output-dev-prompt-file

name	output-dev-prompt-file
description	Create .prompt files for LLM operations in Output SDK workflows. Use when designing prompts, configuring LLM providers, or using Liquid.js templating.
allowed-tools	["Read","Write","Edit"]

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/
    ├── [e-mail]
    ├── [e-mail]
    └── [e-mail]

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

File Naming Convention

{promptName}@v{version}.prompt

Examples:

• [e-mail]
• [e-mail]
• [e-mail]

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

Basic Structure

Picking a model? See output-dev-model-selection for the current decision tree and AI Gateway lookup script. Examples below show concrete IDs as of 2026-05-04 — refresh them with that skill.

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
temperature: 0.7
maxTokens: 4096
---

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

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

The body uses exactly one mode:

• Message mode starts with a role tag and produces messages. Use it with generateText, generateTextWithStreaming, streamText, and Agent.
• Instruction mode starts with plain text and produces instructions. Use it with generateImage or when consuming loadPrompt() results directly:

---
provider: openai
model: gpt-image-1
---

Create a cinematic image of {{ subject }}.

Leading whitespace and HTML comments do not affect mode selection. Once plain text selects instruction mode, later tag-shaped text remains part of the instructions.

YAML Frontmatter Options

Required Fields

---
provider: anthropic    # LLM provider: anthropic, openai, google-vertex, amazon-bedrock, azure, perplexity
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
---

Provider Consistency

All prompt files in a workflow should use the same provider unless the user explicitly requests otherwise. Mixing providers (e.g., some prompts using anthropic and others using openai) requires the user to have API keys for all providers, which causes runtime failures if they don't.

When no existing prompts dictate a provider, default to anthropic. For the model itself, see output-dev-model-selection — it walks priority (reasoning/balance/speed/cost), provider lookup, and produces a current model ID.

Optional Fields

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
temperature: 0.7       # 0.0 to 1.0, default varies by provider
maxTokens: 4096        # Maximum output tokens
maxSteps: 5            # Tool-loop ceiling when tools or skills are present (default 10)
skills:                # Skill file or directory paths, relative to this prompt
  - ./skills
providerOptions:       # Provider-specific options
  thinking:
    type: enabled
    budgetTokens: 2000
---

Frontmatter is a strict camelCase allowlist. Unknown top-level keys throw Invalid prompt file. A snake_case alias of a known field fails with a suggestion (max_tokens -> use maxTokens). Put provider-specific keys (effort, reasoningEffort, topP) under providerOptions, which stays open. Nested thinking stays open too (budgetTokens is the documented key; extra nested keys are not rejected as unknown top-level config).

Allowed top-level keys: provider, model, temperature, maxTokens, maxSteps, skills, tools, providerOptions, messageOptions, n, maxImagesPerCall, size, aspectRatio, seed.

Call arguments: prompt, promptDir, variables, tools, output, toolChoice, stopWhen, abortSignal on generateText (plus onChunk on generateTextWithStreaming; plus onChunk / onFinish / onError on streamText). generateImage: prompt, promptDir, variables, images, mask, abortSignal.

Common Provider Configurations

Each example below pins a model that was current as of 2026-05-04. Run output-dev-model-selection when picking or refreshing.

Anthropic (Claude)

---
provider: anthropic
model: claude-sonnet-4-6
temperature: 0.7
maxTokens: 8192
---

Anthropic with Extended Thinking

---
provider: anthropic
model: claude-sonnet-4-6
temperature: 0.7
maxTokens: 32000
providerOptions:
  thinking:
    type: enabled
    budgetTokens: 2000
---

OpenAI

---
provider: openai
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: gpt-5-5
temperature: 0.7
maxTokens: 4096
---

Google Vertex (Gemini)

---
provider: google-vertex
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: gemini-3-pro
temperature: 0.7
maxTokens: 8192
---

Message Blocks

Message mode uses a small XML-like syntax, not a general HTML or XML parser. The only valid top-level role tags are <system>, <user>, and <assistant>.

Do not author <tool> blocks. AI SDK tool results are structured message parts tied to a preceding tool call; AI SDK creates them during execution, and Agent callers may supply them through messages or messageStore.

Follow these parser rules:

• Put only whitespace or HTML comments between top-level role blocks. Root text and self-closing blocks are invalid.
• Close every top-level role block with the matching tag.
• Different-name tags inside a message, such as <context> inside <user>, remain message content.
• Do not nest a non-self-closing tag with the same name as its containing message. Escape literal examples as <user>example</user>.
• Code and HTML-like text inside a message are preserved, including forms such as Array<string>.
• On role tags, only the options attribute is supported. It must have a value naming one or more frontmatter messageOptions sets, for example options="cached fast". Bare options and unknown attributes throw when the prompt loads.

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 ([e-mail]):

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
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>

Structured Variables

The variables field in generateText and Agent accepts scalars, nested objects, and arrays. Pass structured data directly when the prompt benefits from Liquid loops, conditions, or dot notation:

const { output } = await generateText( {
  prompt: 'rank@v1',
  variables: {
    stories: storyArray,
    interests: interestArray
  }
} );

{% for story in stories %}
- {{ story.title }} (score: {{ story.score }}, by: {{ story.author }})
{% endfor %}

Interests: {{ interests | join: ", " }}

Pre-format data in the step only when the exact rendered text is application logic rather than prompt presentation.

