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Start the OpenWork dev stack via Docker and verify real user flows via Chrome MCP. Triggers when user mentions: - "dev-up.sh" - "docker dev stack" - "verify in chrome mcp" - "test the real flow"
Use when you need to visually interact with a GUI — test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Install: pip install cua.
Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.
Build, run, and test IronClaw locally using Docker containers and Chrome MCP browser automation.
Update Go version across the Pyroscope codebase (go.mod, go.work, CI workflows, Dockerfiles, goreleaser, examples). Use when bumping Go to a new patch or minor version.
Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security hardening. Use when: user wants to optimize a Dockerfile, create or improve docker-compose configurations, implement multi-stage builds, audit container security, reduce image size, or follow container best practices. Covers build performance, layer caching, secret management, and production-ready container patterns.
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
Deploy Electric via Docker, Docker Compose, or Electric Cloud. Covers DATABASE_URL (direct connection, not pooler), ELECTRIC_SECRET (required since v1.x), ELECTRIC_INSECURE for dev, wal_level=logical, max_replication_slots, ELECTRIC_STORAGE_DIR persistence, ELECTRIC_POOLED_DATABASE_URL for pooled queries, IPv6 with ELECTRIC_DATABASE_USE_IPV6, Kubernetes readiness probes (200 vs 202), replication slot cleanup, and Postgres v14+ requirements. Load when deploying Electric or configuring Postgres for logical replication.
Build and deploy a full local OpenMetadata stack with Docker to test your connector in the UI. Handles code generation, build optimization, health checks, and guided testing.
Local development environment management for Polar using Docker
Local development environment management for Polar using Docker
Download videos from m3u8/HLS streams, Bilibili, and direct URLs using MediaGo. 下载视频、m3u8直播流、B站视频。 Triggers on: download video, 下载视频, 下载这个链接, 帮我下载, m3u8 download, 设置mediago地址, configure mediago, mediago api key. Requires a running MediaGo instance (desktop app or Docker).
Droidrun documentation reference. Use when users ask about Droidrun setup, configuration, SDK usage, CLI commands, device setup, agents, architecture, app cards, credentials, tracing, Docker, migration, structured output, or any Droidrun "how do I..." questions.
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.
Use when running tests in the Nango monorepo - knows unit vs integration configs, vitest commands, Docker setup, and common test patterns
Use when running the Nango application locally for development and browser testing - covers Docker services, dev commands, service URLs, and troubleshooting startup issues
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.
Build Docker images for testing or production. Use this when asked to build container, build image, docker build, build test image, or launch production container.
