Claude-skill-registry flow-convert-prompts-to-files

Convert inline prompts and prompt arrays to .prompt files with YAML frontmatter. Use when migrating prompts from Flow SDK format to Output SDK prompt files.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/flow-convert-prompts-to-files" ~/.claude/skills/majiayu000-claude-skill-registry-flow-convert-prompts-to-files && rm -rf "$T"
manifest: skills/data/flow-convert-prompts-to-files/SKILL.md
source content

Convert Prompts to .prompt Files

Overview

This skill guides the conversion of Flow SDK inline prompts, XML prompts, and JavaScript prompt arrays to Output SDK

.prompt
files with YAML frontmatter.

When to Use This Skill

During Migration:

  • Converting
    prompts.ts
    or
    prompts.xml
    to
    .prompt
    files
  • Extracting inline prompts from activities to separate files
  • Setting up prompt versioning

Flow SDK Prompt Formats

Flow SDK uses several prompt formats that need conversion:

1. Inline Prompts in Activities

// activities.ts
const prompt = `You are an assistant. Analyze: ${text}`;
const response = await completion( { messages: [ { role: 'user', content: prompt } ] } );

2. JavaScript Prompt Arrays (prompts.ts)

// prompts.ts
export const analyzePrompt = [
  { role: 'system', content: 'You are an expert analyst.' },
  { role: 'user', content: 'Analyze this: {{text}}' }
];

3. XML Prompts (prompts.xml)

<prompt name="analyze">
  <system>You are an expert analyst.</system>
  <user>Analyze this: {{text}}</user>
</prompt>

Output SDK .prompt File Format

Basic Structure

---
provider: openai
model: gpt-4o
temperature: 0.7
---

<system>
System message here.
</system>

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

YAML Frontmatter Fields

FieldTypeRequiredDescription
provider
stringYes
openai
or
anthropic
model
stringYesModel identifier
temperature
numberNo0-1 sampling temperature
max_tokens
numberNoMaximum output tokens

Common Model Values

OpenAI:

  • gpt-4o
  • gpt-4-turbo
  • gpt-3.5-turbo

Anthropic:

  • claude-3-5-sonnet-20241022
  • claude-3-opus-20240229
  • claude-3-haiku-20240307

Conversion Process

Step 1: Identify All Prompts

Find prompts in the Flow SDK workflow:

# Check for prompt files
ls src/workflows/my-workflow/prompts.*

# Check for inline prompts in activities
grep -n "role: 'system'" src/workflows/my-workflow/activities.ts
grep -n "role: 'user'" src/workflows/my-workflow/activities.ts

Step 2: Create .prompt File

Name format:

promptName@version.prompt

analyzeDocument@v1.prompt
generateSummary@v1.prompt
extractEntities@v1.prompt

Step 3: Convert Content

From Inline Prompt:

// Before (activities.ts)
const systemPrompt = 'You are a document analyzer.';
const userPrompt = `Analyze this document: ${documentText}`;
# After (analyzeDocument@v1.prompt)
---
provider: openai
model: gpt-4o
temperature: 0.3
---

<system>
You are a document analyzer.
</system>

<user>
Analyze this document: {{ documentText }}
</user>

From JavaScript Array:

// Before (prompts.ts)
export const summarizePrompt = [
  { role: 'system', content: 'You summarize text concisely.' },
  { role: 'user', content: 'Summarize: {{text}}\nMax length: {{maxLength}}' }
];
# After (summarize@v1.prompt)
---
provider: openai
model: gpt-4o
temperature: 0.5
---

<system>
You summarize text concisely.
</system>

<user>
Summarize: {{ text }}
Max length: {{ maxLength }}
</user>

From XML:

<!-- Before (prompts.xml) -->
<prompt name="extract">
  <system>You extract key entities from text.</system>
  <user>
    Extract entities from:
    {{#if includeContext}}
    Context: {{context}}
    {{/if}}
    Text: {{text}}
  </user>
</prompt>
# After (extract@v1.prompt)
---
provider: openai
model: gpt-4o
temperature: 0.2
---

