Workflow Schema Composer

Generate complete, valid workflow definitions from skill recommendations.

Purpose

Transform the output from skill-capability-matcher into a ready-to-use looplia workflow markdown file that follows the v0.6.2 schema.

Process

Step 1: Receive Inputs

From skill-capability-matcher output:

• Skill sequence with step IDs
• Mission descriptions for each step
• Data flow dependencies
• Original user requirements
• Explicit name (if --name flag was provided) - use this exact name for the workflow

Step 1.5: Parse User Preferences from Enriched Prompt (v0.6.4)

CRITICAL: User preferences from wizard answers MUST be incorporated into step missions.

When the enriched prompt contains "User clarifications: Q: ... A: ..." sections, extract each preference:

Example enriched prompt:

/build search hackernews for AI news. User clarifications: Q: Which social media platforms? A: twitter, linkedin. Q: How many articles? A: top5. Q: Focus areas? A: llm, adoption. Q: Output format? A: posts

Extract as structured preferences:

Preference Injection Rules:

1. COUNT preferences → Add to search/fetch step: "Find top 5 articles..."
2. FOCUS preferences → Add to search and analysis: "...focusing on LLM and adoption trends"
3. PLATFORM preferences → Add to output step: "...optimized for twitter and linkedin"
4. FORMAT preferences → Add to output step: "Create posts (not reports)..."

Step 2: Design Steps

For each recommended skill:

- id: {suggestedStepId}
  skill: {skill-name}
  mission: |
    {mission description from matcher}
  needs: [{dependencies}]
  input: {input path(s)}
  output: {output path}
  model: {optional model override}
  validate:
    required_fields: [{fields}]

Step 3: Resolve Dependencies

Use dataFlow from matcher:

• Steps with no dependencies: needs: is omitted
• Dependent steps: list all required step IDs in needs:
• Final step: add final: true

Step 4: Design Input/Output Paths

Use variable substitution:

• ${{ sandbox }}/inputs/content.md - Initial input (for workflows requiring input)
• ${{ sandbox }}/outputs/{step-id}.json - Step outputs
• ${{ steps.{id}.output }} - Reference previous step output

Input-Less Capable Skills (v0.6.3)

These skills can operate WITHOUT an input field - they fetch/generate data autonomously:

When a workflow's first step uses an input-less capable skill:

1. OMIT the input: field entirely from that step
2. The mission description drives the skill's behavior
3. Subsequent steps reference the output: ${{ steps.{id}.output }}

Example input-less first step:

- id: fetch-data
  skill: browser-research
  mission: |
    Search the web for recent technology trends.
    Extract titles, URLs, and key details.
  output: ${{ sandbox }}/outputs/data.json
  # NO input field - browser-research operates autonomously

Step 5: Suggest Validation

Based on skill output type:

• Analysis skills: required_fields: [contentId, headline, keyThemes]
• Idea skills: required_fields: [contentId, hooks, angles]
• Assembly skills: required_fields: [contentId, suggestedOutline]

Step 6: Compose Frontmatter (v0.7.0)

CRITICAL: If --name flag was provided, use that exact name. Do not derive or modify it.

---
name: {explicit-name OR derived-from-description}
version: 1.0.0
description: {user's original description, cleaned up}

# v0.7.0: Explicit skills declaration for selective plugin loading
skills:
  - {skill-name-1}
  - {skill-name-2}
  - ...

steps:
  - id: ...
---

Skills Declaration (v0.7.0): Extract unique skill names from all step recommendations and list them in the skills: field. This enables selective plugin loading at runtime - only required skills are loaded.

Naming rules:

1. If --name article-summary was provided → use article-summary exactly
2. If no --name → derive from description (e.g., "analyze videos" → "video-analyzer")
3. Always use kebab-case for names
4. Always include skills: field with all unique skills from steps

Step 7: Generate Markdown Body

Add usage documentation:

For workflows requiring input:

# {Workflow Name}

{Brief description}

## Usage

looplia run {workflow-name} --file <content.md>

For input-less workflows (v0.6.3):

# {Workflow Name}

{Brief description}

## Usage

looplia run {workflow-name}

No input required - this workflow uses autonomous skills to fetch data.

Steps section:

## Steps

1. **{step-id}**: {brief description}
2. ...

Output Format

Return a JSON object (v0.7.3: used by CLI for artifact persistence):

{
  "filename": "video-to-blog.md",
  "content": "---\nname: video-to-blog\nversion: 1.0.0\n...\n---\n\n# Video to Blog Workflow\n..."
}

Important: content MUST be the complete, ready-to-write markdown file including:

• Full YAML frontmatter (between --- delimiters)
• Markdown body (usage docs, steps section)

The CLI writes this content directly to {workspace}/workflows/{filename}.

Schema Reference

See SCHEMA.md in this skill directory for the complete v0.6.2 workflow schema.

