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agent-workflow-designer

agent-workflow-designer

github.com/alirezarezvaniAgent workflows28.9k662 installsUpdated 2d ago
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About agent-workflow-designer

agent-workflow-designer helps teams design multi-agent workflows before implementation. It provides pattern selection and scaffolding, with attention to handoffs, failure recovery, and context and cost limits.

Key features

  1. 01Multi-agent pattern selection
  2. 02Workflow skeleton generation
  3. 03Explicit handoff contracts
  4. 04Retry and error-recovery scaffolding
  5. 05Context and cost controls

Use cases

  1. 01Structure a multi-step content pipeline
  2. 02Route incident reports to specialist agents
  3. 03Add validation loops to agent workflows
  4. 04Prototype a workflow with bounded context

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill agent-workflow-designer

After installing, type/agent-workflow-designerin Claude Code to confirm it loaded.

Bundled filesreferences/workflow-patterns.mdscripts/workflow_scaffolder.py

Details

From the project README

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.

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