discord-chat-summary
Summarize Discord chat messages across servers. Use when user asks for chat summary, digest, highlights, recap, or overview of Discord conversations.
multi-llm-agent
여러 LLM(OpenAI, Gemini, Ollama 등)을 통합하여 멀티 에이전트 협업을 수행합니다. 역할 분담, 토론/합의, 체인 파이프라인, 병렬 처리 등 다양한 협업 패턴을 지원하며, 사용 시점에 시나리오를 동적으로 구성할 수 있습니다. 복잡한 작업을 여러 LLM에게 분산하여 더 나은 결과를 얻고 싶을 때 사용하세요.
agentuity-cli-cloud-vector-search
Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations
agent-development
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
spawn-research-agents
Use when conducting codebase research to orchestrate specialized agents in parallel for comprehensive investigation.
dspy-framework
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers. Use when optimizing prompts systematically, building production LLM systems, implementing RAG/classification tasks, or requiring version-controlled reproducible prompts.
ai-sdk
Vercel AI SDK reference for building AI-powered applications. Use when implementing text/object generation (generateText, streamText, generateObject, streamObject), building chatbots with useChat/useCompletion hooks, defining tools with Zod schemas, creating agents with ToolLoopAgent, or integrating with AI providers (OpenAI, Anthropic, Google, etc.).
autonomous-lucid
Autonomous agent factory - research a domain and generate a monorepo of 10 production Lucid Agents
processing-computer-vision-tasks
Process images using object detection, classification, and segmentation. Use when requesting "analyze image", "object detection", "image classification", or "computer vision".
command-creator
Create slash commands for user-initiated workflows. Use when building repeatable /name commands for Claude Code.
custom-durable-agent
Build a custom durable AI agent with full control over streamText options, provider configs, and tool loops. Compatible with the Workflow Development Kit.
planning-agents
여러 AI 에이전트(Claude, Codex)가 동일한 주제에 대해 병렬로 기획을 수행하고, 각 결과를 보여준 후 최종 머지된 기획안을 제시합니다. "3명이 기획해주세요"처럼 에이전트 수를 지정할 수 있으며, Claude와 Codex가 랜덤하게 분배됩니다.
codex-parallel-subagents
[DEPRECATED] Run multiple AI agent threads in parallel with bounded concurrency. Use evolving-workflow instead.
verify-claude-setup
Verify .claude directory configuration is complete and correct. Use when checking if agents, hooks, rules, and memory are properly set up, or after making changes to .claude configuration.
model-optimization
Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.
distributed-claude-receiver
You are a remote Claude instance running on a VPS. Receive messages via chat.sh wrapper, maintain persistent context, use Z.ai GLM backend via Doppler.
reasoning-framework-selection
Select and apply the optimal AI reasoning framework for any task
sage-llm-integration
Sage LLM 客户端集成开发指南，涵盖多 Provider 支持、Fallback、Rate Limiting、Streaming
subagent-generator
Generates custom Claude Code subagents with specialized expertise. Activates when user wants to create a subagent, specialized agent, or task-specific AI assistant. Creates properly formatted .md files with YAML frontmatter, suggests tool restrictions and model selection, generates effective system prompts. Use when user mentions "create subagent", "new agent", "specialized agent", "task-specific agent", or wants isolated context for domain-specific work.
create-agent
Quickly create a new specialized subagent when needed. Use when you need a specialist that doesn't exist yet for a specific task domain.
langchain-framework
LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications. Use when implementing RAG (Retrieval Augmented Generation), creating AI agents with tools, or chaining multiple LLM calls.