agent-workflow-designer

Design Community

Repo stars 15,974
Author repo claude-skills

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “Core Capabilities”, “When to Use”, “Quick Start”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name agent-workflow-designer directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / Core Capabilities / When to Use” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Design
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @alirezarezvani · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview agent-workflow-designer.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# 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

```bash
# 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.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Overview → Core Capabilities → When to Use → Quick Start → Pattern Map → Recommended Workflow
terms    -> Tier · Category · Domain · Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls. · 1. Select pattern based on dependency shape and risk profile. · 1. Start with the smallest pattern that can satisfy requirements.
files/cmd -> sequential · parallel · router · orchestrator · evaluator · references/workflow-patterns.md · scripts/workflowscaffolder.py
body sha256 -> e85adb0994b8