Using Prompts in Steps

With generateText and aiSdk.Output.object()

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

const { output } = await generateText( {
  prompt: 'generateImageIdeas@v1',  // References prompts/[e-mail]
  variables: {
    content: 'Solar panel technology explained...',
    numberOfIdeas: 3,
    colorPalette: 'blue and green tones',
    artDirection: 'minimalist style'
  },
  output: aiSdk.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.

Place .md files next to the prompt (commonly in prompts/skills/) and list the path in frontmatter. A sibling skills/ folder is not loaded unless you list it:

src/workflows/{workflow-name}/
└── prompts/
    ├── [e-mail]
    └── skills/
        ├── clarity_guidelines.md
        └── structure_guide.md

---
provider: anthropic
model: claude-sonnet-4-6
skills:
  - ./skills
---

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>

List skills: paths in this prompt's frontmatter. See output-dev-skill-file for the file format and path rules.

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, aiSdk } from '@outputai/llm';

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

CRITICAL: Prompts and Structured Output Schemas

Do Not Duplicate the Schema in the Prompt

When a step uses aiSdk.Output.object() with generateText, the Zod schema is automatically sent to the LLM provider as a tool definition. The LLM already knows the exact JSON shape it must return. Do not also specify the schema in the prompt.

This is a best practice documented by multiple LLM providers:

• Anthropic: The schema is sent as a tool definition; .describe() on fields is how you guide the model's output. The SDK automatically transforms unsupported constraints into field descriptions.
• Google Vertex AI: "Only specify the schema in the schema object. Don't also specify the schema in the prompt. Doing both can reduce performance." If you must discuss the schema in the prompt, match the exact field order from the schema.

Why this matters:

1. Performance: Redundant schema instructions can confuse the model and reduce output quality
2. Maintenance: When the schema changes, you must update both the schema AND the prompt, or they drift apart
3. Correctness: The prompt's JSON examples can contradict the actual schema (wrong field names, missing fields, wrong types)

What NOT to Include in Prompts

When aiSdk.Output.object() is used, do not include any of these in the prompt:

• ## Output Format sections describing the JSON shape
• JSON examples showing the expected response structure
• Field-by-field descriptions that mirror the schema
• Instructions like "Return a JSON object with exactly these fields"
• Instructions like "Return only the JSON object with no surrounding explanation"

<!-- WRONG - prompt duplicates what aiSdk.Output.object() already sends -->
<system>
## Output Format
Return a JSON object with this shape:
{
  "title": "string",
  "summary": "string",
  "tags": ["string"]
}
</system>

What TO Include in Prompts

Use the prompt for quality expectations, domain knowledge, and content guidance -- things the schema cannot express:

<!-- CORRECT - prompt focuses on content quality, not structure -->
<system>
Write a concise, specific title (under 80 characters).
The summary should capture the main argument, not just the topic.
Choose tags from the reader's domain -- avoid generic terms like "technology".
</system>

Use .describe() on Schema Fields Instead

The right place to communicate field-level expectations is on the schema itself, using .describe(). LLM providers use these descriptions when generating output:

// In types.ts -- .describe() guides the LLM on each field
const ArticleSummarySchema = z.object( {
  title: z.string().describe( 'Concise title under 80 characters' ),
  summary: z.string().describe( 'One-sentence summary capturing the main argument' ),
  tags: z.array( z.string() ).describe( '3-5 domain-specific tags, avoid generic terms' )
} );

The schema handles structure AND field-level guidance; the prompt handles task framing, methodology, and quality standards.

When the Step Does NOT Use aiSdk.Output.object()

If generateText is called without aiSdk.Output.object() (plain text output), then including output format instructions in the prompt is appropriate since no schema is sent to the provider.

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:

• [e-mail] - Original
• [e-mail] - 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

The model lines in the patterns below were current as of 2026-05-04. Refresh via output-dev-model-selection when copying into a new prompt.

Classification Prompt

---
provider: anthropic
model: claude-sonnet-4-6
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-4-6
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
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
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
• Frontmatter uses camelCase only; no unknown top-level keys (topP, effort, reasoningEffort, max_tokens)
• The body uses message mode for text generation, or instruction mode for generateImage / direct loadPrompt() consumption
• Message blocks use supported role tags (<system>, <user>, <assistant>); no authored <tool> blocks
• Message mode has no root text between blocks, no self-closing role blocks, and no unclosed blocks
• Literal same-name role tags inside a message are escaped (<user>...</user>)
• Role-tag attributes use only options="<messageOptions names>"; options is never bare
• Variables use {{ variableName }} syntax
• Conditionals use {% if %}...{% endif %} syntax
• All required variables are documented or have defaults
• Step code references correct prompt name
• No JSON output format instructions when step uses aiSdk.Output.object() (schema handles structure)
• If the prompt uses skills, frontmatter lists skills: paths (a sibling skills/ folder is not auto-loaded)
• If the prompt uses skills or tools, set when the default of 10 is wrong

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
• output-eval-judge-prompt — Methodology for designing effective LLM judge prompts