<system>
You extract key entities from text.
</system>

<user>
Extract entities from:
{% if includeContext %}
Context: {{ context }}
{% endif %}
Text: {{ text }}
</user>

Step 4: Update Step to Use Prompt File

// Before (activities.ts)
import { summarizePrompt } from './prompts';

export async function summarize( text: string ): Promise<string> {
  const response = await completion( {
    model: 'gpt-4',
    messages: summarizePrompt.map( m => ( {
      ...m,
      content: m.content.replace( '{{text}}', text )
    } ) )
  } );
  return response.content;
}

// After (steps.ts)
import { step, z } from '@output.ai/core';
import { generateText } from '@output.ai/llm';

export const summarize = step( {
  name: 'summarize',
  inputSchema: z.object( { text: z.string() } ),
  outputSchema: z.string(),
  fn: async ( input ) => {
    const { result } = await generateText( {
      prompt: 'summarize@v1',
      variables: {
        text: input.text
      }
    } );
    return result;
  }
} );

Template Syntax Conversion

Important: Convert Handlebars to Liquid.js syntax!

HandlebarsLiquid.js
{{variable}}
{{ variable }}
{{#if cond}}
{% if cond %}
{{/if}}
{% endif %}
{{#each items}}
{% for item in items %}
{{/each}}
{% endfor %}
{{else}}
{% else %}

See

flow-convert-handlebars-to-liquid
for detailed conversion rules.

Complete Migration Example

Before: prompts.ts (Flow SDK)

export const analyzeDocumentPrompt = [
  {
    role: 'system',
    content: `You are a document analysis expert. Analyze documents for:
- Key themes
- Important entities
- Sentiment
- Action items`
  },
  {
    role: 'user',
    content: `Document Type: {{documentType}}

{{#if previousAnalysis}}
Previous Analysis:
{{previousAnalysis}}
{{/if}}

Document Content:
{{content}}

Provide a comprehensive analysis.`
  }
];

After: analyzeDocument@v1.prompt (Output SDK)

---
provider: openai
model: gpt-4o
temperature: 0.3
max_tokens: 4000
---

<system>
You are a document analysis expert. Analyze documents for:
- Key themes
- Important entities
- Sentiment
- Action items
</system>

<user>
Document Type: {{ documentType }}

{% if previousAnalysis %}
Previous Analysis:
{{ previousAnalysis }}
{% endif %}

Document Content:
{{ content }}

Provide a comprehensive analysis.
</user>

After: steps.ts (Using the Prompt)

import { step, z } from '@output.ai/core';
import { generateObject } from '@output.ai/llm';
import { AnalysisResultSchema, AnalysisResult } from './types.js';

const AnalyzeDocumentInputSchema = z.object( {
  documentType: z.string(),
  content: z.string(),
  previousAnalysis: z.string().optional()
} );

export const analyzeDocument = step( {
  name: 'analyzeDocument',
  inputSchema: AnalyzeDocumentInputSchema,
  outputSchema: AnalysisResultSchema,
  fn: async ( input ) => {
    const { result } = await generateObject<AnalysisResult>( {
      prompt: 'analyzeDocument@v1',
      variables: {
        documentType: input.documentType,
        content: input.content,
        previousAnalysis: input.previousAnalysis || ''
      },
      schema: AnalysisResultSchema
    } );

    return result;
  }
} );

Prompt Naming Convention

{descriptiveName}@{version}.prompt

Examples:
- analyzeDocument@v1.prompt
- generateSummary@v1.prompt
- extractEntities@v2.prompt
- translateContent@v1.prompt

Verification Checklist

  • All prompts extracted to
    .prompt
    files
  • YAML frontmatter includes provider and model
  • Template syntax converted to Liquid.js
  • Variable spacing correct:
    {{ var }}
    not
    {{var}}
  • Steps use
    generateText()
    or
    generateObject()
    with prompt reference
  • Prompt file names follow naming convention

Related Skills

  • flow-convert-handlebars-to-liquid
    - Template syntax conversion
  • flow-convert-activities-to-steps
    - Step conversion
  • flow-analyze-prompts
    - Prompt cataloging