Content Generation

Basic Pattern

ensemble: generate-content

agents:
  # 1. Research
  - name: research
    operation: rag
    config:
      action: search
      query: ${input.topic}

  # 2. Generate draft
  - name: draft
    operation: think
    config:
      prompt: |
        Write about: ${input.topic}
        Context: ${research.output}

  # 3. Refine
  - name: refine
    operation: think
    config:
      prompt: |
        Improve this draft:
        ${draft.output}

  # 4. Approve
  - name: approve
    operation: hitl
    inputs:
      data: ${refine.output}
      prompt: "Review content"

  # 5. Publish
  - name: publish
    condition: ${approve.output.approved}
    operation: http

Blog Post Generation

ensemble: generate-blog-post

inputs:
  topic:
    type: string
    required: true
  keywords:
    type: array
  target_length:
    type: number
    default: 1000

agents:
  # Research phase
  - name: research-topic
    operation: rag
    config:
      action: search
      query: ${input.topic}
      topK: 10

  - name: research-keywords
    operation: scrape
    inputs:
      url: https://www.google.com/search?q=${input.keywords.join('+')}

  # Generate outline
  - name: create-outline
    operation: think
    config:
      provider: openai
      model: gpt-4o
      prompt: |
        Create a blog post outline about: ${input.topic}

        Research: ${research-topic.output.results.map(r => r.text).join('\n')}
        Target length: ${input.target_length} words
        Keywords to include: ${input.keywords.join(', ')}

        Return JSON: {
          "title": string,
          "intro": string,
          "sections": [{"heading": string, "points": [string]}],
          "conclusion": string
        }

  # Write each section in parallel
  - name: write-intro
    operation: think
    config:
      prompt: |
        Write introduction (150 words):
        ${JSON.parse(create-outline.output).intro}

  - name: write-section-1
    operation: think
    config:
      prompt: |
        Write section (300 words):
        Heading: ${JSON.parse(create-outline.output).sections[0].heading}
        Points: ${JSON.parse(create-outline.output).sections[0].points}

  - name: write-section-2
    operation: think
    config:
      prompt: |
        Write section (300 words):
        Heading: ${JSON.parse(create-outline.output).sections[1].heading}
        Points: ${JSON.parse(create-outline.output).sections[1].points}

  - name: write-section-3
    operation: think
    config:
      prompt: |
        Write section (300 words):
        Heading: ${JSON.parse(create-outline.output).sections[2].heading}
        Points: ${JSON.parse(create-outline.output).sections[2].points}

  - name: write-conclusion
    operation: think
    config:
      prompt: |
        Write conclusion (150 words):
        ${JSON.parse(create-outline.output).conclusion}

  # Assemble post
  - name: assemble
    operation: code
    config:
      script: scripts/assemble-blog-post
    input:
      outline: ${create-outline.output}
      intro: ${write-intro.output}
      section1: ${write-section-1.output}
      section2: ${write-section-2.output}
      section3: ${write-section-3.output}
      conclusion: ${write-conclusion.output}
      topic: ${input.topic}
      keywords: ${input.keywords}

  # SEO optimization
  - name: optimize-seo
    operation: think
    config:
      prompt: |
        Optimize SEO for this post:
        Title: ${assemble.output.title}
        Content: ${assemble.output.content}
        Keywords: ${input.keywords}

        Return JSON: {
          "meta_description": string,
          "seo_title": string,
          "slug": string,
          "tags": [string]
        }

  # Generate image prompt
  - name: image-prompt
    operation: think
    config:
      prompt: |
        Create DALL-E prompt for hero image:
        Topic: ${input.topic}
        Title: ${assemble.output.title}

  # Review and approve
  - name: approve
    operation: hitl
    inputs:
      data:
        title: ${assemble.output.title}
        content: ${assemble.output.content}
        seo: ${optimize-seo.output}
        word_count: ${assemble.output.metadata.word_count}
      prompt: |
        Review blog post:
        - Content quality
        - SEO optimization
        - Brand voice
      approvers: [[email protected]]

  # Publish if approved
  - name: publish
    condition: ${approve.output.approved}
    operation: http
    config:
      url: https://cms.example.com/api/posts
      method: POST
      body:
        title: ${JSON.parse(optimize-seo.output).seo_title}
        slug: ${JSON.parse(optimize-seo.output).slug}
        content: ${assemble.output.content}
        meta_description: ${JSON.parse(optimize-seo.output).meta_description}
        tags: ${JSON.parse(optimize-seo.output).tags}
        status: published

output:
  post_url: ${publish.output.url}
  analytics: ${assemble.output.metadata}

