Payments at Scale #2: The AI Skill That Integrates Like You Would
We have had the same conversation with clients over and over. The engineer has read our docs. The API makes sense. And then the real question comes: "Okay, but how do I actually fit this into what we already have?"
That is where the hours go. Not the API surface. The main complexity of the integration very often is not about integrating with the API itself but rather figuring out how to fit the new integration into the existing infrastructure, flows, and assumptions in a consistent way.
The PaymentProvider interface the team agreed on two years ago, the webhook pipeline with its specific event envelope, the retry logic baked into a shared HTTP wrapper. A new payment provider has to fit all of that, consistently with everything else already there.
That is the problem we built our integration skill to solve.
The Real Friction Is Contextual
Most payment integration tools approach this as a documentation challenge. Better structured docs, ready-made snippets, AI-compatible prompts. That helps.
But when we looked at where clients actually got stuck, it was never "I do not understand Request signing." (even though, we have an amazing doc about that 🙂) It was: "I have a PaymentGateway abstract class that all my providers extend, and I need Nopan to fit that same pattern." Or: "Our webhook handler expects a normalized event envelope. How do I wrap Nopan's format in that?"
We knew we can do more.
What the Skill Does
Drop it into Claude, Cursor, Windsurf, or ChatGPT and point it at your codebase. The skill looks for your existing payment provider integrations: your PspOneProvider, your PspTwoGateway, whatever you have. It understands how those are structured - the abstractions, the error handling, how you model a charge or a refund, your test patterns.
Then it builds the Nopan integration the same way.
The output is not boilerplate. It is code that looks like it belongs in your payments service, because it follows the same structure and conventions as everything around it.
It also handles the parts that cause silent production bugs when done manually: request signing, webhook signature verification, and idempotency key management. These are correct and tested out of the box.
How to Use It
Add the skill to your AI tool's context, then say something like: "I have an existing integration under src/payments/providers/existingIntegration. Add Nopan using the same pattern."
That is it. For most codebases, the full integration including signing, webhooks, and basic flow tests takes under 30 minutes.
Docs and Skill, Not Docs or Skill
Our API documentation covers everything: authentication, request signing, webhooks, error handling, edge cases. It is thorough and it is good. But documentation answers "how does Nopan work?" The skill answers a different question: "how should Nopan fit into what we already have?"
Those two questions need two different tools. The skill works best when you already understand what you are integrating. The docs get you there. Then the skill takes the context your AI tool already has about your codebase and does the structural work of fitting Nopan in consistently.
The skill is available now here. If you are evaluating Nopan, it is also a practical way to see what integration actually looks like in your stack before you commit.
If your codebase has patterns it does not handle well yet, tell us. That feedback shapes the next version.
