Output Dev Step Function

Creating Step Functions

Overview

This skill documents how to create step functions in steps.ts for Output SDK workflows. Steps are where all I/O operations happen - HTTP requests, LLM calls, database operations, file system access, etc.

When to Use This Skill

• Implementing I/O operations for a workflow
• Adding HTTP client integrations
• Implementing LLM-powered steps
• Handling errors with FatalError and ValidationError
• Creating reusable step components

File Organization

Option 1: Flat File (Default)

For smaller workflows, use a single steps.ts file:

src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts         # All steps in one file
├── types.ts
└── ...

Option 2: Folder-Based (Large workflows)

For larger workflows with many steps, use a steps/ folder:

src/workflows/{workflow-name}/
├── workflow.ts
├── steps/           # Steps split into individual files
│   ├── fetch_data.ts
│   ├── process.ts
│   └── validate.ts
├── types.ts
└── ...

Component Location Rules

Important: step() calls MUST be in files containing 'steps' in the path:

• src/workflows/my_workflow/steps.ts ✓
• src/workflows/my_workflow/steps/fetch_data.ts ✓
• src/shared/steps/common_steps.ts ✓
• src/workflows/my_workflow/helpers.ts ✗ (cannot contain step() calls)

Activity Isolation Constraints

Steps are Temporal activities with strict import rules to ensure deterministic replay.

Steps CAN import from:

• Local workflow files: ./utils.js, ./types.js, ./helpers.js
• Local subdirectories: ./clients/pokeapi.js, ./lib/helpers.js
• Shared utilities: ../../shared/utils/*.js
• Shared clients: ../../shared/clients/*.js
• Shared services: ../../shared/services/*.js

Steps CANNOT import:

• Other step files (even shared steps - workflows import those)
• Evaluator files
• Workflow files

Example of WRONG imports:

// WRONG - steps cannot import other steps
import { otherStep } from '../../shared/steps/other.js'; // ✗
import { anotherStep } from './other_steps.js'; // ✗

Critical Import Patterns

Core Imports

// CORRECT - Import from @outputai/core
import { step, z, FatalError, ValidationError } from '@outputai/core';

// WRONG - Never import z from zod
import { z } from 'zod';

HTTP Client Import

// CORRECT - Use @outputai/http wrapper
import { createKyClient } from '@outputai/http';

// WRONG - Never use axios directly
import axios from 'axios';

Related Skill: output-error-http-client

LLM Client Import

// CORRECT - Use @outputai/llm wrapper
import { generateText, aiSdk } from '@outputai/llm';

// WRONG - Never call LLM providers directly
import OpenAI from 'openai';

ES Module Imports

All imports MUST use .js extension:

// CORRECT
import { InputSchema, OutputSchema } from './types.js';
import { GeminiService } from '../../shared/clients/gemini_client.js';

// WRONG - Missing .js extension
import { InputSchema, OutputSchema } from './types';

Basic Structure

import { step, z, FatalError, ValidationError } from '@outputai/core';
import { createKyClient } from '@outputai/http';
import { generateText, aiSdk } from '@outputai/llm';

import { StepInputSchema, StepOutputSchema } from './types.js';

export const myStep = step( {
  name: 'myStep',
  description: 'Description of what this step does',
  inputSchema: StepInputSchema,
  outputSchema: StepOutputSchema,
  fn: async input => {
    // Implementation with I/O operations
    return { /* output matching outputSchema */ };
  }
} );

Required Properties

name (string)

Unique identifier for the step. Use camelCase.

name: 'generateImageIdeas'

description (string)

Human-readable description of the step's purpose.

description: 'Generate creative infographic prompt ideas using Claude'

inputSchema (Zod schema)

Schema for validating step input. Define in types.ts and import.

inputSchema: z.object( {
  content: z.string(),
  numberOfIdeas: z.number()
} )

outputSchema (Zod schema)

Schema for validating step output. Define in types.ts and import.

