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.
- License 许可
- MIT
- 最近更新
- 2026-06-19
- Stars
- ★ 3
- Forks
- 1
01 前言 Front Matter
- name
- dot-ai-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.
- user-invocable
- true
02 资源文件 1
- 脚本scripts/rest_request.py10.5 KB
03 SKILL.md 正文
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.
04 同仓库更多 Skills
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.