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 number="1" name="plan_analysis" subagent="workflow-context-fetcher">

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 number="2" name="workflow_implementation" subagent="workflow-quality">

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 number="3" name="steps_implementation" subagent="workflow-quality">

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 number="3.5" name="evaluators_implementation" subagent="workflow-quality">

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 number="4" name="prompt_templates" subagent="workflow-prompt-writer">