outputSchema: z.array( z.string() )

fn (async function)

The step execution function. This is where I/O operations happen.

fn: async input => {
  const result = await someExternalService( input );
  return result;
}

HTTP Client Usage

Creating an HTTP Client

import { createKyClient } from '@outputai/http';
import { FatalError, ValidationError } from '@outputai/core';

const RETRY_STATUS_CODES = [ 408, 429, 500, 502, 503, 504 ];
const FATAL_STATUS_CODES = [ 401, 403, 404 ];

const client = createKyClient( {
  timeout: 30000,
  retry: {
    limit: 3,
    statusCodes: RETRY_STATUS_CODES
  },
  hooks: {
    beforeError: [
      ( { error } ) => {
        const status = error.response?.status;
        const message = error.message;

        if ( status && FATAL_STATUS_CODES.includes( status ) ) {
          throw new FatalError(
            `HTTP ${status} error: ${message}. This is a permanent error.`
          );
        }

        throw new ValidationError(
          `HTTP request failed: ${message}`
        );
      }
    ]
  }
} );

Making HTTP Requests

// GET request
const response = await client.get( 'https://api.example.com/data' );
const data = await response.json();

// POST request with JSON body
const response = await client.post( 'https://api.example.com/submit', {
  json: { field: 'value' }
} );

// HEAD request (check URL accessibility)
const response = await client.head( url );
const contentType = response.headers.get( 'content-type' );

When a non-HEAD request only uses response metadata, such as response.url, response.status, or headers, cancel the unused body in a finally block. Responses read with .json(), .text(), etc. are already consumed.

const response = await client.get( url );

try {
  return response.url;
} finally {
  await response.body?.cancel();
}

Related Skill: output-dev-http-client-create for creating shared clients

LLM Operations

Important: Define LLM Schemas in types.ts

Schemas used in aiSdk.Output.object() must be defined in types.ts and imported -- never defined inline in step functions. Inline schemas lead to duplication, drift between the step's outputSchema and the LLM schema, and make it harder to maintain types.

// WRONG - inline schema in aiSdk.Output.object()
output: aiSdk.Output.object( {
  schema: z.object( {
    analysis: z.string()
  } )
} )

// CORRECT - import from types.ts
import { AnalysisLlmSchema } from './types.js';
// ...
output: aiSdk.Output.object( {
  schema: AnalysisLlmSchema
} )

Using generateText with aiSdk.Output.object()

The variables field accepts scalars, nested objects, and arrays. Use Liquid loops and dot notation in the prompt when it owns presentation; pre-format in the step when the exact rendered text is application logic.

generateText arguments: prompt, promptDir, variables, tools, output, toolChoice, stopWhen, abortSignal.

import { generateText, aiSdk } from '@outputai/llm';
import {
  AnalyzeContentInputSchema,
  AnalyzeContentOutputSchema,
  AnalysisLlmSchema
} from './types.js';

export const analyzeContent = step( {
  name: 'analyzeContent',
  description: 'Analyze content using Claude',
  inputSchema: AnalyzeContentInputSchema,
  outputSchema: AnalyzeContentOutputSchema,
  fn: async ( { content } ) => {
    const { output } = await generateText( {
      prompt: 'analyzeContent@v1',
      variables: {
        content
      },
      output: aiSdk.Output.object( {
        schema: AnalysisLlmSchema
      } )
    } );

    return { analysis: output.analysis };
  }
} );

Using generateText

import { generateText } from '@outputai/llm';
import { SummarizeInputSchema, SummarizeOutputSchema } from './types.js';

export const generateSummary = step( {
  name: 'generateSummary',
  description: 'Generate a text summary',
  inputSchema: SummarizeInputSchema,
  outputSchema: SummarizeOutputSchema,
  fn: async ( { content } ) => {
    const { result } = await generateText( {
      prompt: 'summarize@v1',
      variables: { content }
    } );

    return { summary: result };
  }
} );

