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Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

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Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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。

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.

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。

Queries and retrieves active security findings, external exposures, toxic combinations, vulnerabilities, threats, and sensitive data risks from Google Cloud Security Command Center. Use when retrieving details for a security finding by its name, validating finding scope (e.g., verifying findingClass is TOXIC_COMBINATION, VULNERABILITY, EXTERNAL_EXPOSURE, or THREAT), or fetching finding details for triage. Don't use to draft remediations, apply patches, or execute configurations.

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。

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use google-cloud-recipe-*), Well-Architected Framework reviews or audits (use google-cloud-waf-*), or workloads covered by specialized solution skills.

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。

Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse 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。

Diagnoses and resolves Google Cloud Filestore client mount failures, permission errors (EACCES), and network timeouts (ETIMEDOUT). Use when an NFS mount hangs or fails from a Compute Engine VM, GKE pod, Cloud Run service, or Vertex AI workload, when `mount.nfs` reports "Connection timed out" or "access denied by server", when checking whether VPC ingress firewall rules or `nfsExportOptions` allow a client IP, or when a previously working Filestore share suddenly stops mounting after an administrative change. Don't use for Cloud Storage (GCS) buckets, Persistent Disk, or Cloud NetApp Volumes, and don't use for Filestore capacity scaling or backup and export-policy auditing.

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。

Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.

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。

Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

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。

Show real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.

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。

Ultra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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。

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` 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。

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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 reviewing or refactoring code that pulls apart another object's private representation, such as raw limb/byte/slot access, slice constructors, or helper methods that expose internal storage. Guides agents to find representation envy and suggest moving behavior onto the object that owns the representation, using live-image Genie tools when working in Pharo.

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。

Standalone testing skill — owns writing and running tests, verifying behavior, and two-pass TDD orchestration. Default mode analyzes changed scope and runs the relevant tests; `--unit`/`--integration`/`--e2e`/`--all` scope by test type; `--verify` reruns exactly the prior failures; `--all-phases <plan>` sweeps every completed phase for regression. `--tdd --prepare`/`--tdd --verify` bracket `ck:cook` for red-green TDD. Never edits production code, and blocks until a phase's `ck:quality` gate is APPROVED (except `--tdd --prepare`, which runs before any implementation exists).

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。

Full-pipeline orchestrator. Runs all 7 roles (BA → TechLead → PM → BE Dev → FE Dev → Tester → QA/QC) in sequence by loading and following each per-role skill. Uses pre_write_validator.py hook enforcement and each role's self-validation. Use per-agent commands ($team-ba, $team-techlead, etc.) for manual control.

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 a feature phase by phase from a phased JSON master or Markdown plan. Before each phase, confirm whether to create/run unit tests and whether to run ck:quality, unless explicit flags supply those choices. Supports token-budgeted execution, resumable state, and TDD handoff to ck:test.

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 a feature phase by phase from a phased JSON master or Markdown plan. Before each phase, confirm whether to create/run unit tests and whether to run ck:quality, unless explicit flags supply those choices. Supports token-budgeted execution, resumable state, and TDD handoff to ck:test.

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 complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.

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。

Read and reuse AI coding session context across Claude Code, Codex, Antigravity/agent hooks, Cursor adapters, and compatible AI tools. Use this skill whenever the user asks to read, inspect, resume, compare, or continue another session by name or session ID, asks what happened in the previous session, or starts a new session that should inherit the nearest prior context. The paired provider adapter automatically loads the nearest previous project session at startup and resolves explicit session-read 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。

Full-pipeline orchestrator. Runs all 7 agents (BA → TechLead → PM → BE Dev → FE Dev → Tester → QA/QC) in sequence by invoking each per-agent skill via the Skill tool. All writes go through the pre_write_validator.py hook for hard enforcement. Use per-agent commands (/team-ba, /team-techlead, etc.) for manual control.

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。