agent-workflow-designer

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• 21cfr11-compliance-guide.md
• AUDIT_REPORT.md
• AUTOMATION.md
• AUTOMATION_SETUP.md
• AUTOMATION_TEST.md
• CLAUDE.md
• CODE_OF_CONDUCT.md
• CONTRIBUTING.md
• GEMINI.md
• GIST_CONTENT.md
• GROWTH_STRATEGY.md
• HOW_TO_USE.md
• IMPLEMENTATION_SUMMARY.md
• INSTALLATION.md
• MARKETING-EXECUTION-PLAN.md
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• ORCHESTRATION.md
• PROGRESS.md
• PULL_REQUEST_TEMPLATE.md
• PYTHON_TOOLS_AUDIT.md
• REAL_WORLD_SCENARIO.md
• SECURITY.md
• SKILL-AUTHORING-STANDARD.md
• SKILL.md
• SKILLS_REFACTORING_PLAN.md
• SKILL_PIPELINE.md
• START_HERE.md
• STORE.md
• TEAM_STRUCTURE_GUIDE.md
• TEMPLATE.md
• WORKFLOW.md
• WORKFLOWS.md
• a11y-audit.md
• ab-test-setup.md
• ab_test_template.md
• accessibility.md
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• ad-copy-templates.md
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• agent-designer.md
• agent-protocol.md
• agent-skills-for-codex.md
• agent-template.md
• agent-templates.md
• agent-workflow-designer.md
• agent_architecture_patterns.md
• agenthub-board.md
• agenthub-eval.md
• agenthub-init.md
• agenthub-merge.md
• agenthub-run.md
• agenthub-spawn.md
• agenthub-status.md
• agenthub.md
• agentic_system_design.md
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• …and 140 more

SKILL.md (first lines)

# 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.

From SuperNovaRobot/openclaw-brain, Apache-2.0.

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