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Found 84 Skills
Agent-native CLI for Exa web search and content retrieval workflows.
Expert guidance for researching, documenting, and integrating Model Context Protocol (MCP) servers and tools. Covers MCP architecture, server/client implementation patterns, tool discovery, integration workflows, security best practices, and multi-language SDK usage (Python, TypeScript, C#, Java, Rust). Enables seamless integration of MCP tools into Claude Code and AI applications.
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool integrations, or streaming applications with the openai-agents package.
Claude Code config optimization skill. Use when: - Editing CLAUDE.md, rules/, skills/, agents/, commands/ - User asks about config best practices - Checking optimization status - User says "claude code changelog" or "claude code updates" - User asks about new features or breaking changes in Claude Code
How to create and maintain agent skills in .agents/skills/. Use when creating a new SKILL.md, writing skill descriptions, choosing frontmatter fields, or deciding what content belongs in a skill vs AGENTS.md. Covers the supported spec fields, description writing, naming conventions, and the relationship between always-loaded AGENTS.md and on-demand skills.
Integrate Azure AI Services, Azure OpenAI, and Cognitive Services.
Use when working with code refactoring context restore
TensorLake SDK for building agentic workflows, sandboxed code execution, and document parsing/extraction. Use when the user mentions tensorlake, or asks about TensorLake APIs/docs/capabilities. Also use when the user is building AI agents or agentic applications that need serverless workflow orchestration (parallel map/reduce DAGs), sandboxed execution of LLM-generated code, or document parsing, structured extraction, and OCR from PDFs/images. Works with any LLM provider (OpenAI, Anthropic), agent framework (LangChain, CrewAI, LlamaIndex), database, or API as the infrastructure layer.
How to write Cavekit-quality kits that AI agents can consume effectively. Covers implementation-agnostic cavekit design, testable acceptance criteria, hierarchical structure, cross-referencing, cavekit templates, greenfield and rewrite patterns, cavekit compaction, and gap analysis. Trigger phrases: "write kits", "create kits", "cavekit this out", "define requirements for agents", "how to write kits for AI"
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
Manages context window optimization, session state persistence, and token budget allocation for multi-agent workflows. Use when dealing with token budget management, context window limits, session handoff, state persistence across agents, or /clear strategies. Do NOT use for agent orchestration patterns (use moai-foundation-core instead).
Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.