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

The workflow skeleton has already been created at: $3 (if not it should be)

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: $1
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 $3/workflow.ts 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 </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: '$2',
  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 $3/steps.ts 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 $3/evaluators.ts.

<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 Output 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">

Step 4: Prompt Templates (if needed)

If the plan includes LLM-based steps, create prompt templates in $3/prompts/.

<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 latest
model: claude-sonnet-4-6
temperature: 0.7
---

<assistant>
You are a helpful assistant.
</assistant>

<user>

</user>

</prompt_file_template>

</step>

<step number="5" name="readme_update">

Step 5: README Update

Update $3/README.md 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 number="6" name="scenario_creation">

Step 6: Scenario File Creation

Create at least one scenario file in $3/scenarios/ 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 number="7" name="validation" subagent="workflow-quality">

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 number="8" name="post_flight_check">

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>

<post_flight_check> EXECUTE: Claude Skill: output-meta-post-flight </post_flight_check>

---- START ----

Workflow Name: $2

Workflow Directory: $3

Plan File Path: $1

Additional Instructions:

$ARGUMENTS