Validation Rules (v0.6.3)

1. skill: is REQUIRED - Every step must have a skill
2. mission: is REQUIRED - Every step must have a mission
3. run: is FORBIDDEN - Never use the old agent syntax
4. Step IDs must be unique - No duplicates
5. Dependencies must exist - All needs: references must be valid
6. No circular dependencies - Validate topological ordering
7. Respect explicit --name - If provided, use that exact name for filename and name: field
8. Input-less steps (v0.6.3) - Steps using search skill may OMIT input: field entirely

Example Output

---
name: video-to-blog
version: 1.0.0
description: Analyze YouTube videos and create blog outlines

# v0.7.0: Explicit skills declaration for selective plugin loading
skills:
  - media-reviewer
  - idea-synthesis
  - writing-kit-assembler

steps:
  - id: analyze-content
    skill: media-reviewer
    mission: |
      Deep analysis of video transcript. Extract key themes,
      important quotes with timestamps, and narrative structure.
    input: ${{ sandbox }}/inputs/content.md
    output: ${{ sandbox }}/outputs/analysis.json
    model: haiku
    validate:
      required_fields: [contentId, headline, keyThemes, importantQuotes]

  - id: generate-ideas
    skill: idea-synthesis
    mission: |
      Generate hooks, angles, and questions from the analysis.
      Read user profile for personalization context.
    needs: [analyze-content]
    input: ${{ steps.analyze-content.output }}
    output: ${{ sandbox }}/outputs/ideas.json
    validate:
      required_fields: [contentId, hooks, angles, questions]

  - id: build-outline
    skill: writing-kit-assembler
    mission: |
      Create structured blog outline with sections, key points,
      and supporting quotes from analysis and ideas.
    needs: [analyze-content, generate-ideas]
    input:
      - ${{ steps.analyze-content.output }}
      - ${{ steps.generate-ideas.output }}
    output: ${{ sandbox }}/outputs/outline.json
    final: true
    validate:
      required_fields: [contentId, suggestedOutline]
---

# Video to Blog Workflow

Transform video content into structured blog outlines.

## Usage

looplia run video-to-blog --file <transcript.md>

## Steps

1. **analyze-content**: Deep analysis using media-reviewer skill
2. **generate-ideas**: Idea synthesis with user personalization
3. **build-outline**: Assemble outline using writing-kit-assembler skill

Important Rules

1. Always use skill: syntax - Never use run: agents/X
2. Always include mission - Detailed task description
3. Use valid YAML - Proper indentation and quoting
4. Include validation - Add validate: with appropriate fields
5. Mark final step - Last step gets final: true
6. Respect --name flag - If --name X is provided, the workflow MUST be named X and saved as X.md
7. Detect input-less workflows - If first step uses search skill, OMIT input field
8. Incorporate user preferences (v0.6.4) - Extract preferences from "User clarifications" and inject into step missions. Each preference MUST appear in at least one mission.
9. Include skills declaration (v0.7.0) - Always add skills: field listing all unique skill names from steps. This enables selective plugin loading at runtime.

Example: Input-Less Workflow (v0.6.3)

When the workflow fetches data autonomously (no user input needed):

---
name: daily-news-digest
version: 1.0.0
description: Fetch trending news and compile a digest report

# v0.7.0: Explicit skills declaration
skills:
  - browser-research
  - content-documenter

steps:
  - id: fetch-news
    skill: browser-research
    mission: |
      Search the web for today's trending technology news.
      Extract title, URL, source, and brief summary for each story.
    output: ${{ sandbox }}/outputs/news.json
    # NO input field - browser-research operates autonomously
    validate:
      required_fields: [query, mode, results]

  - id: compile-digest
    skill: content-documenter
    mission: |
      Compile the news into a formatted digest with categories and insights.
    needs: [fetch-news]
    input: ${{ steps.fetch-news.output }}
    output: ${{ sandbox }}/outputs/digest.json
    final: true
    validate:
      required_fields: [reportTitle, sections, summary]
---

# Daily News Digest

Fetches and compiles trending news into a digest.

## Usage

looplia run daily-news-digest

No input required - this workflow fetches data autonomously.

Example: User Preference Injection (v0.6.4)

Given enriched prompt:

/build search hackernews for AI news. User clarifications: Q: Which platforms? A: twitter, linkedin. Q: How many? A: top5. Q: Focus areas? A: llm, adoption. Q: Output format? A: posts

Extracted preferences:

• PLATFORMS: twitter, linkedin
• COUNT: 5
• FOCUS: llm, adoption
• FORMAT: posts

BAD workflow (ignores preferences):

- id: fetch-news
  skill: browser-research
  mission: |
    Search HackerNews for AI news articles.
    Extract titles and summaries.

- id: compile-output
  skill: content-documenter
  mission: |
    Compile the news into a report.

GOOD workflow (incorporates preferences):

- id: fetch-news
  skill: browser-research
  mission: |
    Search HackerNews for the top 5 AI news articles
    focusing on LLM developments and adoption trends.
    Extract titles, URLs, and key summaries.

- id: compile-output
  skill: content-documenter
  mission: |
    Create engaging social media posts optimized for
    twitter and linkedin. Focus on LLM and adoption angles.
    Output as posts, not a formal report.