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name: workflow-builder description: Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents. license: MIT metadata: inspired_by: "https://github.com/ray-amjad/claude-code-workflow-creator (Ray Amjad)" targets: "Claude Code Workflow tool (CLAUDE_CODE_WORKFLOWS=1, /workflows)" version: 1.0.0

Workflow Builder

Author runnable workflow scripts for Claude Code's Workflow tool: deterministic multi-agent orchestration files (.js) that fan work out to fresh-context sub-agents under plain JavaScript control flow. Only leaf agent() calls spend tokens, so the main session stays clean and the whole run is resumable.

ALWAYS start every session with intake (non-negotiable)

Before proposing or writing any workflow, run the intake. Do not skip to code.

1. Ask what kind of workflow they want. Use this opening question set:

   • What repeatable, multi-step task do you want to automate?
   • What is the one unit of work a single sub-agent does once?
   • How many units — a known list, or discovered by looping?
   • Do later steps need all prior results at once, or can each item flow on its own?
   • Does any step need structured data back (a verdict, a list, scores)?
   • Roughly how many tokens / how deep should it go?

2. If the user is vague, do NOT stall. Run the recommendation engine to turn whatever you have into 1-2 concrete proposals, then present them with the reasoning:

   python scripts/workflow_intake.py --task "their description" \
     --units unknown --stages unknown --needs-all unknown --structured unknown
   

   The engine returns a recommended topology (fan-out / pipeline / loop / barrier / judge-panel), model picks, a budget guard, and a one-line rationale per choice. Present those as "Here's what I'd build and why" — never ask the user to re-answer questions they already half-answered.

3. Confirm the shape with the user (topology + phases + parallel-vs-pipeline) before writing the file. This is the only approval gate.

See references/decision_and_intake_guide.md for the full question framework, the vague-input playbook, and worked recommendation examples.

Decide if a workflow is even the right tool

Scenario	Use
Single sub-agent, one task	plain Agent tool
Reusable procedure, Claude picks steps dynamically	a Skill
Many sub-agents in a fixed topology, deterministic + resumable	Workflow ✓

Workflows earn their cost when work is parallel or multi-stage, must be reproducible, long enough to fail halfway (so resume matters), or benefits from isolating each step in its own context window. For one-off tasks, just use Claude directly.

Build → validate → run loop

1. Scaffold a starter from the confirmed topology:

   python scripts/scaffold_workflow.py --topology pipeline --name pr-triage \
     --description "Triage open PRs" > .claude/workflows/pr-triage.js
   

2. Edit the file: meta block first (pure literal, first statement), then the async body using the injected globals — agent(), pipeline(), parallel(), phase(), log(), budget, args, workflow(). Full surface in references/api_reference.md; copy-paste shapes in references/orchestration_patterns.md.
3. Validate before running — catches the parser-fatal mistakes:

   python scripts/validate_workflow.py .claude/workflows/pr-triage.js
   

4. Run it: enable the feature with export CLAUDE_CODE_WORKFLOWS=1, save the file under .claude/workflows/, then use /workflows to launch and watch it live. Press P to pause/resume, X to skip a sub-agent. Failed agents retry automatically.

Hard rules (validator enforces these)

• meta is a pure literal and the first statement — no variables, spreads, template strings, or function calls inside it.
• No non-determinism: Date.now(), Math.random(), argless new Date() break resume — pass timestamps via args.
• No filesystem / Node APIs (require, fs, process, network) in the orchestrator — that work belongs inside agent() prompts.
• parallel() takes thunks (() => agent(...)), not bare promises. Default to pipeline() unless a stage needs the whole prior result set.
• Guard every open-ended loop with a counter or budget.remaining() check — unguarded loops hit the 1000-agent cap.
• Filter skipped/failed agents: results.filter(Boolean).

Tooling

• scripts/workflow_intake.py — intake recommendation engine (topology + model + budget + rationale from vague input).
• scripts/validate_workflow.py — stdlib linter for the rules above; PASS / WARN / FAIL with line numbers.
• scripts/scaffold_workflow.py — generate a starter .js for any topology.
• assets/templates/ — fan-out, pipeline, loop-until-budget starters. assets/examples/ — a complete runnable workflow.

All scripts run with --sample (no args) and --help.