal names.

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。

Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.

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。

Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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 reversible compatibility-safe transitions. Use for schema, data, API, protocol, configuration, or dependency migrations requiring rollback and preservation proof.

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 Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement. Don't use for Google Analytics Admin API operations (e.g., creating properties, managing users) or for front-end tracking installation.

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 Google Analytics account and property settings, enables the Analytics Admin API via the Cloud CLI, lists accounts and properties, and manages data streams, custom dimensions, conversion events, and integrations. Use when you need to programmatically configure Google Analytics accounts, provision properties, manage data retention, configure Measurement Protocol secrets, or manage Firebase and Google Ads links.

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。

Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).

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。

Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).

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。

Guides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API. Don't use for implementing audience or event ingestion logic (use the data-manager-api-audience-ingestion or data-manager-api-event-ingestion skills instead).

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。

Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

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。

Resume work on the FIFA Skeletal Tracking Light 2026 challenge using vailá modules (SAM 3, soccer pitch keypoints, calibration, per-frame DLT). Use whenever the user reopens the project after closing the terminal, asks "where did we stop on FIFA?", or wants the next concrete commands to run from `~/data/vaila` with `.venv` already created.

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 datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and 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。

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Treino Ultralytics YOLO pose para keypoints do campo de futebol (32 kp), export ONNX, e inferência em vídeo com vaila.soccerfield_keypoints_ai (CSV getpixelvideo + overlay). Use quando o usuário fala em football pitch keypoints, YOLO26 pose, treino Roboflow/HF dataset martinjolif, field_keypoints_getpixelvideo, calibração FIFA, ou soccerfield_keypoints_ai.

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。

Scaffolds a structured investigation in scratch/ for empirical research and documentation. Use when the user says "start an investigation" or wants to: trace code paths or data flow ("trace from X to Y", "what touches X", "follow the wiring"), document system architecture comprehensively ("document how the system works", "archeology"), investigate bugs ("figure out why X happens"), explore technical feasibility ("can we do X?"), or explore design options ("explore the API", "gather context", "design alternatives"). Creates dated folder with README. NOT for simple code questions or single-file searches.

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 Opportunity Solution Trees using Teresa Torres' framework for structured product discovery. Use this skill when: - You need to map a desired outcome to opportunities, solutions, and experiments - You want to decide WHAT to build next based on user research and discovery data - You have workshop findings, pilot feedback, or user research to structure into actionable options - You need a visual tree diagram showing the path from outcome to testable experiments

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。

Synthesize pilot observations into structured debrief reports and spec updates. Use this skill when: - A pilot has completed and you have observation notes, tickets, or feedback transcripts - You need to turn messy pilot data into actionable spec changes - You want to generate edge case user stories from field observations - You need negative acceptance criteria from pilot bugs - You want a structured debrief report for stakeholder communication

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 weekly or monthly status updates from local PM data. Use this skill when: - It's end of week and you need a status update for leadership or stakeholders - You need a monthly executive summary - You want to draft an email update using data from meetings, tracker tickets, and decisions - The user says "weekly update", "status report", "what happened this week?", "weekly summary" - The user says "Friday update", "send status email", "EOW update" Pulls from work logs, meetings, ticket tracker, and decisions — no manual input needed.

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。

Update user personas from new feedback, pilot data, or research. Use this skill when: - New pilot data or feedback reveals changed user behaviors or pain points - Annual persona review is due (1-2x per year) - New user segments are discovered that need persona documentation - Existing personas need validation against recent 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。

Behavioral guardrails against the six most common LLM coding failure modes. Apply on every coding task: writing, editing, reviewing, or refactoring.

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。