prompt-engineering
Optimize prompts for LLMs and AI systems with structured techniques, evaluation patterns, and synthetic test data generation. Use when building AI features, improving agent performance, or crafting system prompts.
Best use case
prompt-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Optimize prompts for LLMs and AI systems with structured techniques, evaluation patterns, and synthetic test data generation. Use when building AI features, improving agent performance, or crafting system prompts.
Teams using prompt-engineering 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/prompt-engineering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-engineering Compares
| Feature / Agent | prompt-engineering | 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?
Optimize prompts for LLMs and AI systems with structured techniques, evaluation patterns, and synthetic test data generation. Use when building AI features, improving agent performance, or crafting system prompts.
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.
Related Guides
SKILL.md Source
# Prompt Engineering Craft, test, and iterate prompts that deliver reliable outputs across LLMs. Covers prompt optimization techniques, structured prompt design, synthetic test data generation, and evaluation methodology. ## When to Use This Skill - Building or optimizing prompts for AI-powered features - Crafting system prompts for agents or assistants - Improving reliability and consistency of LLM outputs - Generating synthetic test data to validate prompt behavior - Evaluating prompt performance across edge cases - Designing prompt chains and pipelines ## Quick Reference | Task | Load reference | | --- | --- | | Prompt techniques and patterns | `skills/prompt-engineering/references/techniques.md` | | Synthetic test data generation | `skills/prompt-engineering/references/synthetic-data.md` | ## Workflow 1. **Research**: Gather the use case, constraints, and evaluation criteria. Audit existing prompts and model behaviors. 2. **Design**: Draft structured prompts with examples, constraints, and evaluation hooks. Plan experiments and measurement strategy. 3. **Generate test data**: Analyze prompt variables, generate diverse and realistic test cases to validate the prompt. 4. **Validate**: Run prompt trials, capture outputs, document adjustments. Iterate until quality thresholds are met. 5. **Deliver**: Hand off the final prompt with usage guidance and evaluation results. ## Core Principle When creating prompts, always display the complete prompt text in a clearly marked section. Never describe a prompt without showing it. The prompt must be copyable and self-contained. ## Deliverables Checklist For every prompt engineering task, produce: - [ ] The complete prompt text (displayed in full, properly formatted) - [ ] Explanation of design choices and techniques used - [ ] Usage guidelines (model, temperature, parameters) - [ ] Example expected outputs - [ ] Test cases covering happy path, edge cases, and adversarial inputs ## Example Interactions - "Optimize this system prompt for our code review agent" - "Create a prompt for extracting structured data from support tickets" - "Generate test cases to validate this classification prompt" - "Design a prompt chain for multi-step document analysis" - "Improve consistency of this summarization prompt"
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