output-dev-prompt-file

Creating .prompt Files

Overview

This skill documents how to create .prompt files for LLM operations in Output SDK workflows. Prompt files use YAML frontmatter for configuration and Liquid.js templating for dynamic content.

When to Use This Skill

• Creating prompts for LLM-powered workflow steps
• Configuring LLM provider settings (model, temperature, etc.)
• Using template variables in prompts
• Troubleshooting prompt formatting issues

Location Convention

Prompt files are stored INSIDE the workflow folder:

src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts
├── types.ts
└── prompts/
    ├── analyzeContent@v1.prompt
    ├── generateSummary@v1.prompt
    └── extractData@v2.prompt

Important: Prompts are workflow-specific and live inside the workflow folder, NOT in a shared location.

File Naming Convention

{promptName}@v{version}.prompt

Examples:

• generateImageIdeas@v1.prompt
• analyzeContent@v1.prompt
• summarizeText@v2.prompt

The version suffix (@v1, @v2) allows for prompt versioning without breaking existing code.

Basic Structure

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.7
maxTokens: 4096
---

<system>
System instructions go here.
</system>

<user>
User message with {{ variable }} placeholders.
</user>

YAML Frontmatter Options

Required Fields

---
provider: anthropic    # LLM provider: anthropic, openai, vertex
model: claude-sonnet-4  # Model identifier
---

Optional Fields

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.7       # 0.0 to 1.0, default varies by provider
maxTokens: 4096        # Maximum output tokens
providerOptions:       # Provider-specific options
  thinking:
    type: enabled
    budgetTokens: 2000
---

Common Provider Configurations

Anthropic (Claude)

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.7
maxTokens: 8192
---

Anthropic with Extended Thinking

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.7
maxTokens: 32000
providerOptions:
  thinking:
    type: enabled
    budgetTokens: 2000
---

OpenAI

---
provider: openai
model: gpt-5
temperature: 0.7
maxTokens: 4096
---

Vertex (Gemini)

---
provider: vertex
model: gemini-3-pro
temperature: 0.7
maxTokens: 8192
---

Message Blocks

Use XML-style tags to define message roles:

System Message

<system>
You are an expert at analyzing technical content.
Your responses should be clear and structured.
</system>

User Message

<user>
Please analyze the following content:

{{ content }}
</user>

Assistant Message (for few-shot examples)

<assistant>
I'll analyze this content step by step...
</assistant>

Liquid.js Templating

Variable Substitution

<user>
Analyze this content about {{ topic }}:

{{ content }}

Generate {{ numberOfIdeas }} ideas.
</user>

Conditional Content

<system>
You are an expert content analyzer.

{% if colorPalette %}
**Color Palette Constraints:** {{ colorPalette }}
{% endif %}

{% if artDirection %}
**Art Direction Constraints:** {{ artDirection }}
{% endif %}
</system>

Loops

<user>
Analyze each of these items:

{% for item in items %}
- {{ item.name }}: {{ item.description }}
{% endfor %}
</user>

Default Values

<user>
Generate {{ numberOfIdeas | default: 3 }} ideas for {{ topic }}.
</user>

Complete Example

Based on a real prompt file (generateImageIdeas@v1.prompt):

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.7
maxTokens: 32000
providerOptions:
  thinking:
    type: enabled
    budgetTokens: 2000
---

<system>
You are an expert at creating structured, precise infographic prompts optimized for Gemini's image generation model.

Your task is to generate prompts for informational infographics that illustrate key concepts from the provided content.

CRITICAL RULES you MUST follow:
- Use Markdown dashed lists to specify constraints
- Use ALL CAPS for "MUST" requirements to ensure strict adherence
- Include specific compositional constraints (e.g., rule of thirds, lighting)
- Always include negative constraints to prevent unwanted elements
- Keep each infographic focused on ONE clear concept

{% if colorPalette %}
**Color Palette Constraints:** {{ colorPalette }}
{% endif %}

{% if artDirection %}
**Art Direction Constraints:** {{ artDirection }}
{% endif %}
</system>

<user>
Generate {{ numberOfIdeas }} structured infographic prompts based on key topics from this content.

