AgentSkillsCN

agent-workflow-designer

智能体工作流设计器

SKILL.md
--- frontmatter
name: agent-workflow-designer
description: Agent Workflow Designer
risk: offensive
source: community
license: MIT

Agent Workflow Designer

⚠️ AUTHORIZED USE ONLY This skill is for educational purposes or authorized security assessments only. You must have explicit, written permission from the system owner before using this tool. Misuse of this tool is illegal and strictly prohibited.

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.