agent-loops
Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
npx skills add majiayu000/claude-skill-registry --skill agent-loops --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.
# Agent Loops Enable LLMs to reason, plan, and take autonomous actions. ## When to Use - Multi-step problem solving - Tasks requiring planning - Autonomous tool use - Self-correcting workflows ## ReAct Pattern (Reasoning + Acting) ```python REACT_PROMPT = """You are an agent that reasons step by step. For each step, respond with: Thought: [your reasoning about what to do next] Action: [tool_name(arg1, arg2)] Observation: [you'll see the result here] When you have the final answer: Thought: I now have enough information Final Answer: [your response] Available tools: {tools} Question: {question} """ async def react_loop(question: str, tools: dict, max_steps: int = 10) -> str: """Execute ReAct reasoning loop.""" history = REACT_PROMPT.format(tools=list(tools.keys()), question=question) for step in range(max_steps): response = await llm.chat([{"role": "user", "content": history}]) history += response.content # Check for final answer if "Final Answer:" in response.content: return response.content.split("Final Answer:")[-1].strip() # Extract and execute action if "Action:" in response.content: action = parse_action(response.content) result = await tools[action.name](*action.args) history
- When to Use
- ReAct Pattern (Reasoning + Acting)
- Plan-and-Execute Pattern
- Self-Correction Loop
- Memory Management
- Key Decisions
- Common Mistakes
- Related Skills
- Capability Details
- react-loop
- tool-use
- workflow-template
What does the agent-loops skill do?
Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill agent-loops --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.