Agent Workflow Designer

---
name: "agent-workflow-designer"
description: "A single prompt is insufficient for task complexity"
executor: HYBRID
skill_id: engineering.cs-engineering.agent-workflow-designer
status: ADOPTED
security: {level: standard, pii: false, approval_required: false}
anchors:
  - engineering
  - agent
  - workflow
  - design
tier: 2
input_schema:
  - name: code_or_task
    type: string
    description: "Code snippet, script, or task description to process"
    required: true
  - name: context
    type: string
    description: "Additional context or background information"
    required: false
output_schema:
  - name: plan
    type: string
    description: "Strategic plan or design document"
  - name: next_steps
    type: array
    description: "List of recommended next steps"
---

# 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.
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.

---

## Why This Skill Exists

Implement — Agent Workflow Designer

<!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. -->

## What If Fails

If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.

<!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->