2f57ef8948
- AgentHub: 13 files updated with non-engineering examples (content drafts, research, strategy) — engineering stays primary, cross-domain secondary - AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent, dry_run.py validation (57 checks) - Marketplace: agenthub entry added with cross-domain keywords, engineering POWERFUL updated (25→30), product (12→13), counts synced across all configs - SEO: generate-docs.py now produces keyword-rich <title> tags and meta descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name propagates to all 276 HTML pages - SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.), slug-as-title cleanup, domain label stripping from titles - Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts references/, scripts/, assets/ links to GitHub source URLs; skills/index.md phantom slugs fixed (6 marketing, 7 RA/QM) - Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands, 21 plugins — consistent across CLAUDE.md, README.md, docs/index.md, marketplace.json, getting-started.md, mkdocs.yml - Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
95 lines
2.9 KiB
Markdown
95 lines
2.9 KiB
Markdown
---
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title: "Agent Workflow Designer — Agent Skill for Codex & OpenClaw"
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description: "Agent Workflow Designer. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
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---
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# Agent Workflow Designer
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
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<span class="meta-badge">:material-identifier: `agent-workflow-designer`</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/agent-workflow-designer/SKILL.md">Source</a></span>
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</div>
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<div class="install-banner" markdown>
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<span class="install-label">Install:</span> <code>claude /plugin install engineering-advanced-skills</code>
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</div>
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**Tier:** POWERFUL
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**Category:** Engineering
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**Domain:** Multi-Agent Systems / AI Orchestration
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---
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## Overview
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Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.
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## Core Capabilities
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- Workflow pattern selection for multi-step agent systems
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- Skeleton config generation for fast workflow bootstrapping
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- Context and cost discipline across long-running flows
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- Error recovery and retry strategy scaffolding
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- Documentation pointers for operational pattern tradeoffs
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---
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## When to Use
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- A single prompt is insufficient for task complexity
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- You need specialist agents with explicit boundaries
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- You want deterministic workflow structure before implementation
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- You need validation loops for quality or safety gates
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---
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## Quick Start
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```bash
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# Generate a sequential workflow skeleton
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python3 scripts/workflow_scaffolder.py sequential --name content-pipeline
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# Generate an orchestrator workflow and save it
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python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json
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```
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---
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## Pattern Map
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- `sequential`: strict step-by-step dependency chain
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- `parallel`: fan-out/fan-in for independent subtasks
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- `router`: dispatch by intent/type with fallback
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- `orchestrator`: planner coordinates specialists with dependencies
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- `evaluator`: generator + quality gate loop
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Detailed templates: `references/workflow-patterns.md`
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---
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## Recommended Workflow
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1. Select pattern based on dependency shape and risk profile.
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2. Scaffold config via `scripts/workflow_scaffolder.py`.
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3. Define handoff contract fields for every edge.
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4. Add retry/timeouts and output validation gates.
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5. Dry-run with small context budgets before scaling.
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---
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## Common Pitfalls
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- Over-orchestrating tasks solvable by one well-structured prompt
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- Missing timeout/retry policies for external-model calls
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- Passing full upstream context instead of targeted artifacts
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- Ignoring per-step cost accumulation
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## Best Practices
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1. Start with the smallest pattern that can satisfy requirements.
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2. Keep handoff payloads explicit and bounded.
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3. Validate intermediate outputs before fan-in synthesis.
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4. Enforce budget and timeout limits in every step.
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