<content>
{{ content }}
</content>

Each prompt MUST follow this structure:

Create an infographic about [specific topic]. The infographic MUST follow ALL of these constraints:
- The infographic MUST use the reference images as a visual style guide
- The composition MUST follow the rule of thirds for visual balance
- The infographic MUST use clean, minimal design with simple lines and shapes
{% if colorPalette %}- The color palette MUST strictly follow: {{ colorPalette }}{% endif %}
{% if artDirection %}- The art direction MUST strictly follow: {{ artDirection }}{% endif %}
- NEVER include any watermarks, logos, or decorative overlays
- NEVER use generic AI art buzzwords like "hyperrealistic"

Focus on the most important concepts that would benefit from visual explanation.
</user>

Using Prompts in Steps

With generateText and Output.object()

import { generateText, Output } from '@outputai/llm';
import { z } from '@outputai/core';

const { output } = await generateText({
  prompt: 'generateImageIdeas@v1',  // References prompts/generateImageIdeas@v1.prompt
  variables: {
    content: 'Solar panel technology explained...',
    numberOfIdeas: 3,
    colorPalette: 'blue and green tones',
    artDirection: 'minimalist style'
  },
  output: Output.object({
    schema: z.object({
      ideas: z.array(z.string())
    })
  })
});
// output contains { ideas: [...] }

With generateText

import { generateText } from '@outputai/llm';

const { result } = await generateText({
  prompt: 'summarize@v1',
  variables: {
    content: 'Long article text...',
    maxLength: 200
  }
});
// result contains the generated text string

Using Skills with Prompts

Prompts can load skill files that provide lazy-loaded instructions to the LLM. Skills keep the initial context small while giving the LLM access to deep expertise on demand. See output-dev-skill-file for the full guide on creating skill files.

The simplest approach is colocated auto-discovery. Place .md files in a skills/ folder next to your prompt file:

src/workflows/{workflow-name}/
└── prompts/
    ├── writing_assistant@v1.prompt
    └── skills/
        ├── clarity_guidelines.md
        └── structure_guide.md

The prompt file does not need any special configuration. Output auto-discovers the skills/ directory and injects a load_skill tool the LLM can call. Mention load_skill in the system message so the LLM knows to use it:

<system>
You are an expert technical writing assistant.
Use load_skill to get the full instructions for any skill before applying it.
</system>

You can also list skill paths explicitly in frontmatter, or create inline skills in code. See output-dev-skill-file for all three methods.

Using Prompts with Agent

Prompts work with both generateText and the Agent class. Use Agent for multi-step tool loops and stateful conversations. See output-dev-agent-class for the full guide.

import { Agent, Output } from '@outputai/llm';

const agent = new Agent({
  prompt: 'writing_assistant@v1',
  variables: {
    content_type: 'documentation',
    focus: 'clarity',
    content: input.content
  },
  output: Output.object({ schema: reviewSchema }),
  maxSteps: 5
});
const { output } = await agent.generate();

Best Practices

1. Be Explicit About Requirements

<system>
CRITICAL RULES you MUST follow:
- Rule 1
- Rule 2
- NEVER do X
- ALWAYS do Y
</system>

2. Use XML Tags for Structure in User Messages

<user>
Analyze the following:

<content>
{{ content }}
</content>

<requirements>
{{ requirements }}
</requirements>
</user>

3. Provide Examples (Few-Shot)

<system>
You analyze sentiment. Return: positive, negative, or neutral.
</system>

<user>
"I love this product!"
</user>

<assistant>
positive
</assistant>

<user>
"{{ text }}"
</user>

4. Version Your Prompts

When making significant changes, create a new version:

• analyzeContent@v1.prompt - Original
• analyzeContent@v2.prompt - Improved with better examples

Update the step to use the new version:

prompt: 'analyzeContent@v2'  // Changed from v1

5. Handle Optional Variables

{% if optionalField %}
Additional context: {{ optionalField }}
{% endif %}

Common Patterns

Classification Prompt

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.3
---

<system>
You are a content classifier. Categorize content into exactly one category.
Available categories: {{ categories | join: ", " }}
</system>

<user>
Classify this content:

{{ content }}
</user>

Extraction Prompt

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.2
---

<system>
You extract structured data from text. Be precise and only include information explicitly stated.
</system>

<user>
Extract the following fields from this text:
{% for field in fields %}
- {{ field }}
{% endfor %}

Text:
{{ text }}
</user>

Generation Prompt

---
provider: anthropic
model: claude-sonnet-4
temperature: 0.8
---

<system>
You are a creative writer. Generate engaging content based on the given parameters.
</system>

<user>
Generate {{ count }} {{ type }} about {{ topic }}.

Requirements:
{{ requirements }}
</user>

Verification Checklist

• File located in prompts/ folder inside workflow directory
• File named {promptName}@v{version}.prompt
• YAML frontmatter includes provider and model
• Message blocks use proper XML tags (<system>, <user>, <assistant>)
• Variables use {{ variableName }} syntax
• Conditionals use {% if %}...{% endif %} syntax
• All required variables are documented or have defaults
• Step code references correct prompt name

Related Skills

• output-dev-skill-file - Creating skill files for prompts
• output-dev-agent-class - Using the Agent class with prompts
• output-dev-step-function - Using prompts in step functions
• output-dev-evaluator-function - Using prompts in evaluators
• output-dev-folder-structure - Understanding prompts folder location
• output-dev-workflow-function - Orchestrating LLM-powered steps