ai-agent-basics
Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
Best use case
ai-agent-basics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
Teams using ai-agent-basics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-agent-basics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-agent-basics Compares
| Feature / Agent | ai-agent-basics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI Agent Basics
Build production-grade AI agents with modern architectures and patterns.
## When to Use This Skill
Invoke this skill when:
- Designing new AI agent systems
- Implementing ReAct or Plan-and-Execute patterns
- Building autonomous task-solving agents
- Integrating cognitive loops into applications
## Parameter Schema
| Parameter | Type | Required | Description | Default |
|-----------|------|----------|-------------|---------|
| `task` | string | Yes | What agent capability to build | - |
| `architecture` | enum | No | `single`, `multi`, `hybrid` | `single` |
| `framework` | enum | No | `langchain`, `langgraph`, `custom` | `langgraph` |
| `complexity` | enum | No | `basic`, `intermediate`, `advanced` | `intermediate` |
## Quick Start
```python
# Basic ReAct Agent
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_react_agent(llm, tools=[search, calculator])
result = await agent.ainvoke({"messages": [("user", "What is 25 * 4?")]})
```
## Core Patterns
### 1. ReAct Agent
```python
# Thought → Action → Observation loop
graph = StateGraph(AgentState)
graph.add_node("think", reason_node)
graph.add_node("act", action_node)
graph.add_node("observe", observation_node)
```
### 2. Plan-and-Execute
```python
# Create plan → Execute steps → Verify
planner = create_planner(llm)
executor = create_executor(llm, tools)
```
### 3. Reflexion
```python
# Execute → Reflect → Improve
agent_with_reflection = add_reflection_layer(base_agent)
```
## Troubleshooting
| Issue | Solution |
|-------|----------|
| Agent loops forever | Add max_iterations limit |
| Wrong tool selected | Improve tool descriptions |
| Context too large | Implement summarization |
| Slow responses | Use streaming |
## Best Practices
- Start with simple single-agent before multi-agent
- Always add circuit breakers (max iterations)
- Use verbose mode for debugging
- Implement human-in-the-loop for critical decisions
## Related Skills
- `llm-integration` - LLM API configuration
- `tool-calling` - Function calling implementation
- `agent-memory` - Memory systems
## References
- [LangGraph Docs](https://langchain-ai.github.io/langgraph/)
- [Anthropic Agent Patterns](https://docs.anthropic.com/)Related Skills
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