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Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

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Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

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Explore and debate solutions before writing code. Use when the user poses a design question, asks "how should I approach X", wants to think through options before committing, or says anything like "let's brainstorm", "what's the best way to...", "I'm not sure how to tackle this", or "should I use X or Y". Always runs before /ck:plan for novel or ambiguous features. No code is written — only: explore → clarify → spec → report.

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。

Explore and debate solutions before writing code. Use when the user poses a design question, asks "how should I approach X", wants to think through options before committing, or says anything like "let's brainstorm", "what's the best way to...", "I'm not sure how to tackle this", or "should I use X or Y". Always runs before /ck:plan for novel or ambiguous features. No code is written — only: explore → clarify → spec → report.

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Structured multi-hop web research with explicit confidence gating — plan the inquiry, search (WebSearch/WebFetch), score your own confidence, and keep digging until the answer is well-supported or a hop cap is hit, then emit a cited synthesis. Learns across sessions by storing each research case to memory and reusing prior strategies. Use when a question needs more than one search — comparisons, current-best-practice questions, anything where a single lookup leaves you unsure.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

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Expert guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.

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Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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Learn project rules from accumulated fix patches in .unikit/code/patches/. Analyzes past mistakes, extracts prevention points, classifies each as a coding rule (→ RULES.md) or a per-skill workflow rule (→ skill-context), and proposes them for approval. Use when the user wants to turn past fixes into rules — "evolve", "evolve the rules", "learn from past mistakes", "learn from the fixes", "analyze the patches", "what rules should we add from recent fixes". Best run after several /unikit-fix sessions have left patches behind. This derives rules from accumulated patch history — to add a single rule by hand use /unikit-rules, and to promote mature RULES.md entries into the knowledge base use /unikit-memory migrate-rules.

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Perform a code review on {{engine_name}} {{engine_code_language}} code. Checks for bugs, security issues, performance problems, and best practices against the project's coding rules, design principles, and framework-specific conventions. Supports four modes — staged changes, a pull request, a commit range, or individual files. Use when the user says "review code", "check my code", "code review", "review PR", "review staged changes", "review these commits", or "is this code okay". Optional +check flag validates findings via a fresh-context subagent.

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。

Application security specialist covering OWASP top 10, secure coding, and vulnerability prevention

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。

Test automation expert covering unit tests, integration tests, TDD, and testing best practices

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。

Git expert covering branching strategies, collaboration, conflict resolution, and best practices

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 code reviewer focusing on quality, maintainability, performance, and best practices

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。

RESTful and GraphQL API design expert covering best practices, security, and scalability

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。

React and Next.js performance optimization from Vercel Engineering. Use when building React components, optimizing performance, eliminating waterfalls, reducing bundle size, reviewing code for performance issues, or implementing server/client-side optimizations.

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。

MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

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。

Node.js development principles and decision-making. Framework selection, async patterns, security, and architecture. Teaches thinking, not copying.

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Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

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。

Code review guidelines covering code quality, security, and best practices.

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。