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概要と使いどころ
Analyze the NTFS Master File Table ($MFT) with MFTECmd, analyzeMFT, and X-Ways Forensics to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space. Use when recovering evidence of deleted files, reconstructing NTFS file-system timelines, or detecting anti-forensic timestomping during a Windows forensic examination.
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
Send a team announcement via both Gmail and a Google Chat space.
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。
Subscribe to Google Workspace events.
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, organize, and distribute content across Workspace.
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。
Google Chat: Manage Chat spaces and messages.
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。
Google Workspace Admin SDK: Audit logs and usage 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。
Google Workflow: Announce a Drive file in a Chat space.
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。
Google Chat: Send a message to a space.
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。
Administer IT — monitor security and configure Workspace.
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。
Brainstorm product ideas, explore problem spaces, and challenge assumptions as a thinking partner. Use when exploring a new opportunity, generating solutions to a product problem, stress-testing an idea, or when a PM needs to think out loud with a sharp sparring partner before converging on a direction.
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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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, manage, and optimize monorepos. Covers Turborepo, Nx, pnpm workspaces, and Lerna. Cross-package impact analysis, selective builds/tests on affected packages, remote caching, dependency graph visualization, and structured multi-repo to monorepo migrations. Use when setting up a new monorepo, optimizing CI for a large workspace, debugging cross-package dependency issues, or planning a multi-repo consolidation.
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Use when writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.
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。
Workspace guide to introduce OpenWork and onboard new users.
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。
Query the pre-built code-review-graph knowledge graph (Tree-sitter, whole monorepo) instead of walking call chains with Grep+Read – 2-6x cheaper, and it catches dynamic dispatch that grep misses. Use for ANY task that spans multiple files, even when the user never mentions a graph or asks for it - fixing or tracing a bug ("fix this bug", "why does X happen", "trace this"), exploring unfamiliar code ("how does X work", "who calls X", "what imports Y", "where is X used or handled"), planning or doing a refactor ("rename X", "is it safe to change or remove X", "what would break", "blast radius", "find dead code"), or reviewing changes ("review this PR", "review this branch or diff"). When you are about to Grep for a symbol's callers, callees, or importers, use this 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。
Knowledge and governance closeout: reconcile project docs, rule files (CLAUDE.md/AGENTS.md), authorized agent memory, and workspace residue with what the code and runtime actually do, so the next session or the next person starts from one current answer. Trigger when the user names "neat-freak", "洁癖", or "/neat" — and also on clear knowledge-closeout intent without the name: syncing or tidying project docs/rules/memory after development ("把文档和记忆整理一下", "收尾时把文档同步掉", "docs 和代码对不上了"), stale or conflicting CLAUDE.md/memory, a clean handoff to a teammate or a fresh session, or auditing whether workspace rules are actually followed. Do not trigger for pure coding/refactoring/debugging tasks, tidying data or prose (JSON, 周报, changelog announcements), or a bare "整理" with no project-knowledge context.
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。
Inspects Filestore capacity and utilization on Google Cloud, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.
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。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
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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。
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (`audit_cluster.sh`), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling) and pod securityContext, and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing workload security posture, isolating namespaces, applying pod security standards, or configuring network policies and secret volume mounts. Don't use for Workload Identity (use gke-workload-identity), cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security 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。