Loading...
Loading...
Found 1,194 Skills
Kubernetes deployment, management, and troubleshooting. Activate for k8s, kubectl, pods, deployments, services, ingress, namespaces, and container orchestration tasks.
Markets orchestration — connects ESPN live schedules with Kalshi & Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.
Automates IT infrastructure configuration, application deployment, and orchestration using agentless YAML playbooks.
Azure cloud operations orchestration and Microsoft Agent Framework integration hub.
Use when the user needs workflow orchestration such as branching, concurrency, approvals, waiting and resume, runtime stream, restart-safe execution, mixed sync/async function or module orchestration, event-driven fan-out, process-clarity refactors that make stages explicit, performance-oriented refactors that collapse split requests, or explicit draft-review-revise style multi-stage flows. The user does not need to say TriggerFlow explicitly.
Use this skill when managing cmux terminal panes, surfaces, and workspaces from Claude Code or any AI agent. Triggers on spawning split panes for sub-agents, sending commands to terminal surfaces, reading screen output, creating/closing workspaces, browser automation via cmux, and any task requiring multi-pane terminal orchestration. Also triggers on "cmux", "split pane", "new-pane", "read-screen", "send command to pane", or subagent-driven development requiring isolated terminal surfaces.
Use this skill when containerizing applications, writing Dockerfiles, deploying to Kubernetes, creating Helm charts, or configuring service mesh. Triggers on Docker, Kubernetes, k8s, containers, pods, deployments, services, ingress, Helm, Istio, container orchestration, and any task requiring container or cluster management.
DataWorks data development Skill. Create, configure, validate, deploy, update, move, and rename nodes and workflows. Manage components, file resources, and UDF functions. Covers 150+ node types: Shell, SQL, Python, DI, Flink, EMR, etc. Supports scheduled and manual workflow orchestration via aliyun CLI or Python SDK. WARNING: Supports mutating operations (Move, Rename) requiring explicit user confirmation. Delete operations are NOT supported by this skill. Triggers: DataWorks, data development nodes, workflows, FlowSpec, scheduling tasks, data integration, ETL pipelines, .spec.json. Also triggers for Alibaba Cloud data development, scheduling node configuration, FlowSpec format, or DI task orchestration.
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Design state machines, orchestration workflows, saga patterns, and resilience strategies for distributed systems, AI agents, and complex async processes. Use when asking for a workflow, state machine, orchestration design, saga, HITL checkpoint, or process resilience strategy.
Native web workspace for Hermes Agent with chat, terminal, memory, skills, inspector, and multi-agent orchestration