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Found 268 Skills
Build AI agents with persistent threads, tool calling, and streaming on Convex. Use when implementing chat interfaces, AI assistants, multi-agent workflows, RAG systems, or any LLM-powered features with message history.
Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates hive-* skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
Patterns for parallel subagent execution using Task tool with run_in_background. Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
Detecting whether agent iterations are converging toward a stable solution or hitting a ceiling. Covers convergence signals, ceiling detection, non-convergence diagnosis, test pass rate as a convergence metric, and forward progress tracking for large projects. Trigger phrases: "convergence", "is the agent converging", "ceiling detection", "when to stop iterating", "diminishing returns"
Use when creating or configuring Claude Code agents and their frontmatter.
A complete workshop curriculum for building an agentic application using the Gemini Interactions API. Guides the user from basic API calls to a full production coding agent.
Build AI agents for real-time financial options analysis with LangGraph, ChromaDB RAG, and Polygon.io data
This skill should be used when the user asks to "create an agent", "make an agent", "write an agent", "build a subagent", "add an agent to a plugin", "design an autonomous agent", "generate an agent file", "write a system prompt for an agent", "what frontmatter does an agent need", "create a specialized agent". Not for skills or commands — use create-skill.
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.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
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.