Ai prompt engineering
📚 Agent skills distilled from technical books — AI Engineering, Context Engineering, Designing Data-Intensive Applications, and more. Agent-agnostic, plain Markdown. Give your AI agent a bookshelf.
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Practical knowledge for writing, organizing, and defending prompts for foundation models. Covers in-context learning (zero-shot, few-shot), system vs user prompts, context efficiency, prompting best practices (clear instructions, examples, decomposition, chain-of-thought), prompt versioning, and defensive prompt engineering against jailbreaks and prompt injection attacks. Use this skill when: - Writing or improving prompts for an LLM application - Setting up prompt templates and versioning - Breaking down a complex task into prompts - Defending against prompt injection or jailbreaking - Designing system prompts and context structures - Evaluating prompt engineering tools
SKILL.md
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AI Prompt Engineering
Knowledge from "AI Engineering" by Chip Huyen (Chapter 5). Focused on practical prompting techniques and defensive strategies.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- Apply guidance to your work
Contents
References
| Category | Purpose |
|---|---|
prompting-fundamentals | In-context learning, zero/few-shot, system vs user prompts, context length |
prompting-best-practices | Clear instructions, examples, task decomposition, chain-of-thought, iteration, versioning |
defensive-prompting | Jailbreaking, prompt injection (direct/indirect), information extraction, defenses |
Workflows
| Task | Workflow |
|---|---|
| Write a new prompt with best practices | workflows/build-prompt.md |
| Audit a prompt for security vulnerabilities | workflows/audit-prompt-security.md |
Guidelines
See guidelines.md for task-based file selection.