「resources」の検索結果

Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a linked table of contents. Pipeline: pandoc (GFM → standalone HTML with embedded resources) → headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).

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。

Unity scene and asset architecture decisions. Additive scene composition, Addressables vs Resources, AssetReference workflow, asset lifecycle coordination, loading screens. DECISION format: WHEN/DECISION/SCAFFOLD/GOTCHA. Based on Unity 6.3 LTS.

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。

Marketing project management and execution excellence for B2B SaaS campaigns. Use this skill when coordinating multi-channel campaigns, allocating team resources, planning agile marketing sprints, running QBRs with clients, managing project timelines, balancing marketing workload, or implementing marketing operations scrum. Also triggers on: campaign coordination, resource allocation, agile marketing, sprint planning, client success, QBR, project timeline, marketing ops scrum, workload balancing.

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 Community Health Worker — screening a client for social-determinants-of-health barriers, triaging a referral to housing/food/transportation resources, translating a clinical instruction into a teach-back-verified action plan, or deciding whether a no-show pattern signals disengagement or an unaddressed barrier.

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 CEO/chief executive — setting company strategy, allocating scarce resources (capital, headcount, attention) across competing priorities, making a call that only the top of the org can make, or communicating direction to the board, investors, or the whole company.

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。

Use Terraform/IaC for Volcengine resources only when the user explicitly chooses Terraform/IaC, already has a Terraform workflow/state, or confirms they need plan/diff/drift/destroy safety for VKE, managed databases/cache/storage, load balancers, domains/certificates, logging/monitoring, or team-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。

Manage Volcengine's AI-native BaaS platform (Supabase edition / AIDAP / 火山引擎 AI 原生 BaaS 平台 Supabase 版) — a Volcengine-operated distribution that differs from official Supabase. Use when the user asks to create, inspect, or manage Volcengine Supabase or AIDAP resources — workspaces, branches, computes, SQL queries, schema changes, Auth, Realtime, Edge Functions, Storage buckets, frontend static-site hosting (Pages), API keys, connection info, or TypeScript type generation — or when a Volcengine deployment selects AIDAP as its database provider. Also trigger on Chinese prompts mentioning "火山引擎 Supabase" or "火山 Supabase". Operations run through the byted-supabase-cli command-line tool (installed via `npm i -g @byted-supabase/cli`; this is NOT the official `supabase` CLI). Do NOT use it for general database discussions, non-Supabase services (RDS MySQL, Redis), or pure client-side coding unrelated to Supabase backend management.

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。

Guidance for Tauri v2 daily development workflow, debugging, resources, sidecar usage, and testing strategies.

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。

Scaffolds Pulumi TypeScript infrastructure-as-code projects, writes IaC code with proper resource configuration, manages Pulumi ESC environments for centralized secrets and configuration, configures OIDC authentication for cloud providers, and builds multi-language component resources. Use when the user asks to create Pulumi TypeScript projects, write Pulumi infrastructure code, set up ESC environments, configure OIDC for Pulumi, implement infrastructure automation with Node.js/TypeScript, create reusable Pulumi components, or work with stack references. Also use when the user mentions Pulumi with TypeScript, AWS/Azure/GCP infrastructure in TypeScript, or PulumiPlugin.yaml for multi-language components.

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 Pulumi infrastructure-as-code projects in Go, configures OIDC authentication, integrates with Pulumi ESC for centralized secrets and configuration management, and builds multi-language component resources. Use when setting up Pulumi Go projects, writing infrastructure code with Go, configuring OIDC for Pulumi, using Pulumi ESC with Go, automating cloud infrastructure with Golang, creating reusable Pulumi components in Go, or working with pulumi-go-provider. Also use when the user mentions Pulumi with Go/Golang, AWS/Azure/GCP infrastructure in Go, or Go-based ComponentResource patterns.

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 infrastructure through natural language conversations with Pulumi Neo, an AI agent for platform engineers. Enables infrastructure analysis, resource provisioning, stack deployment, and configuration management via conversational AI. Use when creating Neo tasks, requesting infrastructure analysis, automating cloud deployments, managing infrastructure as code (IaC), provisioning AWS/Azure/GCP resources, managing infrastructure through natural language prompts, reviewing PRs with Neo, handling Neo approval workflows, or checking Neo task status and events. Also use when the user mentions "Pulumi Neo", "Neo task", "Neo agent", or wants AI-assisted infrastructure management.

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。

Bootstraps Flux CD on Kubernetes clusters, creates and manages GitOps sources (Git/Helm/OCI/Bucket), configures kustomizations and Helm releases, reconciles resources, sets up image automation and alerting, and pushes OCI artifacts. Use this skill whenever the user mentions the flux CLI, flux bootstrap, flux create source, flux create kustomization, flux create helmrelease, flux reconcile, flux get, Flux CD, GitOps with Flux, or asks about managing Kubernetes deployments via GitOps. Also trigger when users ask about creating Git/Helm/OCI/Bucket sources, building or diffing kustomizations, pushing OCI artifacts, setting up image automation, creating Flux alerts/receivers, or bootstrapping Flux on GitHub/GitLab/Gitea/Bitbucket. Even if the user just says "flux" in a Kubernetes context, this skill applies.

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。

Analyzes mobile app binary size -- asset audit for unused images and font subsetting, code stripping with ProGuard and tree-shaking, on-demand resources, dynamic feature modules, and app thinning strategies..

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 says "map out our business model", "business model canvas", "how does our business work", "what are our key resources", "define our value proposition", "revenue model design", "cost structure", "who are our key partners", "lean canvas", "business model design", "how do we make money", or wants to document, stress-test, or redesign how [your startup] creates, delivers, and captures value.

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。

Orchestrate Unity Editor via MCP (Model Context Protocol) tools and resources. Use when working with Unity projects through MCP for Unity - creating/modifying GameObjects, editing scripts, managing scenes, running tests, or any Unity Editor automation. Provides best practices, tool schemas, and workflow patterns for effective Unity-MCP integration.

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。

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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。

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

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 to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM 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 agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).

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。

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。

Configures PromQL-based Service Level Objective (SLO) alerting policies for Google Cloud resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.

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。

Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation 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。

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.

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。