Use when answering questions about Claude Code CLI features, configuration, hooks, skills, MCP, permissions, settings, sub-agents, teams, plugins, keybindings, IDE integration, CI/CD setup, troubleshooting, or any Claude Code usage topic.
Use when working with Apache Doris: table design, data models (Duplicate/Unique/Aggregate), partitioning, bucketing, SQL syntax, data import (Stream Load, Broker Load, INSERT INTO), data export, lakehouse (Hive/Iceberg/Hudi/Paimon catalogs), materialized views, query acceleration, inverted index, compute-storage decoupled mode, administration, or Doris ecosystem tools.
justfile (just) documentation — a command runner with make-inspired syntax. Covers recipes (parameters, dependencies, shebang/script recipes), justfile language (settings, strings, functions, constants, attributes, conditionals, backticks), modules/imports, variables, shell configuration, and CLI options. USE THIS SKILL WHEN the user asks about just, justfile syntax, just recipes, just modules, just functions, or just settings.
USE THIS SKILL WHEN working with Mastra (TypeScript AI agent framework): building agents, workflows, RAG pipelines, memory, voice, evals, MCP tools, observability/tracing, deploying to Mastra Cloud/Cloudflare/Vercel, or using Mastra client SDK. Triggers on: mastra, Mastra agent, Mastra workflow, createAgent, createWorkflow, mastra.run, MastraClient, @mastra/core.
Use when working with the OpenRouter API: model routing, provider selection, model variants (free/nitro/thinking), tool calling, structured outputs, prompt caching, OAuth, API keys, rate limits, streaming, embeddings, Claude Code integration, or any OpenRouter-specific feature.
Use when working with Ultralytics YOLO, including YOLO26/YOLO11/YOLOv8 models, detect/segment/classify/pose/OBB tasks, train/val/predict/export/track/benchmark modes, dataset YAML formats, HUB/platform workflows, integrations, solutions, or ultralytics Python API behavior.
Use when working with vLLM inference engine: OpenAI-compatible serving, model deployment, quantization (AWQ, GPTQ, FP8, GGUF, INT4/INT8), speculative decoding, LoRA adapters, structured outputs, tool calling, multimodal inputs, distributed serving (tensor/pipeline/expert/context parallel), Docker/Kubernetes deployment, engine configuration, memory optimization, PagedAttention, offline inference, CLI usage, or troubleshooting vLLM issues.
Use when answering questions about OpenAI platform features, guides, and concepts: Responses API usage, agents, function calling, text generation, vision, audio, embeddings, fine-tuning, batch processing, moderation, assistants, ChatKit, or any OpenAI developer guide topic.