36 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 36 plugins that match these filters.
agents-incagents-inc/skills2
dockerdocker/skills1
wigtn-pluginswigtn/wigtn-plugins1
claude-code-skillsalirezarezvani/claude-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceDocker-authored knowledge skills for AI coding agents across Dockerfiles, Compose, Docker Agent, Docker Sandboxes, and safe Docker workflows.
Unified AI-native Vibe Coding plugin: PRD generation, digging analysis, screen-spec (IA / User Flow / Wireframe HTML / Dev Handoff), team-based parallel implementation, quality-gated auto-commit, 20 design styles, frontend (React/Next.js), backend (NestJS/Prisma), mobile (React Native/Expo), AI integration (STT/LLM), DevOps (Docker/CI-CD). 11 agents, 5 commands, 8 skills, team knowledge wiki (auto-collect + recall for WIGTN repos).
Docker and container development — Dockerfile optimization, docker-compose orchestration, multi-stage builds, security hardening, and CI/CD container pipelines.
Development skills: feature implementation, bug fixing, testing, refactoring, CI/CD generation, framework documentation injection, project planning, image generation (Codex imagegen), database migrations, Docker setup, environment setup, visual regression checks, i18n checks, release shipping, and open-source launch prep
Generate Docker Compose configurations for multi-container applications with best practices
Manage test environments with Docker Compose, Testcontainers, and environment isolation
Infrastructure and DevOps specialists for AWS, GCP, Kubernetes, Docker, Terraform, GitHub Actions, GitLab CI, and security auditing.
Complete DevOps automation suite with 25 plugins covering Git workflows, CI/CD pipelines, Docker optimization, Kubernetes management, Terraform IaC, and deployment strategies
Generate production-grade engineering design diagrams (architecture, sequence, delta, drift) as self-contained dark-themed HTML files with accessible inline SVG. Grounds every diagram in real repo topology via DCI — package manifests, docker-compose, k8s, terraform, import graph. Four modes: generate, diff, trace, watch. Semantic OKLCH palette; Mermaid fallback for large graphs.
Opinionated DevOps and platform engineering skills from a Docker Captain: Dockerfiles and Compose, GitHub Actions workflows and audits, production Kubernetes pod specs, recent Kubernetes features, and local linting setup. Tested with evals.
devopsTest environment setup: Testcontainers and Docker Compose services, Playwright fixtures, database snapshot and restore, and flag-aware test harnesses. Agents (test-environment-bootstrapper, db-snapshot-restore) wire a full integration environment for a greenfield service and reset the database between tests. Shared-state diagnosis composes with e2e-flake-bisector in qa-flake-triage.
foundationsCrowdSec skills: an operational skill (engine, cscli, hub, bouncers, WAF/AppSec and bot detection across bare-metal, Docker, Kubernetes) and a Service API skill (premium Console cloud API — blocklists, allowlists, firewall integrations, metrics, decisions).
Docker containerization patterns for Node.js/TypeScript development and production
Infrastructure code review — supply-chain pinning, secret exposure, workflow triggers and untrusted input, container hygiene, deployment safety. Load when reviewing a diff that touches CI/CD workflows, Dockerfiles, deployment configs or IaC.
Deploy, configure, and operate CrowdSec Security Engine, cscli, remediation components, acquisition pipelines, and AppSec WAF. Use for Linux or Docker installation, detection-to-blocking design, incident review, and safe changes. Do not use for generic firewall, Kubernetes, or reverse-proxy design; route those to the named platform skill and use this skill for CrowdSec integration.
Use Docker Compose to define, run, debug, and harden multi-container applications. Load for compose.yaml design, networking, volumes, secrets, profiles, overrides, watch mode, lifecycle operations, or troubleshooting. Do not use this skill for unrelated requests; route to the nearest named specialist.
Deploy, manage, and troubleshoot self-hosted GitHub Actions runners. Covers systemd service, Docker containers, Kubernetes (Actions Runner Controller), and the Scale Set Client. Use when setting up a CI runner, debugging registration failures, designing autoscaling, or hardening runner security. Do not use this skill for unrelated requests; route to the nearest named specialist.
Operate, configure, provision, secure, and troubleshoot Grafana OSS, Enterprise, and Cloud, including dashboards, folders, data sources, annotations, alert rules, contact points, notification policies, silences, mute timings, service accounts, RBAC, plugins, APIs, and as-code workflows. Use for Grafana product work and Grafana-side integrations. Do not use for defining SLOs or paging policy, operating Prometheus/Loki/Tempo/InfluxDB backends, generic Docker/Kubernetes/Terraform/reverse-proxy work, plugin development, or authorized security assessments; use the corresponding specialist skill.
Use this skill when developing applications with Supabase, running the Supabase CLI, designing migrations and RLS policies, testing database behavior, generating client types, deploying the official self-hosted Docker stack, or administering its Postgres, Auth, Storage, Realtime, Functions, API gateway, backups, upgrades, and security. Use it for managed and self-hosted projects. Do not use for generic PostgreSQL work with no Supabase services or conventions.
Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API serving, throughput and latency benchmarking, continuous batching tuning, GPU operation, and upgrade/rollback. Use when deploying or running a vLLM server (vllm serve, vllm/vllm-openai), sizing a model and its KV cache for GPUs, selecting quantization and parallelism, serving via /v1 endpoints, measuring serving throughput or latency, tuning batching, or diagnosing GPU, OOM, or startup failures in a vLLM deployment. Do not use for model training, fine-tuning, evaluation-set design, or engine-selection methodology (that is ml-engineering), or for operating the llama.cpp stack with GGUF models (that is llama-cpp); other inference engines (TGI, Ollama, Triton) are out of scope.
