- Skills
- 31
- 仓库
- 2
- 职业覆盖
- 1
- 场景覆盖
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- ★ 6
Viktor Farcic 的 Skills 31
validate-plugin
validate a Grafana plugin using the official Grafana plugin validator
build-plugin
build a Grafana plugin using the standard build process (frontend and backend if applicable)
validate-plugin
validate a Grafana plugin using the official Grafana plugin validator
build-plugin
build a Grafana plugin using the standard build process (frontend and backend if applicable)
dot-ai
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.
dot-ai-write-docs
Write documentation with real, validated examples. Executes commands through the user to capture actual output. Use for any new documentation or major doc updates.
dot-ai-worktree-prd
Create a git worktree for PRD work with a descriptive branch name. Infers PRD from context or asks user.
dot-ai-version
Get comprehensive system health and diagnostics
dot-ai-users
Manage users (create, list, delete)
dot-ai-tag-release
Create a release tag based on accumulated changelog fragments. Run when ready to cut a release.
dot-ai-request-dot-ai-feature
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.
dot-ai-remediate
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.
dot-ai-recommend
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.
dot-ai-query
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?"
dot-ai-query-dot-ai
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.
dot-ai-projectSetup
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.
dot-ai-process-feature-request
Process a feature request or response from another dot-ai project. Reads from tmp directory, implements/integrates, and writes response if needed.
dot-ai-prds-get
Fetch all open GitHub issues from this project that have the 'PRD' label