// scripts/assemble-blog-post.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function assembleBlogPost(context: AgentExecutionContext) {
  const { outline, intro, section1, section2, section3, conclusion, topic, keywords } = context.input
  const parsedOutline = JSON.parse(outline)

  const content = [
    intro,
    '## ' + parsedOutline.sections[0].heading,
    section1,
    '## ' + parsedOutline.sections[1].heading,
    section2,
    '## ' + parsedOutline.sections[2].heading,
    section3,
    '## Conclusion',
    conclusion
  ].join('\n\n')

  const wordCount = [intro, section1, section2, section3, conclusion]
    .map(text => text.split(' ').length)
    .reduce((sum, count) => sum + count, 0)

  return {
    title: parsedOutline.title,
    content,
    metadata: {
      topic,
      keywords,
      word_count: wordCount
    }
  }
}

Marketing Copy

ensemble: generate-marketing-copy

agents:
  # Generate variants (A/B test)
  - name: variant-a
    operation: think
    config:
      model: gpt-4o
      temperature: 0.9
      prompt: |
        Write marketing copy for: ${input.product}
        Tone: Professional
        Length: 100 words
        Focus: Features and benefits

  - name: variant-b
    operation: think
    config:
      model: gpt-4o
      temperature: 0.9
      prompt: |
        Write marketing copy for: ${input.product}
        Tone: Casual and friendly
        Length: 100 words
        Focus: Emotional appeal

  - name: variant-c
    operation: think
    config:
      model: gpt-4o
      temperature: 0.9
      prompt: |
        Write marketing copy for: ${input.product}
        Tone: Urgent and direct
        Length: 100 words
        Focus: Call-to-action

  # Generate CTAs for each
  - name: cta-a
    operation: think
    config:
      prompt: Create compelling CTA for: ${variant-a.output}

  - name: cta-b
    operation: think
    config:
      prompt: Create compelling CTA for: ${variant-b.output}

  - name: cta-c
    operation: think
    config:
      prompt: Create compelling CTA for: ${variant-c.output}

  # Package variants
  - name: package
    operation: code
    config:
      script: scripts/package-marketing-variants
    input:
      variantA: ${variant-a.output}
      variantB: ${variant-b.output}
      variantC: ${variant-c.output}
      ctaA: ${cta-a.output}
      ctaB: ${cta-b.output}
      ctaC: ${cta-c.output}

// scripts/package-marketing-variants.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function packageMarketingVariants(context: AgentExecutionContext) {
  const { variantA, variantB, variantC, ctaA, ctaB, ctaC } = context.input

  return {
    variants: [
      {
        id: 'a',
        copy: variantA,
        cta: ctaA,
        tone: 'professional'
      },
      {
        id: 'b',
        copy: variantB,
        cta: ctaB,
        tone: 'casual'
      },
      {
        id: 'c',
        copy: variantC,
        cta: ctaC,
        tone: 'urgent'
      }
    ]
  }
}

Product Documentation

ensemble: generate-docs

agents:
  # Extract code info
  - name: analyze-code
    operation: code
    config:
      script: scripts/analyze-codebase
    input:
      codebasePath: ${input.codebasePath}

  # Generate API docs
  - name: api-docs
    operation: think
    config:
      prompt: |
        Generate API documentation:
        Functions: ${analyze-code.output.functions}
        Classes: ${analyze-code.output.classes}

        Format: Markdown with examples

  # Generate tutorial
  - name: tutorial
    operation: think
    config:
      prompt: |
        Write getting started tutorial:
        API: ${api-docs.output}

        Include:
        - Installation
        - Quick start
        - Common patterns

  # Generate examples
  - name: examples
    operation: think
    config:
      prompt: |
        Create 5 code examples:
        API: ${api-docs.output}

        Show real-world use cases

  # Combine into docs
  - name: assemble-docs
    operation: code
    config:
      script: scripts/assemble-documentation
    input:
      apiReference: ${api-docs.output}
      gettingStarted: ${tutorial.output}
      examples: ${examples.output}

// scripts/analyze-codebase.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function analyzeCodebase(context: AgentExecutionContext) {
  const { codebasePath } = context.input

  // Analyze codebase structure
  return {
    functions: ['init', 'process', 'cleanup'],
    classes: ['Manager', 'Worker'],
    config: {}
  }
}

// scripts/assemble-documentation.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function assembleDocumentation(context: AgentExecutionContext) {
  const { apiReference, gettingStarted, examples } = context.input

  return {
    api_reference: apiReference,
    getting_started: gettingStarted,
    examples,
    generated_at: new Date().toISOString()
  }
}

ensemble: social-media-content

agents:
  # Generate posts for different platforms
  - name: twitter
    operation: think
    config:
      prompt: |
        Create Twitter thread (5 tweets) about: ${input.topic}
        - Hook in first tweet
        - Value in middle tweets
        - CTA in last tweet
        Max 280 chars per tweet

