Output Build Workflow

Your task is to implement an Output.ai workflow based on a provided plan document.

The workflow directory is provided as an argument (the workflow directory path). The workflow skeleton should already have been created there; if it has not, create it first.

Please read the plan file and implement the workflow according to its specifications.

Use the todo tool to track your progress through the implementation process.

Implementation Rules

Overview

Implement the workflow described in the plan document, following Output SDK patterns and best practices.

<pre_flight_check>

EXECUTE: Claude Skill: output-meta-pre-flight

</pre_flight_check>

<process_flow>

Step 1: Plan Analysis

Read and understand the plan document.

1. Read the plan file from the provided plan file path
2. Identify the workflow name, description, and purpose
3. Extract input and output schema definitions
4. List all required steps and their relationships
5. Note any LLM-based steps that require prompt templates
6. Understand error handling and retry requirements

</step>

Step 2: Workflow Implementation

Update workflow.ts in the workflow directory with the workflow definition.

<implementation_checklist>

• Import required dependencies (workflow, z from '@outputai/core')
• Define inputSchema based on plan specifications
• Define outputSchema based on plan specifications
• Import step functions from steps.ts
• Implement workflow function with proper orchestration
• Handle conditional logic if specified in plan
• Add proper error handling
• When catching a specific step or evaluator error, use hasErrorType(error, ErrorClass) instead of instanceof (see output-error-try-catch)

</implementation_checklist>

<workflow_template>

import { workflow, z } from '@outputai/core';import { stepName } from './steps.js';
const inputSchema = z.object( {  // Define based on plan} );
const outputSchema = z.object( {  // Define based on plan} );
export default workflow( {  name: 'workflow-name-from-plan',  description: 'Description from plan',  inputSchema,  outputSchema,  fn: async input => {    // Implement orchestration logic from plan    const result = await stepName( input );    return { result };  }} );

</workflow_template>

</step>

Step 3: Steps Implementation

Update steps.ts in the workflow directory with all step definitions from the plan.

<implementation_checklist>

• Import required dependencies (step, z from '@outputai/core')
• Implement each step with proper schema validation
• Add error handling and retry logic as specified
• Ensure step names match plan specifications
• Add descriptive comments for complex logic

</implementation_checklist>

<step_template>

import { step, z } from '@outputai/core';
export const stepName = step( {  name: 'stepName',  description: 'Description from plan',  inputSchema: z.object( {    // Define based on plan  } ),  outputSchema: z.object( {    // Define based on plan  } ),  fn: async input => {    // Implement step logic from plan    return output;  }} );

</step_template>

</step>

Step 3.5: Evaluators Implementation (if needed)

If the plan includes evaluator functions, implement them in evaluators.ts in the workflow directory.

<decision_tree>

IF plan_includes_evaluators: CREATE evaluators.ts IMPLEMENT evaluator functions per plan ELSE: SKIP to step 4

</decision_tree>

<implementation_checklist>

• Import required dependencies (evaluator, z, result types from '@outputai/core')
• Import generateText and aiSdk from @outputai/llm if using LLM-powered evaluators
• Implement each evaluator with proper schema validation
• Use appropriate result types (EvaluationBooleanResult, EvaluationNumberResult, EvaluationStringResult)
• Include confidence scores (0.0-1.0)
• Add reasoning for transparency
• All imports use .js extension
• Consider offline eval tests for dataset-driven verification (see output-dev-eval-testing skill)

</implementation_checklist>

<evaluator_template>

import { evaluator, z, EvaluationBooleanResult } from '@outputai/core';
export const evaluateName = evaluator( {  name: 'evaluate_name',  description: 'Description from plan',  inputSchema: z.object( {    // Define based on plan  } ),  fn: async input => {    // Implement evaluation logic from plan    return new EvaluationBooleanResult( {      value: true,      confidence: 0.95,      reasoning: 'Explanation of evaluation'    } );  }} );

</evaluator_template>

</step>

Step 4: Prompt Templates (if needed)

If the plan includes LLM-based steps, create prompt templates in the prompts/ subdirectory of the workflow directory.

<decision_tree>

IF plan_includes_llm_steps: CREATE prompt_templates UPDATE steps.ts to use loadPrompt and generateText ELSE: SKIP to step 6

</decision_tree>

<llm_step_template>

import { step, z } from '@outputai/core';import { generateText } from '@outputai/llm';
export const llmStep = step( {  name: 'llmStep',  description: 'LLM-based step',  inputSchema: z.object( {    param: z.string()  } ),  outputSchema: z.string(),  fn: async ( { param } ) => {    const { result } = await generateText( {      prompt: 'prompt_name@v1',      variables: { param }    } );    return result;  }} );

</llm_step_template>

<prompt_file_template>

---provider: anthropic# current as of 2026-05-04 — run output-dev-model-selection for the latestmodel: claude-sonnet-4-6temperature: 0.7---
<assistant>You are a helpful assistant.</assistant>
<user>
</user>

</prompt_file_template>

</step>

Step 5: README Update

Update README.md in the workflow directory with workflow-specific documentation.

<documentation_requirements>

• Update workflow name and description
• Document input schema with examples
• Document output schema with examples
• Explain each step's purpose
• Provide usage examples
• Document any prerequisites or setup requirements
• Include testing instructions

</documentation_requirements>

</step>

Step 6: Scenario File Creation

Create at least one scenario file in the scenarios/ subdirectory of the workflow directory for testing the workflow.

<scenario_requirements>

• Create scenarios/ directory if it doesn't exist
• Create test_input.json with valid example input matching the inputSchema
• Input values should be realistic and demonstrate the workflow's purpose
• JSON must be valid and parseable

</scenario_requirements>

<scenario_template>

{  // Populate with example values matching inputSchema  // Use realistic test data that demonstrates the workflow}

</scenario_template>

<example>

For a workflow with inputSchema:

z.object( {  topic: z.string(),  maxLength: z.number().optional()} )

Create scenarios/test_input.json:

{  "topic": "The history of artificial intelligence",  "maxLength": 500}

</example>

</step>

Step 7: Implementation Validation

Verify the implementation is complete and correct.

<validation_checklist>

• All steps from plan are implemented
• Input/output schemas match plan specifications
• Workflow orchestration logic is correct
• Error handling is in place
• LLM prompts are created (if needed)
• Evaluators are implemented (if specified in plan)
• Evaluators use correct result types and confidence scores
• README is updated with accurate information
• Code follows Output SDK patterns
• TypeScript types are properly defined
• Scenario file exists with valid example input
• Offline eval tests created (if applicable)

</validation_checklist>

</step>

Step 8: Post-Flight Check

Verify the implementation is ready for use.

<post_flight_check>

EXECUTE: Claude Skill: output-meta-post-flight

</post_flight_check>

</step>

</process_flow>

---- START ----

Use the workflow name, workflow directory, and plan file path provided as arguments, along with any additional instructions the user provided.