prompt-engineering
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.
npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-giuseppe-trisciuogli-developer-kit --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.
# 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 - Order examples from simple to
- Overview
- When to Use
- Core Patterns
- 1. Few-Shot Learning
- 2. Chain-of-Thought Reasoning
- 3. Prompt Optimization
- 4. Template Systems
- 5. System Prompt Design
- Implementation Workflows
- Workflow 1: Create New Prompt from Requirements
- Workflow 2: Optimize Existing Prompt
- Workflow 3: Scale Prompt Systems
- Quality Gates
- Best Practices
What does the prompt-engineering skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-engineering-giuseppe-trisciuogli-developer-kit --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.