Multi-agent skills, designed as a graph.

What the agent does with it

1. 01Your request matches the skill's description, and the agent loads the plan: a few hundred tokens, not every prompt.
2. 02It starts the subagents of stage 1, handing each one exactly the input listed for it.
3. 03Before the next stage, it checks each output against its contract and re-runs a step once if it does not match.
4. 04Steps in the same stage start together. A fresh-context verifier gets only the raw outputs it audits.
5. 05Command checks and the verifier's gate decide what passes. On a failure the agent re-runs only the steps that failed, with the reason, or stops and reports, as the plan says.
6. 06The last step's output is returned as the answer.

The format

---
name: parallel-system-audit
description: Splits a goal into 4 parallel workers, verifies with fresh context, and synthesizes.
spec: skill-graph/1
topology:
  nodes:
    - id: worker_research
      type: worker
      model: haiku
      tools: [Read, Grep, Glob, WebSearch]
    - id: verifier
      type: verifier
      isolated_context: true
      model: opus
      tools: [Read, Grep, Glob]
      gate: Every kept claim cites concrete evidence.
      on_fail: retry
  edges:
    - from: worker_research
      to: verifier
      contract: findings_array
contracts:
  findings_array:
    format: markdown
    description: Numbered findings, each with evidence and a confidence.
---

## @node: worker_research
Focus ONLY on deep research for: {{splitter.worker_research}}

Prompts reference upstream work with {{goal}}, {{node.field}} (one key of a JSON output), {{node.output}} and group aliases such as {{workers.output}}. The studio checks that every reference points upstream, every contract is defined and the graph has no cycles, and shows exactly what each step's subagent will receive.