  - name: linkedin
    operation: think
    config:
      prompt: |
        Create LinkedIn post about: ${input.topic}
        - Professional tone
        - 1300 characters
        - Include relevant hashtags

  - name: facebook
    operation: think
    config:
      prompt: |
        Create Facebook post about: ${input.topic}
        - Casual tone
        - 400 characters
        - Engagement-focused

  # Generate images
  - name: image-prompts
    operation: think
    config:
      prompt: |
        Create 3 DALL-E prompts for: ${input.topic}
        Return JSON array

  # Schedule posts
  - name: schedule
    operation: code
    config:
      script: scripts/schedule-social-posts
    input:
      twitter: ${twitter.output}
      linkedin: ${linkedin.output}
      facebook: ${facebook.output}

Content Repurposing

ensemble: repurpose-content

agents:
  # Start with long-form content
  - name: input
    operation: code
    config:
      script: scripts/prepare-blog-content
    input:
      blogPost: ${input.blog_post}

  # Generate derivatives in parallel
  - name: twitter-thread
    operation: think
    config:
      prompt: |
        Convert to Twitter thread:
        ${input.blog_post}

  - name: linkedin-post
    operation: think
    config:
      prompt: |
        Convert to LinkedIn post:
        ${input.blog_post}

  - name: email-newsletter
    operation: think
    config:
      prompt: |
        Convert to email newsletter:
        ${input.blog_post}

  - name: video-script
    operation: think
    config:
      prompt: |
        Convert to video script (3 minutes):
        ${input.blog_post}

  - name: infographic-outline
    operation: think
    config:
      prompt: |
        Create infographic outline:
        ${input.blog_post}

  # Package all formats
  - name: package
    operation: code
    config:
      script: scripts/package-repurposed-content
    input:
      originalPost: ${input.blog_post}
      twitter: ${twitter-thread.output}
      linkedin: ${linkedin-post.output}
      email: ${email-newsletter.output}
      video: ${video-script.output}
      infographic: ${infographic-outline.output}

// scripts/schedule-social-posts.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function scheduleSocialPosts(context: AgentExecutionContext) {
  const { twitter, linkedin, facebook } = context.input

  return {
    twitter: {
      content: JSON.parse(twitter),
      schedule: new Date(Date.now() + 3600000)  // 1 hour
    },
    linkedin: {
      content: linkedin,
      schedule: new Date(Date.now() + 7200000)  // 2 hours
    },
    facebook: {
      content: facebook,
      schedule: new Date(Date.now() + 10800000)  // 3 hours
    }
  }
}

// scripts/prepare-blog-content.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function prepareBlogContent(context: AgentExecutionContext) {
  const { blogPost } = context.input
  return { content: blogPost }
}

// scripts/package-repurposed-content.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function packageRepurposedContent(context: AgentExecutionContext) {
  const { originalPost, twitter, linkedin, email, video, infographic } = context.input

  return {
    original: originalPost,
    formats: {
      twitter,
      linkedin,
      email,
      video,
      infographic
    }
  }
}

Best Practices

1. Parallel Generation

# Generate variants simultaneously
agents:
  - name: variant-1
    operation: think
  - name: variant-2
    operation: think
  - name: variant-3
    operation: think

2. Higher Temperature for Creativity

config:
  temperature: 0.9  # More creative
  temperature: 0.3  # More focused

3. Always Review

- name: approve
  operation: hitl
  inputs:
    prompt: "Review content for quality and brand voice"

4. SEO Optimization

- name: optimize-seo
  operation: think
  config:
    prompt: |
      Optimize for SEO:
      - Keywords: ${input.keywords}
      - Meta description
      - Title tag
      - Headers

5. Version Content

- name: store
  operation: data
  config:
    backend: d1
    binding: DB
    operation: execute
    sql: INSERT INTO content_versions (content, version, timestamp) VALUES (?, ?, ?)

Next Steps

think Operation

AI generation

HITL Approval

Review workflow

RAG Pipeline

Research phase

A/B Testing

Test variants

Conductor

Getting Started

Core Concepts

Building

Components

Operations Reference

Plugins

Starter Kit

Playbooks

Reference

Basic Pattern

Blog Post Generation

Marketing Copy

Product Documentation

Content Repurposing

Best Practices

Next Steps

think Operation

HITL Approval

RAG Pipeline

A/B Testing

Conductor

Getting Started

Core Concepts

Building

Components

Operations Reference

Plugins

Starter Kit

Playbooks

Reference

​Basic Pattern

​Blog Post Generation

​Marketing Copy

​Product Documentation

​Social Media Content

​Content Repurposing

​Best Practices

​Next Steps

think Operation

HITL Approval

RAG Pipeline

A/B Testing

Basic Pattern

Blog Post Generation

Marketing Copy

Product Documentation

Social Media Content

Content Repurposing

Best Practices

Next Steps