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Found 711 Skills
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.
Show agent flow trace timeline and summary
Skill for working with the Lucid Agents SDK - a TypeScript framework for building and monetizing AI agents. Use this skill when building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication. Activate when: Building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication, developing in the lucid-agents monorepo, creating new templates or CLI features, or questions about the Lucid Agents architecture or API.
This skill helps users get started with existing (brownfield) projects by scanning the codebase, documenting structure and purpose, analyzing architecture and technical stack, identifying design flaws, suggesting improvements for testing and CI/CD pipelines, and generating AI agent constitution files (AGENTS.md) with project-specific context, coding principles, and UI/UX guidelines.
A guide to creating efficient Skills. Use this skill when users need to create a new skill (or update an existing one) to extend Claude's capabilities through expertise, workflows, or tool integrations.
Brief description of what this skill does and when to use it. Be specific about capabilities and use cases to help agents decide when to load this skill.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Developer oversight and AI agent coaching. Use when viewing project status across repos, syncing GitHub data, or analyzing agents.md against commit patterns.
Orchestrates multiple skills to achieve high-level goals. Acts as the brain of the ecosystem to coordinate complex workflows across the SDLC.
Provides guidance on choosing between Agent Teams and Sub-agents and executing complex plans with parallel coordination. Use when implementing complex features requiring multiple specialized teammates working in parallel.