Agent skill · software engineering · alirezarezvani
agent-workflow-designer
Use when architecting a multi-step LLM or agent pipeline, choosing single vs multi-agent, or fixing context bloat and broken handoffs. Scaffolds the pattern, handoff contracts and failure handling with workflow_scaffolder.py.
Why this skill is useful
Adds executable scripts for generating multi-agent workflow configurations and error handling strategies that are not commonly found in public documentation.
What it needs
Requires python3 installed locally. About 1k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
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 --- 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/workflowscaffolder.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.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/agent-workflow-designer--85b62e