| name | prompt-engineering |
| description | Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.
|
| allowed-tools | Read, Write, Edit, Glob, Grep, Bash |
Prompt Engineering
Overview
Use this skill to design prompt systems that are clear, testable, and reusable.
It covers prompt drafting, optimization, evaluation, and production-oriented
patterns for few-shot prompting, reasoning workflows, templates, and system
prompts.
Keep the main workflow in this file and load the targeted reference files only
for the pattern you are applying.
When to Use
Use this skill when:
- A user asks to write, rewrite, or improve a prompt
- A prompt needs better structure, reliability, or output formatting
- Few-shot examples or reasoning scaffolds are needed
- A system prompt or reusable prompt template must be created
- An existing prompt needs measurable optimization and testing
Read the relevant files in references/ when you need deeper guidance on a
specific pattern.
Core Patterns
1. Few-Shot Learning
Example Selection Strategy
- Use
references/few-shot-patterns.md for comprehensive selection frameworks
- Balance example count (3-5 optimal) with context window limitations
- Include edge cases and boundary conditions in example sets
- Prioritize diverse examples that cover problem space variations
