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Found 1,079 Skills
Comprehensive CI/CD pipeline patterns skill covering GitHub Actions, workflows, automation, testing, deployment strategies, and release management for modern software delivery
Architect a full-stack application on Eve Horizon — manifest-driven services, managed databases, build pipelines, deployment strategies, secrets, and observability. Use when designing a new app, planning a migration, or evaluating your architecture.
Deploy Databricks jobs and pipelines with Asset Bundles. Use when deploying jobs to different environments, managing deployments, or setting up deployment automation. Trigger with phrases like "databricks deploy", "asset bundles", "databricks deployment", "deploy to production", "bundle deploy".
Automate Vercel tasks via Rube MCP (Composio): manage deployments, domains, DNS, env vars, projects, and teams. Always search tools first for current schemas.
Configure Scarb.toml, dojo profiles, world settings, and dependencies. Use when setting up project configuration, managing dependencies, or configuring deployment environments.
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".
This skill should be used when the user asks to "build an MCP server", "create an MCP", "make an MCP integration", "wrap an API for Claude", "expose tools to Claude", "make an MCP app", or discusses building something with the Model Context Protocol. It is the entry point for MCP server development — it interrogates the user about their use case, determines the right deployment model (remote HTTP, MCPB, local stdio), picks a tool-design pattern, and hands off to specialized skills.
Expert knowledge for Azure Attestation development including troubleshooting, best practices, security, configuration, and deployment. Use when validating attestation tokens, authoring SGX/TPM policies, configuring policy signers, or securing endpoints, and other Azure Attestation related development tasks. Not for Azure Confidential Computing (use azure-confidential-computing), Azure Virtual Enclaves (use azure-virtual-enclaves), Azure Key Vault (use azure-key-vault), Azure Security (use azure-security).
Deploy OpenClaw AI agent platform on Alibaba Cloud ECS and integrate with DingTalk bot. OpenClaw (formerly Clawdbot/Moltbot, 中文名"龙虾") is an open-source AI assistant and automation platform supporting natural language-driven task automation with multi-channel chat integration. This Skill covers the full workflow from ECS instance creation, public network configuration, base environment setup, one-click OpenClaw deployment to DingTalk bot verification. End users can chat with the AI assistant by @mentioning the bot in a DingTalk group. Triggers: "OpenClaw", "龙虾", "Clawdbot", "Moltbot", "DingTalk bot", "DingTalk AI", "deploy OpenClaw on ECS", "AI agent platform", "DingTalk integration", "openclaw dingtalk", "openclaw deploy", "DingTalk AI employee", "Alibaba Cloud OpenClaw", "Bailian + DingTalk", "DingTalk group AI", "DingTalk smart assistant", "部署龙虾", "龙虾机器人", "龙虾钉钉"
Diagnoses and resolves issues on GuaraCloud — failed deployments, crash loops, health check failures, image pull errors, OOM kills, and CLI errors. Use when the user reports something broken, a deployment failed, a service is unhealthy, or they see an error.
Generate enterprise-grade documentation for NetSuite SDF projects. Analyze scripts, object XML files, `manifest.xml`, and SuiteQL queries to produce README.md, architecture diagrams (Mermaid/ASCII), deployment guides, and troubleshooting tables. Can integrate with post-deployment documentation workflows when automation (for example, hooks) is available.
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.