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Found 813 Skills
Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.
Execute use when generating infrastructure as code configurations. Trigger with phrases like "create Terraform config", "generate CloudFormation template", "write Pulumi code", or "IaC for AWS/GCP/Azure". Produces production-ready code for Terraform, CloudFormation, Pulumi, ARM templates, and CDK across multiple cloud providers.
Japanese version of the PUA Universal Motivation Engine. It compels exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology in Japanese. MUST trigger under the following conditions: (1) Any task has failed 2+ times, or you're stuck in a loop of tweaking the same approach; (2) You're about to say 'I cannot', suggest manual handling to the user, or blame the environment without verification; (3) You find yourself being passive — not searching, not reading source code, not verifying, just waiting for instructions; (4) The user expresses frustration in any form: 'try harder', 'stop giving up', 'figure it out', 'why isn't this working', 'again???', 'もっと頑張れ', 'なんでまた失敗したの', 'もう一回やって', 'なんとかしろ', or any similar sentiment regardless of phrasing. It should also trigger when facing complex multi-step debugging, environment issues, configuration problems, or deployment failures where early surrender is tempting. Applies to ALL task types: code, configuration, research, writing, deployment, infrastructure, API integration. DO NOT trigger on first-attempt failures or when a known fix is already executing successfully.
Manage Microsoft Teams bot infrastructure using the Teams CLI. Use when the user wants to create, configure, or troubleshoot Teams bot apps and registrations. Does not cover building or hosting bot application code.
Discover and implement real-world OpenClaw use cases from a curated community collection covering productivity, automation, content creation, and infrastructure.
Guides VP-level cloud program leadership—multi-year cloud strategy and migration/modernization portfolio, landing zone and CCoE operating model at org scale, hyperscaler enterprise agreement and commit governance, hybrid/multi-cloud posture, cloud center of excellence and talent, and board/CFO/CTO cloud narratives. Use when setting cloud direction, prioritizing migration waves, governing EA/MACC and cloud spend envelope, designing federated cloud org model, steering CCoE and standards adoption, preparing executive or board cloud updates, or adjudicating product vs platform vs security cloud trade-offs—not for Terraform/K8s implementation (cloud-engineer, infrastructure-engineer), landing zone technical design (enterprise-cloud-architect, cloud-architect), monthly CUR FinOps (finops-analyst), TCO/NPV modeling (cloud-economist), full infra portfolio including DC capex (vp-of-infrastructure), or GL close (compute-accounting-manager).
Hostinger VPS API for virtual machine management, Docker projects, firewalls, SSH keys, backups, snapshots, OS templates, post-install scripts, recovery mode, malware scanning, PTR records, and metrics. Use when creating, managing, or troubleshooting VPS instances, deploying Docker containers, configuring firewalls, or managing server infrastructure.
Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.
Principle-engineering posture for production-grade code: reads the repo first, plans before code, matches conventions, pulls latest docs over training recall, and ships the simplest correct change that holds the bar — proper algorithms and data structures, idempotent writes, schema+queries+indexes as one artefact, typed errors, tests in the same diff. Substrate-agnostic; defers to peer skills on their lanes. Use for non-trivial planning, design, implementation, review, or refactoring; RCA and debugging; performance and optimization work; changes touching a database schema, security, infrastructure, or a public API; hardening inherited, vibe-coded, or LLM-generated code (dependency/CVE and migration audits); and over-engineering cleanup ("simplest solution," "YAGNI," "what can we delete").
Operate InstaCloud infrastructure with the `insta` CLI: create projects, add postgres/storage/compute services, deploy apps, create disposable branch environments (isolated DB + storage + compute per branch), wire `insta secrets` into `.env`, run multiple agents each in their own branch, handle governance approvals, check metrics/logs/usage, and promote branches to main. Use this skill when working in an InstaCloud-managed project (a `.insta/` dir or the `insta` CLI), when the user mentions InstaCloud or insta, AND when they ask to deploy an app, need a database/backend/object storage, want preview or per-agent sandbox environments, want branchable infrastructure, or mention agent setup or MCP — even if they don't say "InstaCloud" explicitly. Also covers the insta-cloud remote MCP server (insta_* tools) and the self-hosted insta-oss runtime (same CLI, local daemon).
Manage Runpod infrastructure — pods, serverless endpoints, jobs, templates, network volumes, container-registry auth, GPU/CPU catalog, and billing — via the Runpod MCP server's structured tool calls. Use when the Runpod MCP tools (create-pod, list-endpoints, …) are connected in this session, or to connect them (hosted OAuth or local npx). Prefer this over runpodctl for plain infra CRUD when MCP is available; use runpodctl for the terminal, file transfer, or SSH setup.
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, selecting base models from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3 usage. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.