Agent Workflow Designer

alirezarezvani/claude-skills/engineering/skills/agent-workflow-designer

作者 alirezarezvani19392f7a0826无许可证27K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库5周前更新

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.

包含脚本AI & Agents
AI 生成的概览

设计多智能体 LLM 工作流,涵盖模式选择、交接契约、故障处理与成本控制。

功能
该技能指导设计生产级多智能体工作流,涵盖顺序、并行、路由、编排器和评估器等模式的选择。它通过附带的 Python 脚本生成工作流配置文件,并定义交接契约字段、重试与超时策略以及输出校验关卡。它还说明长流程中的上下文与成本控制要点,以及常见陷阱和最佳实践。
适用场景
当单个提示不足以应对任务复杂度、需要边界明确的专家智能体,或需要重构存在上下文膨胀、交接不可靠问题的 LLM 工作流时使用。它也适合希望在实现前先确定工作流结构的团队。
运行要求
需要 Python 3 运行时来执行附带的脚本 scripts/workflow_scaffolder.py。参考文件 references/workflow-patterns.md 供智能体阅读。未说明需要凭据或网络访问。

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 skeletonpython3 scripts/workflow_scaffolder.py sequential --name content-pipeline
# Generate an orchestrator workflow and save itpython3 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.

来源与署名

来源:alirezarezvani/claude-skills位于engineering/skills/agent-workflow-designer提交19392f7

许可证: 无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架