agent-workflow-designer

Overview of agent-workflow-designer skill

What agent-workflow-designer is for

agent-workflow-designer is an engineering skill for designing multi-agent workflows before implementation. It helps you choose between sequential, parallel, router, orchestrator, and evaluator patterns, then define the handoff contracts, retry behavior, timeouts, context limits, and quality gates that make the workflow operable instead of just conceptually interesting.

Best-fit users and projects

This skill is most useful for teams building agent pipelines, AI product prototypes, internal automation, content production systems, incident triage flows, research workflows, or code-review agents where a single prompt has become too broad. It fits users who already know the task they want to automate but need help turning it into explicit agent roles, dependencies, outputs, and failure handling.

Why it is different from a generic prompt

A normal prompt may suggest “use multiple agents.” The agent-workflow-designer skill pushes toward a concrete architecture: pattern choice, agent boundaries, fan-out/fan-in behavior, evaluator loops, fallback routes, and minimum handoff fields such as workflow_id, step_id, task, constraints, upstream_artifacts, budget_tokens, and timeout_seconds.

Key files to inspect before installing

For an install decision, read SKILL.md first for scope and usage, then references/workflow-patterns.md for pattern templates and selection heuristics. If you want generated starter configs, inspect scripts/workflow_scaffolder.py; it provides skeleton JSON for common patterns such as sequential, parallel, router, and orchestrator.

How to Use agent-workflow-designer skill

agent-workflow-designer install context

Install the skill from the repository path used by your Claude skills setup:

npx skills add alirezarezvani/claude-skills --skill agent-workflow-designer

The upstream SKILL.md does not center the install command, so verify your local skill manager supports GitHub-based skill installation and that scripts are available after install. The skill lives under engineering/skills/agent-workflow-designer, with support content in references/ and scripts/.

Inputs that produce better workflow designs

Before invoking the skill, prepare five pieces of information: the business goal, required outputs, agent/tool constraints, failure tolerance, and budget or latency limits. Weak input is “design an agent workflow for research.” Strong input is: “Design a workflow for weekly competitor research. Sources are web search and internal notes. Output is a cited brief. Max 5 minutes runtime, evaluator must check source quality, and failed source collection should retry once then continue with a warning.”

Practical agent-workflow-designer usage flow

Start by asking whether the task should be single-agent or multi-agent. If multi-agent is justified, ask the skill to choose a pattern and explain the tradeoff. Then request a workflow contract: agents, responsibilities, inputs, outputs, dependencies, retry rules, timeout values, context budget, and evaluation criteria. Finally, convert the design into a skeleton config and adapt it to your runtime.

Example prompt:

Use agent-workflow-designer for Agent Orchestration. Design a production workflow for incident triage from incoming alerts. Choose the pattern, define specialist agents, handoff fields, retries, timeout rules, evaluator checks, and a JSON-like workflow skeleton. Keep context passed between agents minimal and specify fallback behavior.

Using the scaffolder script

If you want a starter file, run the bundled Python script from the skill directory:

python3 scripts/workflow_scaffolder.py sequential --name content-pipeline
python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json

Treat generated configs as scaffolds, not production-ready orchestration. Rename agents, replace placeholder tasks, add real tool calls, define observable outputs, and align retries and timeouts with your infrastructure.

agent-workflow-designer skill FAQ

When should I use agent-workflow-designer?

Use agent-workflow-designer when your workflow has multiple stages, specialist responsibilities, branching decisions, parallel tasks, or mandatory review loops. It is especially valuable when context bloat, vague handoffs, or unpredictable agent behavior are already causing failures.

When is this skill overkill?

Do not use it for simple one-shot generation, small transformations, or tasks where a single model call with clear instructions is enough. Multi-agent design adds coordination overhead, more failure points, and higher cost. The skill is best when that overhead buys reliability, separation of concerns, or throughput.

Is it beginner-friendly?

It is accessible if you understand basic agent concepts, but it is not a no-code product builder. Beginners should start with references/workflow-patterns.md, choose one pattern, and avoid combining router, evaluator, and orchestrator designs until the first workflow is observable and testable.

How does it fit an agent orchestration stack?

The skill does not replace LangGraph, CrewAI, AutoGen, custom queues, or internal orchestration code. It helps design the architecture those tools implement: agent roles, dependency structure, fallback paths, validation loops, and handoff payloads. Think of it as a planning and specification layer before coding.

How to Improve agent-workflow-designer skill

Improve agent-workflow-designer inputs

The biggest quality gain comes from describing constraints, not just goals. Include allowed tools, unavailable data, compliance limits, maximum iterations, token budget, latency target, and what counts as success. For example, “review until correct” is vague; “run at most 3 evaluator iterations and require a pass threshold of 0.8” gives the skill a designable control loop.

Avoid common workflow design failures

Watch for agents with overlapping responsibilities, missing fallback routes, unbounded evaluator loops, excessive context passed between steps, and no definition of intermediate artifacts. Ask the skill to explicitly list these risks in the proposed design. If the workflow has no clear handoff contract, it is not ready to implement.

Iterate from pattern to production contract

A good improvement loop is: pattern selection, workflow skeleton, handoff schema, failure model, evaluation plan, then implementation notes. After the first output, ask: “Which agents can be merged?”, “Where can context be summarized?”, “What should be retried versus escalated?”, and “Which outputs should be logged for debugging?”

Customize the references and scaffolder

For repeated use, adapt references/workflow-patterns.md with your organization’s preferred patterns, naming conventions, risk categories, and standard timeout values. Extend scripts/workflow_scaffolder.py with templates for your real orchestration platform so the agent-workflow-designer install becomes a practical starting point rather than a generic architecture exercise.