plungeai-workflows

name: "Simple Single Task"
version: "2.0.0"
description: "Single search task — the smallest valid CNL workflow."

tasks:
  - type: "task"
    id: "single_task"
    agent: brave-agent
    prompt: "simple search test"

input: "electric vehicles 2026"

name: "Parallel Research With Synthesis"
version: "2.0.0"
description: "Three parallel searches from different engines, then LLM synthesis. The most common production pattern."

tasks:
  - type: "parallel"
    id: "research_phase"
    subtasks:
      - type: "task"
        id: "brave_search"
        agent: brave-agent
        prompt: "{input} — latest news, trends, and analysis"
      - type: "task"
        id: "tavily_research"
        agent: tavily-agent
        prompt: "{input} — cited sources and detailed reporting from the last 12 months"
      - type: "task"
        id: "exa_deep"
        agent: exa-agent
        operationType: "search"
        prompt: "{input} — academic papers, institutional research, and expert analyses"

  - type: "task"
    id: "synthesis"
    agent: llm-agent
    prompt: |
      Synthesize the parallel research into a comprehensive report on {input}.
      Sources:
      Brave: {data:brave_search}
      Tavily: {data:tavily_research}
      Exa: {data:exa_deep}

      Identify common themes, contradictions, and the three strongest insights.

input: "quantum computing commercial applications"

name: "3-Level Hierarchical Nesting"
version: "2.0.0"
description: "parallel → sequential → parallel nesting. Demonstrates unlimited nesting depth with data flowing automatically."

tasks:
  - type: "parallel"
    id: "level_1_parallel"
    subtasks:
      - type: "task"
        id: "simple_task"
        agent: brave-agent
        prompt: "{input} — quick overview"
      - type: "sequential"
        id: "level_2_sequential"
        subtasks:
          - type: "task"
            id: "deep_search"
            agent: tavily-agent
            prompt: "{input} — deep research with citations"
          - type: "parallel"
            id: "level_3_parallel"
            subtasks:
              - type: "task"
                id: "business_angle"
                agent: llm-agent
                prompt: "From the research, extract business implications for {input}"
              - type: "task"
                id: "technical_angle"
                agent: llm-agent
                prompt: "From the research, extract technical implications for {input}"

  - type: "task"
    id: "final_report"
    agent: llm-agent
    prompt: "Combine all prior branches into a comprehensive report on {input}."

input: "autonomous vehicle regulation"

workflow:
  name: "Consensus Validation With 3 Validators"
  version: "2.0.0"
  description: "Three LLM validators run in consensus on a single piece of content. Use when correctness must be triple-checked before publishing."
  tasks:
    - type: validate
      id: fact_check
      aggregation: consensus
      validators:
        - agent: llm-agent
          validation_rule: "Check if the numerical claims are plausible and internally consistent"
        - agent: llm-agent
          validation_rule: "Check for logical consistency — identify any contradictions"
        - agent: llm-agent
          validation_rule: "Check completeness — are all key financial metrics covered"
      success_criteria: "Content must be factually plausible, logically consistent, and comprehensive"

input: "Tesla reported Q4 2025 revenue of $25.7B. The automotive segment contributed $21.3B while energy generation and storage added $2.8B. Gross margins improved to 19.8% from 17.6% year-over-year. The company delivered 495,000 vehicles in Q4, a 12% increase from Q3."

name: "Per-Item Batch Pipeline"
version: "1.0.0"
description: "Run the same two-step pipeline for every item in a list, then roll up."

tasks:
  - type: "batch"
    id: "per_company"
    items:
      - "Cloudflare"
      - "Vercel"
      - "Fly.io"
    tasks:
      - type: "task"
        id: "research"
        agent: brave-agent
        prompt: "{item} — company overview, funding, latest news"
      - type: "task"
        id: "brief"
        agent: llm-agent
        prompt: "From the research above, write a 5-bullet analyst brief on {item}"

  - type: "task"
    id: "rollup"
    agent: llm-agent
    prompt: "Combine the briefs into one comparison table: {data:per_company}"

input: "edge platforms"

name: "Bounded Research Mission"
version: "1.0.0"
description: "A goal-driven harness run with an inline mission, effort preset, and turn cap."

tasks:
  - type: "harness"
    id: "market_scan"
    goal: "Map the top 5 vendors for {input}, with pricing and one differentiator each."
    mission:
      mission: "You are a market analyst. Research thoroughly, cite sources, stay on topic."
      effort: "standard"
      max_turns: 12
      success_criteria:
        - "5 vendors named with pricing"
        - "every claim has a source URL"

  - type: "output"
    id: "deliver"
    format: "markdown"

input: "workflow orchestration platforms"