Best for
- A single prompt is insufficient for task complexity
- You need specialist agents with explicit boundaries
- You want deterministic workflow structure before implementation
alirezarezvani/claude-skills/engineering/skills/agent-workflow-designer/SKILL.md
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.
Decision brief
Tier: POWERFUL Category: Engineering Domain: Multi-Agent Systems / AI Orchestration
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/alirezarezvani/claude-skills --skill "engineering/skills/agent-workflow-designer"Inspect the Agent Skill "agent-workflow-designer" from https://github.com/alirezarezvani/claude-skills/blob/aa8d778811a557a2c28ccadda4cf3d0bd028a4cc/engineering/skills/agent-workflow-designer/SKILL.md at commit aa8d778811a557a2c28ccadda4cf3d0bd028a4cc. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Review the “Quick Start” section in the pinned source before continuing.
python3 scripts/workflowscaffolder.py sequential --name content-pipeline
python3 scripts/workflowscaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
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.
Permission review
The documentation asks the agent to run terminal commands or scripts.
python3 scripts/workflow_scaffolder.py sequential --name content-pipelineThe documentation asks the agent to run terminal commands or scripts.
python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.jsonEvidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 83/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 23,781 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Tier: POWERFUL
Category: Engineering
Domain: Multi-Agent Systems / AI Orchestration
Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.
# 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
sequential: strict step-by-step dependency chainparallel: fan-out/fan-in for independent subtasksrouter: dispatch by intent/type with fallbackorchestrator: planner coordinates specialists with dependenciesevaluator: generator + quality gate loopDetailed templates: references/workflow-patterns.md
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