Design production-grade multi-agent workflows with clear

Agent workflow designer

by alirezarezvani · MIT license · ★ 26,349 on GitHub · updated Aug 30

Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing…

---
name: "agent-workflow-designer"
description: "Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs."
---

# 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

Overview

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

Manual install (npx, ZIP)

Claude Codenpx skills add alirezarezvani/claude-skills --skill agent-workflow-designer -g -a claude-code

Claude Code (manual)

mkdir -p ~/.claude/skills && curl -sL https://codeload.github.com/alirezarezvani/claude-skills/tar.gz/19392f7a0826 \
  | tar -xz -C ~/.claude/skills --strip-components=3 claude-skills-19392f7a0826/engineering/skills/agent-workflow-designer

Windows (PowerShell)

New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null; Invoke-WebRequest "https://m.agentalley.io/zip/ba47a5ed9270d7cee485/agent-workflow-designer.zip" -OutFile "$env:TEMP\agent-workflow-designer.zip"; Expand-Archive "$env:TEMP\agent-workflow-designer.zip" -DestinationPath "$HOME\.claude\skills" -Force

Claude.ai / desktopDownload the ZIP and upload it under Settings → Capabilities → Skills.

Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

Files included

SKILL.md2.7 KB
references/1 file · 1.5 KB
scripts/1 file · 3.3 KB

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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

Select pattern based on dependency shape and risk profile.
Scaffold config via scripts/workflow_scaffolder.py.
Define handoff contract fields for every edge.
Add retry/timeouts and output validation gates.
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

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

1	---
2	name: "agent-workflow-designer"
3	description: "Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs."
4	---
5
6	# Agent Workflow Designer
7
8	Tier: POWERFUL
9	Category: Engineering
10	Domain: Multi-Agent Systems / AI Orchestration
11
12	---
13
14	## Overview
15
16	Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.
17
18	## Core Capabilities
19
20	- Workflow pattern selection for multi-step agent systems
21	- Skeleton config generation for fast workflow bootstrapping
22	- Context and cost discipline across long-running flows
23	- Error recovery and retry strategy scaffolding
24	- Documentation pointers for operational pattern tradeoffs
25
26	---
27
28	## When to Use
29
30	- A single prompt is insufficient for task complexity
31	- You need specialist agents with explicit boundaries
32	- You want deterministic workflow structure before implementation
33	- You need validation loops for quality or safety gates
34
35	---
36
37	## Quick Start
38
39	```bash
40	# Generate a sequential workflow skeleton
41	python3 scripts/workflow_scaffolder.py sequential --name content-pipeline
42
43	# Generate an orchestrator workflow and save it
44	python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
45	```
46
47	---
48
49	## Pattern Map
50
51	- `sequential`: strict step-by-step dependency chain
52	- `parallel`: fan-out/fan-in for independent subtasks
53	- `router`: dispatch by intent/type with fallback
54	- `orchestrator`: planner coordinates specialists with dependencies
55	- `evaluator`: generator + quality gate loop
56
57	Detailed templates: `references/workflow-patterns.md`
58
59	---
60
61	## Recommended Workflow
62
63	1. Select pattern based on dependency shape and risk profile.
64	2. Scaffold config via `scripts/workflow_scaffolder.py`.
65	3. Define handoff contract fields for every edge.
66	4. Add retry/timeouts and output validation gates.
67	5. Dry-run with small context budgets before scaling.
68
69	---
70
71	## Common Pitfalls
72
73	- Over-orchestrating tasks solvable by one well-structured prompt
74	- Missing timeout/retry policies for external-model calls
75	- Passing full upstream context instead of targeted artifacts
76	- Ignoring per-step cost accumulation
77
78	## Best Practices
79
80	1. Start with the smallest pattern that can satisfy requirements.
81	2. Keep handoff payloads explicit and bounded.
82	3. Validate intermediate outputs before fan-in synthesis.
83	4. Enforce budget and timeout limits in every step.
84