agents
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
npx skills add majiayu000/claude-skill-registry --skill agents-itsmostafa-llm-engineering-skil-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Building Agents Agents are systems where LLMs dynamically direct their own processes and tool usage. This skill covers when to use agents vs workflows, common architectural patterns, and practical implementation guidance. ## Table of Contents - [Agents vs Workflows](#agents-vs-workflows) - [Workflow Patterns](#workflow-patterns) - [Agent Architectures](#agent-architectures) - [ReAct Pattern](#react-pattern) - [Tool Design](#tool-design) - [Best Practices](#best-practices) - [References](#references) ## Agents vs Workflows | Aspect | Workflows | Agents | |--------|-----------|--------| | **Control flow** | Predefined code paths | LLM determines next step | | **Predictability** | High - deterministic steps | Lower - dynamic decisions | | **Complexity** | Simpler to debug and test | More complex, harder to predict | | **Best for** | Well-defined, repeatable tasks | Open-ended, adaptive problems | **Key principle**: Start with the simplest solution. Use workflows when the task is predictable; use agents when flexibility is required. ## Workflow Patterns ### 1. Prompt Chaining Decompose tasks into sequential LLM calls, where each step's output feeds the next. ```python async def promp
- Table of Contents
- Agents vs Workflows
- Workflow Patterns
- 1. Prompt Chaining
- 2. Routing
- 3. Parallelization
- 4. Orchestrator-Workers
- 5. Evaluator-Optimizer
- Agent Architectures
- Autonomous Agent Loop
- Human-in-the-Loop
- ReAct Pattern
- Tool Design
- Principles
What does the agents skill do?
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill agents-itsmostafa-llm-engineering-skil-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.