AI-Scaffolded Hooks: From Prompt to Production in 5 Minutes

What if creating a new business logic hook took 5 minutes instead of 45? With AI-assisted scaffolding in Logic Bee, it can — but only if you know what to verify before hitting merge.

The Premise

Every Logic Bee hook follows the same anatomy: a @LogicHook decorator, a static execute() method, the Bob Wrapper pattern, and a returnEventResult call. That's a lot of boilerplate that's identical across hundreds of hooks — and boilerplate is exactly what AI excels at.

But scaffolding is only half the story. The other half is knowing what the AI cannot know: your business rules, your tenant boundaries, and the edge cases that live in your head.

The Story

A developer needs a new hook in the finance-bills library to calculate late fees on overdue invoices. Instead of copy-pasting from an existing hook, they open their AI agent and type:

"Create a new hook in finance-bills that calculates late fees on overdue invoices. Skip bills with zero balance. Use the creating-hooks and flow-query skills."

In under a minute, the AI:

1. Runs the CLI — npx tsx scripts/logic-bee-create-new-hook.ts scaffolds the folder, .hook.ts, hook.yml, and context.yaml
2. Searches for patterns — finds calculate-bill as a reference implementation
3. Writes the logic — generates a complete hook with FlowQuery chains, naoDateTime() comparisons, and naoUtils.mathChain() for fee calculations
4. Self-validates — checks all 10 hard rules and 12 soft conventions

The developer has working code. But is it production-ready?

What the AI Gets Right

The generated hook follows every structural convention perfectly:

• File path: hooks/finance-bills/calculate-late-fees/calculate-late-fees.finance-bills.hook.ts
• Decorator metadata: name, path, library, method — all correctly derived
• Bob Wrapper: let ok = true, error: any = null, data → try/catch → returnEventResult
• Arrow comments: // -->Get:, // -->Set:, // -->Iterate:
• Error handling: naoFormatErrorById('bad_request', { reason: '...' })

This is the 80% that would have taken a human 30 minutes to type.

What You Need to Verify

1. Tenant Scoping

Every FlowQuery chain must pass bob.flowUser. The AI almost always includes it, but a missing .user(bob.flowUser) means cross-tenant data leakage.

// ✅ AI usually generates this correctly
const bills = await fc.docs.flowQuery()
  .user(bob.flowUser)  // ← verify this exists on EVERY query
  .flowOptions(eventOptions.billNaoQueryOptions)
  .query({ 'data.status': 'posted' })
  .getMany(undefined, 'FinanceInterface.Bill')

2. Business Logic Accuracy

The AI calculated late fees as a flat percentage. Your business rule says it should be tiered: 1.5% for 1–30 days, 3% for 31–60 days, 5% for 60+. The AI doesn't know this unless you told it.

3. Transaction Session Usage

For hooks that update multiple documents, verify bob.dbSession() is passed to all write operations. The AI may omit it on some update calls, breaking atomicity.

4. Edge Cases

The prompt said "skip bills with zero balance," and the AI added a guard clause. But what about negative balances (credits)? What about bills in draft status?

The Takeaway

AI scaffolding eliminates the mechanical overhead of creating hooks. Here's your pre-merge checklist:

• .user(bob.flowUser) on every FlowQuery chain
• bob.dbSession() on every write operation
• Business logic matches actual requirements (not AI assumptions)
• Edge cases covered: nulls, zero values, unexpected statuses
• eventOptions declared at the top of the try block

The rule of thumb: let the AI write the structure, but own the logic.