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---
name: llm-chaining
description: Chain LLM calls with output parsing, context passing, and conditional branching
category: AI Agents
---
# Llm Chaining
## Overview
Chain LLM calls with output parsing, context passing, and conditional branching. This skill helps you apply structured, repeatable methods for consistent results.
## When to Use
- When you need to apply llm chaining best practices
- When establishing processes or standards for your workflow
- When training team members on llm chaining
- When automating or optimizing llm chaining tasks
## Instructions
1. **Assess**: Evaluate the current state, requirements, and constraints
2. **Plan**: Define the approach, steps, and success criteria
3. **Execute**: Follow the established patterns and best practices
4. **Verify**: Check results against expected outcomes and quality standards
5. **Iterate**: Refine based on feedback and lessons learned
## Examples
```
User: Help me apply llm chaining for my current project
Assistant: I'll help you apply llm chaining step by step...
```
## Related Skills
- Use with `workflow` tool for orchestrated execution
- Combine with `task` tool for delegated processing
- Reference `system-prompt` for system-level integration
