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概要と使いどころ
Use when a task needs the judgment of a DevOps/Site Reliability Engineer — designing deployment pipelines, setting SLOs/error budgets, responding to or reviewing an incident, or making infrastructure/reliability tradeoffs. Distinct from a backend software engineer role — this one owns the system's operational health across all services, not one service's feature code.
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 a task needs the judgment of a Data Engineer — designing an idempotent pipeline operation so retries and backfills don't corrupt data, choosing event-time over processing-time partitioning for time-series aggregation, enforcing schema compatibility rules before an upstream change propagates downstream, adding automated data quality checks to a pipeline, or deciding between batch and streaming architecture for a specific freshness requirement.
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 a task needs the judgment of a Computer and Information Systems Manager (IT Manager/Director/CIO-adjacent) — planning IT infrastructure and systems strategy, evaluating a technology vendor or platform decision, allocating an IT budget, or managing IT risk (security, uptime, technical debt) at an organizational level. Broader than the devops-sre or software-engineer roles — this one owns IT as a business function, not a specific engineering discipline.
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 a task needs the judgment of a Computer Hardware Engineer — designing a power delivery network and selecting decoupling capacitors for a digital IC's core rail, budgeting a high-speed signal's rise time against trace impedance and via stub length, closing static timing on a board-level DDR or SerDes interface, sizing thermal resistance for a package's TDP, or reviewing a PCB layout for signal- or power-integrity risk before fab release.
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 a task needs the judgment of a Chief Technology Officer — deciding build vs buy for a core system, choosing whether an architecture bet is worth a full rewrite, sizing an SLA/uptime commitment against engineering cost, structuring engineering teams around a product roadmap, or writing the technical risk section of a board update.
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 a task needs the judgment of a Chemical Engineer — closing a mass/energy balance around a reactor or separation unit, evaluating scale-up risk from bench to pilot to plant, reviewing a HAZOP finding or LOPA scenario for a covered process, sizing relief for a runaway-reaction or fire case, or checking a process safety information (PSI) package against OSHA 1910.119 before startup.
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 a task needs the judgment of a Blockchain Engineer — applying the checks-effects-interactions pattern to prevent a reentrancy exploit, auditing a privileged/administrative function for missing access control, designing a manipulation-resistant oracle for on-chain price data, minimizing gas cost through storage-write design, or deciding whether a smart contract needs an upgradability pattern before deployment.
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 a task needs the judgment of an Application Security Engineer — checking whether an endpoint verifies object-level authorization (not just authentication), running threat modeling at design time rather than deferring security review to the end, triaging a dependency vulnerability by actual reachability rather than CVSS score alone, combining SAST/DAST/SCA tooling to cover their distinct blind spots, or applying context-specific output encoding to untrusted input. Distinct from an information security analyst — this role works inside the SDLC to prevent vulnerabilities before they ship, not detect and respond to intrusions in production.
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 a task needs the judgment of an Architecture/Engineering firm or department manager — pricing a project fee (lump sum vs. hourly vs. % of construction cost), staffing a project against backlog, evaluating scope creep on a fixed-fee job, managing professional liability exposure, or deciding whether to pursue a project given current utilization.
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 a task needs the judgment of an Acoustical Engineer — calculating room reverberation time (RT60) and specifying absorption/reflection treatment for a space, evaluating a wall or floor assembly's STC/IIC rating against a code or design target, diagnosing why a completed space fails its acoustic design intent, specifying industrial or mechanical equipment noise controls against OSHA/NIOSH exposure limits, or writing an acoustic design report or noise-control specification for a construction or renovation project.
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 a task needs the judgment of an agricultural engineer — sizing a center-pivot or drip irrigation system's peak capacity against crop water demand, computing lateral grain-bin wall pressure with Janssen's equation, sizing a waterway or tile-drainage system with Manning's equation and an NRCS design-storm return period, or building a manure/nutrient first-year availability mass balance against a crop's N requirement.
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 a task needs the judgment of an Aerospace Engineer — reconciling a vehicle mass/weight budget against a certification limit, computing a structural margin of safety, choosing a means of compliance for an FAA certification basis, tracing a weight-growth overrun back to a design decision, or evaluating a propulsion/aerodynamics tradeoff against a range or payload requirement.
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。
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", "data-driven SEO", "pSEO", "template engine", "data-driven pages", "index bloat", or "thin content at scale".
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。
Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.
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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform 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。
Analyze content engagement metrics, audience retention, attribution models, A/B test results, and recommendation engine effectiveness. Triggers: 'audit content metrics', 'evaluate recommendation engine', 'review retention curves', 'assess content A/B tests'.
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。
Decompose large epics into sprint-ready sub-epics using 9 splitting patterns. Use this skill when: - A spec is approved and you need to break a large feature into estimable work items for engineering - You want to split an epic that is too large for a single sprint - You need sized sub-epics with story point estimates for sprint planning - You are preparing for engineering handoff and need to identify work packages
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 concise feature spec for engineering handoff. Use when the user says "write a spec", "feature spec", "one-pager for eng", "spec this out", "write up this feature", "I need to hand this off to engineering", "shape this up", or wants a concise technical brief - even if they don't say "feature spec".
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 component spec for engineering handoff. Use when the user says "component spec", "design handoff", "spec this component", "document this component for engineering", "Figma handoff", "component documentation", "how do I hand this off", or needs to communicate a UI component design to developers - even if they don't explicitly say "component spec".
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。
Ultimate AI prompt database and builder with 2,500+ curated prompts across 19 categories and 17 AI models (Midjourney, Flux, Leonardo AI, DALL-E, Sora, Imagen, Mystic, Stable Diffusion, Ideogram, Adobe Firefly, and more). Search, build, enhance, and adapt prompts for image generation, video creation, text, and meta-prompt generators. Use when user says "prompt", "find a prompt", "build a prompt", "enhance my prompt", "adapt this prompt", "prompt library", "image prompt", "video prompt", "midjourney prompt", "flux prompt", or any AI prompt engineering task. Do NOT use for terminal/CLI prompts, JavaScript prompt() dialogs, user input prompts, or bash prompt customization.
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。
Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.
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 alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
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。
Router for the 34 SfSkills `omnistudio` skill packages. OmniStudio: OmniScripts, FlexCards, DataRaptors, Integration Procedures, Business Rules Engine and DataPack deployment. Use when the request mentions OmniStudio, OmniScript, FlexCard, DataRaptor, Integration Procedure, Business Rules Engine, calculation procedure, DataPack, Vlocity. Finds and opens the exact skill package to read; it does not contain the guidance itself.
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。