12 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 12 plugins that match these filters.
Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceCI/CD pipeline configuration, GitHub Actions/GitLab CI workflow setup, and automated deployment pipeline orchestration
Build, launch, grade, and schedule Claude Managed Agents (CMA) in your own Anthropic account — a plugin re-implementation of Anthropic's launch-your-agent reference skill (Apache-2.0; independent, not a fork). Every session starts with a goal (./my-agent/goal.json, opt-in SessionStart hook, driven by /cs-goal); loop_compiler.py compiles it into a bounded grade->iterate loop (max_iterations 1..20), a recurring cron scheduled-deployment loop, or a single-pass interview->stage->launch workflow. 6 skills: orchestrator (context: fork goal router) + interview + stage-launch + grade-iterate + run-without-you + wrap-up. 18 stdlib-only deterministic scaffolders (no network/API calls; live launches emitted as BYOK curl that never prints the key), 4 agents, 8 commands, hooks, 5 references, 4 assets. Validators enforce CMA limits. Distinct from engineering/agent-harness (generic domain loop) and write-a-skill (authors Claude Code skills, not CMAs).
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
End-to-end feature orchestration with testing, security, performance, and deployment
Build full-stack apps with Butterbase — AI-Native Backend-as-a-Service. Skills for app creation, schema design, frontend deployment, RLS debugging, serverless functions, and contributing.
Pre-deployment checks, configuration validation, and deployment readiness assessment
Manage environment configurations and secrets across deployments
Create Kubernetes deployments, services, and configurations with best practices
Quick smoke test suites to verify critical functionality after deployments
Orchestrate complex multi-stage deployment pipelines
Manage and execute deployment rollbacks with safety checks
Deploy ML models to production
