Kubernetes cluster operations via dot-ai CLI - natural language cluster queries, AI-powered deployment recommendations, issue troubleshooting and remediation, Day 2 operations (scale, update, rollback, delete), resources, namespaces, events, logs, organizational knowledge base (ingest, search, query), organizational patterns/policies/capabilities management, project scaffolding and repo audit, and session history and visualization. Run `dot-ai --help` for commands.

Generate intelligent CI/CD workflows through interactive conversation by analyzing repository structure and user preferences

Manage the knowledge base: ingest documents, search with natural language, or delete chunks. Use "ingest" to store organizational documentation, "search" to find relevant content semantically, or "deleteByUri" to remove all chunks for a document. TIP: For complex questions, you can call search multiple times with different phrasings to gather comprehensive information before synthesizing your answer.

Unified tool for managing cluster data: organizational patterns, policy intents, and resource capabilities. For patterns and policies: supports create, list, get, delete, deleteAll, and search operations (patterns also support step-by-step creation workflow). For capabilities: supports scan, list, get, delete, deleteAll, and progress operations for cluster resource capability discovery and management. Use dataType parameter to specify what to manage: "pattern" for organizational patterns, "policy" for policy intents, "capabilities" for resource capabilities.

AI-powered Kubernetes application operations tool for Day 2 operations. Handles updates, scaling, enhancements, rollbacks, and deletions through natural language intents. Analyzes current state, applies organizational patterns and policies, validates changes via dry-run, and executes approved operations safely.

Create documentation-first PRDs that guide development through user-facing content

Complete PRD implementation workflow - create branch, push changes, create PR, merge, and close issue

Analyze existing PRD to identify and recommend the single highest-priority task to work on next

Start working on a PRD implementation

Update PRD based on design decisions and strategic changes made during conversations

Update PRD progress based on git commits and code changes, enhanced by conversation context

Fetch all open GitHub issues from this project that have the 'PRD' label

Process a feature request or response from another dot-ai project. Reads from tmp directory, implements/integrates, and writes response if needed.

Setup project, audit repository, or generate repository files. Use this when user wants to: setup project, audit repo, check missing files, create README, add LICENSE, generate CONTRIBUTING.md, add CI/CD workflows, initialize documentation, setup governance files. Analyzes local repositories and generates missing configuration, documentation, and governance files. Does NOT handle Kubernetes deployments - use recommend for those.

Natural language query interface for Kubernetes cluster intelligence. Ask any questions about your cluster resources, capabilities, and status in plain English. Examples: "What databases are running?", "Describe the nginx deployment", "Show me pods in the kube-system namespace", "What operators are installed?", "Is my-postgres healthy?"

Query sibling dot-ai projects to verify features are USABLE (not just defined). IMPORTANT: When calling this skill, explain HOW you plan to use the feature (e.g., 'I need to call X via REST API from the UI' or 'I need to import Y function'). This helps verify the full chain from definition to exposure.

Deploy applications, infrastructure, and services using Kubernetes resources with AI recommendations. Supports cloud resources via operators like Crossplane, cluster management via CAPI, and traditional Kubernetes workloads. Describe what you want to deploy. Does NOT handle policy creation, organizational patterns, or resource capabilities - use manageOrgData for those.

AI-powered Kubernetes issue analysis that provides root cause identification and actionable remediation steps. Unlike basic kubectl commands, this tool performs multi-step investigation, correlates cluster data, and generates intelligent solutions. Use when users want to understand WHY something is broken, not just see raw status. Ideal for: troubleshooting failures, diagnosing performance issues, analyzing pod problems, investigating networking/storage issues, or any "what's wrong" questions.

Generate a feature request prompt for another dot-ai project. Use when you need a feature implemented in a sibling project (MCP server, controller, etc.) to unblock work in the current project.

Create a release tag based on accumulated changelog fragments. Run when ready to cut a release.