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Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc 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。
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。
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
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 Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query 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。
Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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。
Manage an electronics bill of materials across distributors and fabricators: part selection, stock, pricing, alternates, ordering, KiCad properties, and BOM/CPL export. Use for distributor-neutral sourcing, cost comparison, order preparation, or requests such as "what parts do I need" and "export for JLCPCB". Do not take over schematic analysis or a complete new-design workflow unless the requested task is specifically its sourcing/manufacturing stage.
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 a product brief and its task list when the user requests a product specification. Never implement 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。
Use when the user invokes Arsenal, needs help choosing an approach, or wants an end-to-end task handled with the installed Arsenal 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。
Run, document, or review a structured month-end close process. Use when an accountant says "help me with month-end close", "close checklist", "end of month tasks", "what do I need to do to close the books", "journal entries for close", "reconciliation checklist", "accruals and prepaids", "intercompany eliminations", "trial balance review", "close sign-off", or anything related to closing a financial period. Also trigger when someone is preparing for a quarterly or year-end close and needs a structured approach - even if they don't say "month-end close" explicitly.
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 Jira tickets from meeting notes, user stories, or quick lists. Use this skill when: - User says "create tickets", "make Jira tickets from", "push to Jira" - User has meeting action items to convert to tickets - User has user stories from a spec to push to the backlog - User wants to batch-create tasks, stories, or bugs
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。
Annotate genetic variants (GWAS hits, eQTLs, rare variants) with ENCODE functional data to interpret non-coding variation. Use when the user has variants of interest and wants to understand their regulatory context, identify causal variants from GWAS loci, assess variant impact on regulatory elements, perform enrichment testing of variant sets in tissue-specific annotations, or link variants to target genes through enhancer-gene maps. Handles the full post-GWAS workflow from variant set → tissue mapping → functional annotation → fine-mapping awareness → enrichment → variant-to-gene → prioritization. Use this skill for ANY variant interpretation task involving ENCODE chromatin, accessibility, TF binding, or 3D genome 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。
Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE 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。
Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.
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。
Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell 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。
Create Product Requirements Document (PRD) and setup for Ralph autonomous loop. Use when user runs /create-prd command, wants to set up a project for Ralph, mentions "ralph setup", "create prd", "product requirements", or needs to generate tasks for autonomous 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。
Creates feature branches with optimized short naming, auto-incrementing, and commit type detection (feat/fix/refactor). Supports manual descriptions and auto-generation from uncommitted git changes. Use when user requests to create branch, start new branch, make branch, checkout new branch, switch branch, new task, start working on, begin work on feature, begin feature, create feature branch, run /create-branch command, or mentions "branch", "feature", "new feature", "feat", "fix", or "checkout".
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 starting any conversation - establishes how arkhe skills bootstrap on Claude Code, Antigravity CLI (agy) / Gemini CLI, and Codex CLI, and maps Claude-only tools (AskUserQuestion, TaskCreate, EnterPlanMode, the Skill tool, the Agent tool) to their cross-platform equivalents
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 graph-informed implementation plans before writing code. Use before any non-trivial task. Skip for small changes (≤2 files, <20 lines, no hub/bridge).
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。
Plan-aware auto-fix loop after coding. Runs tests, lint, format, and graph checks. Updates plan task status. Use after execute/tdd.
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。
Compact current session context into a handoff document for seamless continuation in a new session. Use when context window is exhausted, switching machines, handing work to another agent, or ending a long session mid-task.
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。
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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。
Dispatch and execute implementation plans with TDD and checkpoints. Use when plan is ready. Parallel by default for independent 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。
Meta-orchestrator — dispatches to the right skill for the task. Use before any non-trivial work.
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。