
query-dot-ai
by vfarcic
Intelligent dual-mode agent for deploying applications to ANY Kubernetes cluster through dynamic discovery and plain English governance
SKILL.md
name: query-dot-ai description: "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." context: fork agent: Explore allowed-tools:
- Read
- Glob
- Grep
- Bash(grep:*)
Query dot-ai Projects
Explore the dot-ai ecosystem codebases to find the requested information.
Project Locations
Sibling projects are located in the parent directory of the current working directory (../):
- dot-ai - Main MCP server (API endpoints, tools, handlers)
- dot-ai-ui - Web UI for visualizations and dashboard
- dot-ai-controller - Kubernetes controller
- dot-ai-stack - Stack deployment configs
- dot-ai-website - Documentation website
Default to dot-ai (MCP server) if the target project is unclear.
Important: Do NOT use this skill to query the project you're currently working in. Use local tools (Read, Grep, Glob) instead.
Excluded
dot-ai-infra - Production infrastructure. Only query if user explicitly requests it.
Verification Mindset
Don't just find that something EXISTS - prove it's USABLE.
- Finding a type/interface is NOT enough
- Finding internal code is NOT enough
- You must trace from definition → implementation → exposure
When asked "does X exist?", answer:
- "Yes, and here's how to use it: [concrete usage]" OR
- "It exists internally but is NOT exposed for external use"
Go deep, not wide. Follow the code path until you can prove how the caller would actually use the feature.
Score
Total Score
Based on repository quality metrics
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Reviews
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