Related Skill: output-dev-prompt-file for creating prompt files

Streaming LLM Progress

Prefer generateTextWithStreaming() in steps when the caller needs progress callbacks and a complete result:

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

const reportProgress = ( chunk: string ) => process.stdout.write( chunk );

const result = await generateTextWithStreaming( {
  prompt: 'summarize@v1',
  variables: { content },
  onChunk( { chunk } ) {
    if ( chunk.type === 'text-delta' ) {
      reportProgress( chunk.text );
    }
  }
} );

return result.result;

The returned promise rejects on stream failures, allowing Temporal to retry the activity. Use streamText() only when direct stream access is required; capture onError and throw the captured error after consumption. See output-dev-llm-streaming for complete patterns.

Error Handling

FatalError (Non-Retryable)

Use FatalError for permanent failures that should not be retried:

import { FatalError } from '@outputai/core';
import { credentials } from '@outputai/credentials';

// Authentication failures
if ( response.status === 401 ) {
  throw new FatalError( 'Invalid API key' );
}

// Invalid input that cannot be fixed by retry
if ( !input.requiredField ) {
  throw new FatalError( 'Missing required field: requiredField' );
}

// Resource not found
if ( response.status === 404 ) {
  throw new FatalError( `Resource not found: ${resourceId}` );
}

// Configuration errors
if ( !credentials.get( 'service.api_key' ) ) {
  throw new FatalError( 'service.api_key credential not set' );
}

ValidationError (Retryable)

Use ValidationError for temporary failures that may succeed on retry:

import { ValidationError } from '@outputai/core';

// Rate limiting
if ( response.status === 429 ) {
  throw new ValidationError( 'Rate limit exceeded, will retry' );
}

// Temporary service unavailability
if ( response.status === 503 ) {
  throw new ValidationError( 'Service temporarily unavailable' );
}

// Network errors
try {
  const response = await client.get( url );
} catch ( error ) {
  throw new ValidationError( `Network error: ${error.message}` );
}

// Empty response that might be temporary
if ( results.length === 0 ) {
  throw new ValidationError( 'No results returned, will retry' );
}

Related Skill: output-error-try-catch for proper error handling patterns

Complete Example

Based on a real workflow step:

import { step, z, FatalError, ValidationError } from '@outputai/core';
import { createKyClient } from '@outputai/http';
import { generateText, aiSdk } from '@outputai/llm';

import { GeminiImageService } from '../../shared/clients/gemini_client.js';
import {
  GenerateImageIdeasInputSchema,
  GenerateImagesInputSchema,
  ImageIdeasSchema
} from './types.js';

const RETRY_STATUS_CODES = [ 408, 429, 500, 502, 503, 504 ];
const FATAL_STATUS_CODES = [ 401, 403, 404 ];

const client = createKyClient( {
  timeout: 30000,
  retry: {
    limit: 3,
    statusCodes: RETRY_STATUS_CODES
  },
  hooks: {
    beforeError: [
      ( { error } ) => {
        const status = error.response?.status;
        const message = error.message;

        if ( status && FATAL_STATUS_CODES.includes( status ) ) {
          throw new FatalError( `HTTP ${status} error: ${message}` );
        }

        throw new ValidationError( `HTTP request failed: ${message}` );
      }
    ]
  }
} );

// Step 1: Generate Ideas using LLM
export const generateImageIdeas = step( {
  name: 'generateImageIdeas',
  description: 'Generate creative infographic prompt ideas using Claude',
  inputSchema: GenerateImageIdeasInputSchema,
  outputSchema: z.array( z.string() ),
  fn: async ( { content, numberOfIdeas, colorPalette, artDirection } ) => {
    const { output } = await generateText( {
      prompt: 'generateImageIdeas@v1',
      variables: {
        content,
        numberOfIdeas,
        colorPalette: colorPalette || '',
        artDirection: artDirection || ''
      },
      output: aiSdk.Output.object( {
        schema: ImageIdeasSchema
      } )
    } );

    return output.ideas;
  }
} );

