Agent Workflow Designer

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

作者 alirezarezvani19392f7a0826無授權條款27K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫5 週前更新

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.

包含腳本AI & Agents
AI 產生的概覽

設計多代理 LLM 工作流程,涵蓋模式選擇、交接契約、失敗處理與成本控制。

功能
此技能指導設計生產級多代理工作流程,涵蓋循序、平行、路由、協調器與評估器等模式的選擇。它透過隨附的 Python 指令碼產生工作流程設定檔,並定義交接契約欄位、重試與逾時策略以及輸出驗證關卡。它也說明長時間流程中的上下文與成本控管要點,以及常見陷阱與最佳實務。
適用情境
當單一提示不足以應付任務複雜度、需要界線明確的專家代理,或需要重構有上下文膨脹、交接不可靠問題的 LLM 工作流程時使用。它也適合希望在實作前先確定工作流程結構的團隊。
執行需求
需要 Python 3 執行環境來執行隨附的指令碼 scripts/workflow_scaffolder.py。參考檔案 references/workflow-patterns.md 供代理閱讀。未說明需要憑證或網路存取。

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.

來源與署名

來源:alirezarezvani/claude-skills位於engineering/skills/agent-workflow-designer提交19392f7

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