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name: prompt-writing description: Write effective prompts for AI systems — system prompts, agent instructions, skills, or any LLM prompt. Use when creating or improving prompts.

Prompt Writing

Good prompts are clear and concise, not long and verbose. Longer prompts cause attention dilution — the model pays less attention to each part.

What Improves Prompts

Remove Redundancy

Same instruction said multiple ways dilutes attention.

Before: "You must always verify each item. Do not skip items. Every item needs to be checked. Make sure you don't miss any items."

After: "Verify each item individually. No skipping."

Remove Noise

Don't teach the model things it already knows. State your specific requirements.

Before: 40-line example of what a code walkthrough looks like

After: "Write a detailed walkthrough: what changed, line numbers, analysis of the flow, data transformations, dependencies."

Sharpen Instructions

"Do X" is clearer than "You should consider doing X because..."

Before: "It's important that you remember to always navigate to the project root directory before starting any work, as this ensures that all file paths will be correct..."

After: cd "$CLAUDE_PROJECT_DIR"

Keep Load-Bearing Content

These must stay:

• Workflow steps and their order
• Quality criteria
• Critical rules and constraints
• Output format requirements (if parsed programmatically)
• Behavioral guardrails

Write Clean Prose

Write as if it was always this way — not "correction" style.

Before: "Remember that you should do TWO interviews, not just one. The first interview is for business requirements, and then after discovery you should do a second interview..."

After:

Copy### 0. Business Requirements Interview
Interview the user to understand what needs to be built.

### 2. Technical Interview
With discovery complete, interview the user about implementation details.

Structure

Good prompts have clear sections. Adapt this pattern to your use case:

Copy[1-2 sentence role/purpose]

## Core Concept or Approach
[Key principle guiding behavior]

## Workflow / Steps
[What to do, in order]

## Rules / Constraints
[Non-negotiable requirements]

## Quality Criteria
[What good looks like, what to avoid]

## Output Format
[Expected structure if applicable]

For procedural agents, number the steps clearly. For guidance prompts, use descriptive sections.

Common Patterns

Workflow Steps: Numbered, clear action, what to produce, when to proceed.

Quality Criteria: Two lists — DO (what matters) and DON'T (what to ignore). Prevents false positives and negatives.

Output Formats: If parsed programmatically, include exact schema. If human-readable, describe what to include.

Checklist

• Every instruction serves a purpose (no redundancy)
• No verbose examples of things the model knows
• Instructions are direct, not hedged
• Load-bearing content preserved
• Written in clean prose, not "correction" style