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=== WORKFLOW ===
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type: data-pipeline
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{ "sources": ["api-alpha", "api-beta"], "validationMode": "strict" }
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=== EXPECTED OUTPUT ===
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AI report: Full analytical report via AI Gateway
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Stats: Record counts, validation, aggregation
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=== SELECTED RUN ===
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id: wrun_01KHWF6FCA9RF4MJDXXRB35ZSR
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status: completed
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type: data-pipeline
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startedAt: 2026-02-20T08:48:52Z
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--- steps ---
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[{ "name": "Ingest", "status": "completed", "output": {...} },
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{ "name": "Validate", "status": "completed", "output": {...} },
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{ "name": "Transform", "status": "completed", "output": {...} },
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{ "name": "Aggregate", "status": "completed", "output": {...} },
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{ "name": "AI Report", "status": "completed", "output": {...} }]
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--- output ---
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{ "stats": { "totalFetched": 25, "validRecords": 25, "invalidRecords": 0 },
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"aggregation": { "byCategory": {...}, "bySource": {...}, "byDay": {...} },
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"report": { "summary": "...", "topCategory": "electronics", "topSource": "api-alpha" } }
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--- readable output ---
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Sources: api-alpha, api-beta
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Fetched: 25 | Valid: 25 | Invalid: 0
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By Category:
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electronics: 8 records (avg: 299.50)
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clothing: 6 records (avg: 49.99)
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AI Report (top category: electronics, top source: api-alpha):
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The data pipeline processed 25 records across two sources...
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=== CONSOLE ===
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8:48:52 AM [info] POST /api/workflows/data-pipeline
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8:48:53 AM [phase] Phase: Ingesting data from 2 sources
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8:48:55 AM [phase] Phase: Validating 25 records (strict mode)
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8:48:57 AM [phase] Phase: Aggregating by category, source, and day
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8:48:59 AM [phase] Phase: Generating AI analytical report
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8:49:00 AM [success] Run completed (8s)

Notice the structure. Labeled sections with === and --- separators. Expected output alongside actual output so v0 can compare. Both raw JSON (for programmatic analysis) and readable summaries (for quick comprehension). Console logs with timestamps so v0 can reconstruct the execution timeline.

This is intentional. The format is designed so v0 can:

1. Validate – compare expected vs actual output and confirm the workflow worked correctly
2. Diagnose – identify which step failed, what the error was, and what succeeded before it
3. Suggest fixes – propose code changes based on the failure mode and step output
4. Extend – add new steps to the workflow using the observed data shapes as reference
5. Refactor – restructure the workflow based on timing data and step dependencies

Why v0-First Matters for Rapid Iteration

The traditional workflow development cycle looks like this:

1. Write workflow code
2. Run it
3. Read the output
4. Manually describe what happened to an AI assistant
5. Get suggestions
6. Apply changes
7. Repeat

Step 4 is the bottleneck. Every time you describe output to v0, you lose fidelity. You forget to mention a step’s status, or you paraphrase an error message, or you don’t include the console timing. v0 asks follow-up questions. You go back to the tool, copy more fragments, paste them in. The context builds slowly and imperfectly.

With Copy All, step 4 becomes: paste. That’s it. v0 has everything. The iteration loop compresses to:

1. Run
2. Copy All
3. Paste into v0
4. Apply the response
5. Run again

Each cycle is fast because context transfer is instant and lossless.

The Three Included Workflows

Each workflow demonstrates different DevKit primitives, and each produces v0-optimized Copy All output:

Email Drip Campaign

A three-stage onboarding sequence. AI composes each email via the Vercel AI Gateway, Resend delivers to a real inbox, and durable sleep() timers space the sends. Copy All captures each email’s subject, body, and send timestamp so v0 can evaluate the AI-generated content and suggest improvements.

Data Processing Pipeline

Five-stage pipeline: ingest from multiple sources, validate (strict/lenient modes), normalize, aggregate, and generate an AI analytical report. Uses RetryableError for transient failures and FatalError for permanent ones. Copy All captures per-step record counts, category breakdowns, and the full AI report – v0 can analyze the entire data flow in one paste.

AI Content Agent

Research, outline generation, human approval gate via createHook(), AI-written article, and AI review with a quality score. The human-in-the-loop gate demonstrates hook.suspend() – the workflow genuinely pauses and the lab shows a resume button. Copy All captures the research findings, outline structure, full article text, and review score so v0 can evaluate the content quality and suggest editorial improvements.

Architecture

The lab is a single "use client" page with no external state dependencies. The Copy All logic is inline – it reads the current workflow template, selected run, and console logs, then assembles the structured clipboard output dynamically. This means:

• Any workflow you add to the WORKFLOW_TEMPLATES array automatically gets Copy All support
• The readable output section adapts per workflow type (emails, data reports, articles)
• The entire lab can be copied into any Next.js project – the v0-first output comes with it

Try It

• v0 template: https://v0.app/templates/workflow-lab-VfaC3JPSzkU
• GitHub: https://github.com/headline-design/workflow-lab
• Live demo: https://v0-workflow-lab.vercel.app
• Workflow DevKit docs: https://useworkflow.dev

If you build a workflow with this template, paste the Copy All output into v0 and see how fast the iteration loop gets. I’d love to hear what you build.