
Prompt Engineering
This one gives you systematic workflows for writing and optimizing LLM prompts instead of guessing what works. It covers few-shot example selection (3-5 examples, simple to complex ordering), chain-of-thought scaffolding with verification steps, and template composition with conditional sections. The optimization workflow is solid: baseline metrics first, single-variable A/B tests, revert if accuracy drops. It loads targeted reference files for specific patterns rather than dumping everything at once. Use it when you need to debug why a prompt isn't working, structure few-shot examples that actually improve results, or build reusable prompt templates with measurable performance tracking.
- skills.sh installs
- 4.1K
- GitHub stars
- 369
- last updated
- Aug 21, 2026
Install
$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill prompt-engineering --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
Sponsors
Details
- Repository
- github.com/giuseppe-trisciuoglio/developer-kit
- Categories
- AI & Agent Building, Debugging, Design & UI/UX
- First seen
- May 16, 2026
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