Output Meta Project Context

# Development
npx output dev                              # Start dev environment

# List & inspect
npx output workflow list                    # List available workflows

# Execute
npx output workflow run <name> --input '{}'  # Run synchronously (waits)
npx output workflow start <name> --input '{}' # Run async (returns ID)
npx output workflow status <id>              # Check async status
npx output workflow result <id>              # Get async result

# Debug
npx output workflow debug <id>               # Debug failed workflow
npx output workflow debug <id> --json # Machine-readable output

# Rerun from a step (replays up to <stepName>, re-executes everything after)
npx output workflow reset <id> --step <stepName>
npx output workflow reset <id> --step <stepName> --reason "why"

# Eval Testing
npx output workflow test <name>              # Run eval tests against datasets
npx output workflow test <name> --cached     # Use cached output (fast)
npx output workflow test <name> --save       # Run fresh and save results
npx output workflow dataset list <name>      # List datasets for a workflow
npx output workflow dataset generate <name> --input '{}'  # Generate dataset

# Credentials
npx output credentials init                  # Initialize encrypted credentials
npx output credentials edit                  # Edit credentials (decrypts, opens $EDITOR)
npx output credentials set <path> <value>    # Set single credential value
npx output credentials show                  # Show decrypted credentials
npx output credentials get <path>            # Get single credential value

import { workflow, z } from '@outputai/core';
import { fetchData, processData } from './steps.js';

export const inputSchema = z.object( { url: z.string().url() } );
export const outputSchema = z.object( { result: z.string() } );

export default workflow( {
  name: 'my_workflow',
  description: 'Processes data from URL',
  inputSchema,
  outputSchema,
  fn: async input => {
    const data = await fetchData( input.url );
    const result = await processData( data );
    return { result };
  }
} );

import { step, z } from '@outputai/core';
import { createKyClient } from '@outputai/http';

export const fetchData = step(
  { name: 'fetchData', inputSchema: z.string(), outputSchema: z.any() },
  async url => {
    const client = createKyClient( { prefix: url } );
    const response = await client.get( '' );
    return response.json();
  }
);

// src/shared/clients/example.ts
import { FatalError, ValidationError } from '@outputai/core';
import { createKyClient } from '@outputai/http';
import { credentials } from '@outputai/core/credentials';

const API_KEY = credentials.require( 'example.api_key' );

const client = createKyClient( {
  prefix: 'https://api.example.com',
  headers: { Authorization: `Bearer ${API_KEY}` },
  timeout: 30000,
  retry: { limit: 3, statusCodes: [ 408, 429, 500, 502, 503, 504 ] }
  // If this is a paid API, add an `afterResponse` cost hook — see
  // output-dev-http-client-create. Free/internal services need nothing more.
} );

export async function fetchFromExample( query: string ): Promise<ExampleResponse> {

  try {
    const response = await client.get( 'endpoint', { searchParams: { q: query } } );
    return response.json();
  } catch ( error: unknown ) {
    const err = error as { status?: number; message?: string };
    if ( err.status === 401 || err.status === 403 ) {
      throw new FatalError( `Auth failed: ${err.message}` );
    }
    throw new ValidationError( `Request failed: ${err.message}` );
  }
}

import { evaluator, z, EvaluationBooleanResult, EvaluationNumberResult, EvaluationStringResult } from '@outputai/core';

// Boolean evaluator - pass/fail checks
export const evaluateCompleteness = evaluator( {
  name: 'evaluate_completeness',
  description: 'Check if content meets minimum length',
  inputSchema: z.object( { content: z.string(), minLength: z.number() } ),
  fn: async ( { content, minLength } ) => {
    return new EvaluationBooleanResult( {
      value: content.length >= minLength,
      confidence: 1.0,
      reasoning: `Content has ${content.length} chars (min: ${minLength})`
    } );
  }
} );

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
temperature: 0.7
maxOutputTokens: 4096
---

<system>
You are an expert content analyzer.

{% if context %}
Additional context: {{ context }}
{% endif %}
</system>

<user>
Analyze the following content:

<content>
{{ content }}
</content>

Provide {{ numberOfPoints | default: 3 }} key insights.
</user>

import { generateText, aiSdk } from '@outputai/llm';
import { z } from '@outputai/core';

// Structured output
const { output } = await generateText( {
  prompt: 'analyze@v1',
  variables: { content: 'Article text...', numberOfPoints: 5 },
  output: aiSdk.Output.object( {
    schema: z.object( { insights: z.array( z.string() ) } )
  } )
} );

// Text output
const { result } = await generateText( {
  prompt: 'summarize@v1',
  variables: { content: 'Article text...' }
} );