Prompt Engineering Skill

A comprehensive reference for writing production-quality prompts for LLM-based agents and assistants. Based on analysis of system prompts from OpenAI (GPT-5, Codex CLI), Anthropic (Claude Opus/Sonnet, Claude Code v2.0), Google (Gemini, Gemini CLI), xAI (Grok), Mistral (Le Chat), and 10+ agentic tools (Cursor, Windsurf, Devin, Manus, Copilot, Cline, Kiro, Aider, pi-coding-agent, OpenCode).

How to use this skill

This skill is organized into modules. Read the relevant module(s) for your task — don't load everything at once.

Before writing any prompt, read references/01-system-prompts.md. It covers the universal anatomy of a system prompt and patterns that apply regardless of use case.

Then read the module(s) specific to your task:

Universal principles

These apply to ALL prompt writing, regardless of module:

1. Examples beat descriptions. DON'T: "According to my memories..." DO: "Since you're at Kolb Antik..." is more effective than "Use memories naturally without mentioning the system."

2. Position matters. Content at the beginning and end of the prompt gets more attention than the middle. Put critical rules at the top.

3. Shorter is better for smaller models. Every unnecessary token dilutes attention from the important parts. A 2000-token prompt that works is better than a 5000-token prompt that's "more complete."

4. Positive framing outperforms negation. "Write short answers" works better than "Don't write long answers" — models focus on keywords, and with negation those keywords are ironically the undesired behavior.

5. Test with the target model. A prompt that works on GPT-4 or Claude Opus may fail completely on a 7B model. Always test with the model that will actually run the prompt.

6. Show, don't tell. Concrete examples, paired good/bad demonstrations, and sample outputs are more effective than abstract descriptions of desired behavior.

7. Structure helps comprehension. Use markdown headers, numbered lists, and clear section boundaries. For Claude specifically, XML tags work exceptionally well. For smaller models, keep structure simple — one level of headers, no deep nesting.

Model-specific notes

• Small models (7B-13B): Need explicit step-by-step instructions, multiple examples per concept, precise output format specs, and shorter overall prompts. Avoid complex conditionals and nested logic.
• Mid-size models (14B-30B): Handle moderate complexity. Can follow structured prompts with 2-3 levels of hierarchy. Benefit from examples but can generalize from fewer of them. Tool calling may be unreliable — test thoroughly.
• Large models (70B+, frontier APIs): Follow nuanced instructions, handle complex conditionals, and generalize well. Can handle longer prompts and more abstract guidance. XML tags, complex tool schemas, and multi-step reasoning work reliably.

Sources and references

This skill synthesizes findings from:

• Leaked system prompts: ChatGPT/GPT-5 (Aug 2025), Claude Opus 4.6, Claude Code v2.0 (Sep 2025), Gemini 3 Flash (Jan 2026), Grok 4, Mistral Le Chat
• Agentic tool prompts: Cursor v1.0–v2.0 (GPT-5), Windsurf/Cascade Wave 11, Devin AI, Manus AI (event-driven), GitHub Copilot (VSCode Agent), Kiro (AWS), OpenAI Codex CLI, Google Gemini CLI, Cline
• Official docs: Anthropic prompt engineering guide, OpenAI GPT-4.1 prompting guide, Google Gemini 3 prompting guide, Mistral prompting docs
• Open-source agents: Aider, pi-coding-agent (@mariozechner), OpenCode
• Memory frameworks: Mem0, Letta/MemGPT, Claude Code Auto Dream
• Research: MemGPT paper (arXiv:2310.08560), Sleep-time Compute (arXiv:2504.13171), LoCoMo benchmark, LongMemEval