Multi-Agent Workflow Designer is a ai-agents claude skill built by Alireza Rezvani. Best for: Engineers and AI architects use this to structure complex multi-step tasks requiring specialist agents with deterministic control flow and safety gates..

What it does
Design production-grade multi-agent workflows with pattern selection, handoff contracts, and failure handling.
Category
ai-agents
Created by
Alireza Rezvani
Last updated
Claude Skillai-agents GitHub-backed CuratedadvancedClaude Code

Multi-Agent Workflow Designer

Design production-grade multi-agent workflows with pattern selection, handoff contracts, and failure handling.

Skill instructions


name: "agent-workflow-designer" description: "Agent Workflow Designer"

Agent Workflow Designer

Tier: POWERFUL
Category: Engineering
Domain: Multi-Agent Systems / AI Orchestration


Overview

Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.

Core Capabilities

  • Workflow pattern selection for multi-step agent systems
  • Skeleton config generation for fast workflow bootstrapping
  • Context and cost discipline across long-running flows
  • Error recovery and retry strategy scaffolding
  • Documentation pointers for operational pattern tradeoffs

When to Use

  • A single prompt is insufficient for task complexity
  • You need specialist agents with explicit boundaries
  • You want deterministic workflow structure before implementation
  • You need validation loops for quality or safety gates

Quick Start

# Generate a sequential workflow skeleton
python3 scripts/workflow_scaffolder.py sequential --name content-pipeline

# Generate an orchestrator workflow and save it
python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json

Pattern Map

  • sequential: strict step-by-step dependency chain
  • parallel: fan-out/fan-in for independent subtasks
  • router: dispatch by intent/type with fallback
  • orchestrator: planner coordinates specialists with dependencies
  • evaluator: generator + quality gate loop

Detailed templates: references/workflow-patterns.md


Recommended Workflow

  1. Select pattern based on dependency shape and risk profile.
  2. Scaffold config via scripts/workflow_scaffolder.py.
  3. Define handoff contract fields for every edge.
  4. Add retry/timeouts and output validation gates.
  5. Dry-run with small context budgets before scaling.

Common Pitfalls

  • Over-orchestrating tasks solvable by one well-structured prompt
  • Missing timeout/retry policies for external-model calls
  • Passing full upstream context instead of targeted artifacts
  • Ignoring per-step cost accumulation

Best Practices

  1. Start with the smallest pattern that can satisfy requirements.
  2. Keep handoff payloads explicit and bounded.
  3. Validate intermediate outputs before fan-in synthesis.
  4. Enforce budget and timeout limits in every step.

Use this skill

Most skills are portable instruction packages. Claude Code supports SKILL.md directly. Other agents can use adapted files like AGENTS.md, .cursorrules, and GEMINI.md.

Claude Code

Save SKILL.md into your Claude Skills folder, then restart Claude Code.

mkdir -p ~/.claude/skills/multi-agent-workflow-designer && curl -L "https://raw.githubusercontent.com/alirezarezvani/claude-skills/HEAD/engineering/agent-workflow-designer/SKILL.md" -o ~/.claude/skills/multi-agent-workflow-designer/SKILL.md

Installs to ~/.claude/skills/multi-agent-workflow-designer/SKILL.md.

Use cases

Engineers and AI architects use this to structure complex multi-step tasks requiring specialist agents with deterministic control flow and safety gates.

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Stats

Installs0
GitHub Stars11.6k
Forks1507
LicenseMIT
UpdatedMar 27, 2026