// Step 2: Generate Images using external API
export const generateImages = step( {
  name: 'generateImages',
  description: 'Generate images using Gemini API',
  inputSchema: GenerateImagesInputSchema,
  outputSchema: z.array( z.string() ),
  fn: async ( { input, prompt } ) => {
    const geminiImageService = new GeminiImageService();

    const generatedImages = await geminiImageService.generateImage( {
      prompt,
      aspectRatio: input.aspectRatio,
      resolution: input.resolution,
      numberOfImages: input.numberOfGenerations
    } );

    if ( generatedImages.length === 0 ) {
      throw new ValidationError( 'No images were generated by Gemini' );
    }

    return generatedImages;
  }
} );

// Step 3: Validate URLs using HTTP client
export const validateReferenceImages = step( {
  name: 'validateReferenceImages',
  description: 'Validates that all provided reference image URLs are accessible',
  inputSchema: z.object( {
    referenceImageUrls: z.array( z.string() ).optional()
  } ),
  outputSchema: z.boolean(),
  fn: async ( { referenceImageUrls } ) => {
    if ( !referenceImageUrls || referenceImageUrls.length === 0 ) {
      return true;
    }

    for ( const [ index, url ] of referenceImageUrls.entries() ) {
      const response = await client.head( url );
      const contentType = response.headers.get( 'content-type' );

      if ( contentType && !contentType.startsWith( 'image/' ) ) {
        throw new FatalError(
          `Reference URL ${index + 1} (${url}) is not an image file`
        );
      }
    }

    return true;
  }
} );

Best Practices

1. One Responsibility Per Step

// Good - focused step
export const fetchUserData = step( {
  name: 'fetchUserData',
  description: 'Fetch user data from the API'
  // ...
} );

// Avoid - step doing too much
export const fetchAndProcessAndSaveUserData = step( {
  name: 'fetchAndProcessAndSaveUserData'
  // ...
} );

2. Clear Error Messages

// Good - specific error message
throw new FatalError( `Invalid API key for service: ${serviceName}` );

// Avoid - generic error message
throw new FatalError( 'Error occurred' );

3. Validate Input Early

fn: async input => {
  if ( !input.url.startsWith( 'https://' ) ) {
    throw new FatalError( 'URL must use HTTPS protocol' );
  }

  const response = await client.get( input.url );
  // ...
}

Verification Checklist

• step, z, FatalError, ValidationError imported from @outputai/core
• createKyClient imported from @outputai/http (not axios)
• generateText and aiSdk imported from @outputai/llm (not direct provider)
• Structured output uses aiSdk.Output.object() with .describe() (not .min()/.max()/.length()) on number and array schemas
• Schemas for aiSdk.Output.object() are defined in types.ts and imported, not inline
• All imports use .js extension
• Named exports used for each step
• Each step has name, description, inputSchema, outputSchema, fn
• FatalError used for non-retryable failures
• ValidationError used for retryable failures
• Non-HEAD HTTP responses are consumed or cancelled when only metadata is used
• No bare try-catch blocks that swallow errors
• Steps only import allowed dependencies (local files, shared code)
• No imports of other steps, evaluators, or workflows
• Code follows style conventions (see output-dev-code-style)

Related Skills

• output-dev-workflow-function - Orchestrating steps in workflow.ts
• output-dev-evaluator-function - Using steps in evaluator functions
• output-dev-types-file - Defining step input/output schemas
• output-dev-code-style - Code formatting and style conventions
• output-dev-http-client-create - Creating shared HTTP clients
• output-dev-llm-streaming - Streaming LLM progress with Temporal-safe failures
• output-dev-prompt-file - Creating prompt files for LLM operations
• output-error-try-catch - Proper error handling patterns
• output-error-direct-io - Avoiding direct I/O in workflows