writing-prompts

Designs, writes, and reviews production-grade prompts for enterprise LLM agents.

Updated Mar 28, 2026
One-click install
npx skills add https://github.com/thedutchvisiongroup/agent-skills --skill writing-prompts-thedutchvisiongroup
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: writing-prompts
Source: https://github.com/thedutchvisiongroup/agent-skills/tree/main/skills/writing-prompts
Command: npx skills add https://github.com/thedutchvisiongroup/agent-skills --skill writing-prompts-thedutchvisiongroup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Writing prompts for production LLM agents often results in vague instructions, leaked system/user boundaries, missing guardrails, and brittle behavior. This Skill enforces a structured, research-backed workflow so prompts are clear, safe, and correctly layered before they ship. ## Core Features & Use Cases - Mandatory clarification gate: Confirms structure (XML default vs Markdown), system/user split, and output target before any drafting begins. - Layered prompt architecture: Orders role, instructions, tools, guardrails, examples, context, task, and output format for clarity and prompt-caching efficiency. - Review and audit tooling: Ships a review checklist plus references on techniques (few-shot, CoT, ReAct, Reflexion), reliability, injection defense, and context engineering. - Ready-to-adapt templates: Includes XML and Markdown templates for system prompts, user prompts, and combined prompts. - Use Case: You need a system prompt for a contract-analysis agent with tool access and strict grounding rules. The Skill walks you through the gate questions, then produces a separate system prompt (role, tools, guardrails, examples) and user prompt (context, task, reminder) in XML. ## Quick Start Ask the agent to write a production system prompt for your LLM agent and answer its clarification questions about structure, system/user split, and output format.

Frequently Asked Questions about writing-prompts

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a system prompt for an LLM agent?

Start by confirming the structure (XML is the default), whether a separate system prompt is needed, and the output target. Then layer role, instructions, tools, guardrails, examples, and output format in the system prompt, keeping dynamic context and the task in the user prompt.

Should I use XML or Markdown for prompt structure?

XML is the default for production agent prompts because tags like <instructions>, <context>, and <output_format> give explicit semantic boundaries and unambiguous nesting. Use Markdown when the user prefers it, the platform mandates it, or the prompt is short and single-purpose.

What goes in the system prompt vs the user prompt?

Stable content belongs in the system prompt: role, high-level instructions, tool definitions, guardrails, output contract, and few-shot examples. Dynamic content belongs in the user prompt: the task, per-request context or retrieved data, request parameters, and a closing restatement of critical constraints.

How do I protect an LLM agent from prompt injection?

Fence untrusted input with delimiters and label it explicitly as data, not instructions. Add a refusal-on-conflict rule, validate tool outputs before reuse, keep tools least-privilege, and repeat critical constraints in both the system prompt and a closing reminder.

When should I not use a structured prompt engineering workflow?

Skip it for quick one-off chat questions, fine-tuning dataset creation, and RAG pipeline architecture design. The workflow targets inference-time prompts for agents, not training data or retrieval and indexing design.