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Sub agent orchestrator

Skill OneWave-AI/claude-skills/sub-agent-orchestrator

172 production-ready Claude Code skills for sales, marketing, design, engineering, and AI agent architecture. Built and maintained by OneWave AI.

Install
npx -y skills add OneWave-AI/claude-skills --skill sub-agent-orchestrator

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What its author says it does

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Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.

SKILL.md

3.2 KB, as published. Nobody here has run it

Sub-Agent Orchestrator

Design and execute multi-agent pipelines where each step is a different agent that depends on the previous one. Define roles, dependencies, and handoffs in YAML, then run sequential, parallel, conditional, loop, and map-reduce workflows with retry, timeout, and validation.

Unlike Agent Army (homogeneous parallel code changes) and Agent Swarm (homogeneous parallel data processing), this orchestrator coordinates heterogeneous pipelines where the output of A feeds the input of B.

Contents

  • references/patterns.md -- The six workflow patterns and the comparison to Agent Army/Swarm.
  • references/workflow-schema.md -- Full YAML workflow definition language.
  • references/examples.md -- Complete worked workflows (research-to-proposal, lead scoring).
  • references/execution-engine.md -- Per-step execution model, retry, timeout, validation, edge cases.
  • references/templates.md -- Reusable workflow scaffolds.
  • references/visual-and-reporting.md -- Text diagrams and the execution report template.

Workflow

  1. Determine the mode from the request:

    • Run a workflow file: read the YAML at the given path.
    • Define and run inline: convert the natural-language description into a workflow YAML (see references/workflow-schema.md), then show it for approval.
    • Dry run: parse, validate, resolve inputs, and show the execution plan without deploying agents.
    • Inspect: parse the YAML and produce a human-readable description plus a text diagram (see references/visual-and-reporting.md).

  2. Parse and validate the workflow: confirm required fields, that agent IDs resolve, and that there are no circular dependencies. Report syntax or reference errors with the offending line. See references/execution-engine.md.

  3. Resolve inputs: collect every required input from the user before starting; apply defaults for optional inputs.

  4. Build the execution DAG and run each step in topological order using the matching execution model (sequential, parallel, conditional, loop, map). See references/execution-engine.md.

  5. After each agent completes, validate its output against the agent's schema and rules. On failure, apply the retry/timeout/failure policy (skip, abort, or fallback).

  6. On completion, present results using the execution report template in references/visual-and-reporting.md. For partial or failed runs, report what completed, what failed, and any collected partial output.

Choosing a pattern

Match the task shape to a pattern, then scaffold from references/templates.md:

  • Strict ordering of distinct steps: sequential chain.
  • One input scored or analyzed from multiple angles: parallel fan-out/fan-in.
  • Input routed by classification: conditional routing.
  • Output must meet a quality bar: loop with a validator.
  • Large input chunked and recombined: map-reduce.
  • A step needs a backup approach on failure: pipeline with fallback.

See references/patterns.md for diagrams and examples of each.

Keep looking

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