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Found 1,211 Skills
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.
Check Custom SCAPI (B2C/SFCC/Demandware) endpoint registration status with the b2c cli. Always reference when using the CLI to check custom API endpoint status, verify custom API deployment, or debug "endpoint not found" errors. For creating new custom APIs, use b2c-custom-api-development skill instead.
Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots. (gstack)
Provides foundational knowledge about GuaraCloud PaaS platform — projects, services, deployments, tiers, build methods, and CLI installation and authentication. Use when the user mentions GuaraCloud, asks about platform concepts, or needs to set up the CLI.
Grafana Alloy OpenTelemetry collector and telemetry pipeline configuration. Covers the Alloy configuration language (blocks, attributes, expressions), components for collecting metrics/logs/traces/profiles, sending data to Grafana Cloud/Prometheus/Loki/Tempo, clustering, Fleet Management remote config, and building telemetry pipelines. Use when configuring Alloy, writing Alloy config files (.alloy), building data collection pipelines, setting up scraping, or troubleshooting Alloy deployments.
Grafana Beyla eBPF auto-instrumentation for application observability without code changes. Covers supported languages/runtimes, requirements, installation, configuration (discovery, eBPF settings, OTLP traces export, Prometheus metrics export), Kubernetes deployment, and integration with Grafana Cloud. Use when setting up zero-code instrumentation, configuring eBPF probes, deploying Beyla to Kubernetes, connecting to Tempo/Prometheus, or troubleshooting instrumentation issues.
Grafana Mimir scalable long-term metrics storage. Covers architecture (distributor/ingester/compactor/querier/ query-frontend/store-gateway/ruler), deployment modes (monolithic/microservices), configuration, Prometheus remote write, PromQL querying, multi-tenancy, compaction, and operations. Use when working with Mimir for metrics storage, scaling Prometheus, configuring Mimir clusters, writing PromQL, or debugging Mimir.
Migrate Next.js, Vite, React, Vue, Svelte, and other web applications from Vercel to CreateOS. Parses vercel.json, maps environment variables, detects framework and build settings, and deploys to CreateOS via the CreateOS MCP server. Use this skill whenever the user mentions migrating from Vercel, leaving Vercel, moving a deployment off Vercel, replacing Vercel, or when a repository contains a vercel.json file and the user wants to deploy elsewhere. Also use when the user references concerns about Vercel reliability, pricing, security, or the Vercel breach, and wants an alternative.
Infrastructure deployment for online novel writing toolset. Deploy infrastructure such as hooks/rules/agents/CLAUDE.md to the user's project directory. Trigger methods: /story-setup, "Prepare to write a book", "Help me set up the environment", "Configure writing project"
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
Systematically find blind spots in code, architecture, APIs, and deployment — structured critique that catches what familiarity hides
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT.