AI-assisted FileMaker development

From a conversation to the Script Workspace.

How I build FileMaker scripts and web apps with Claude Code, an Obsidian vault, VS Code, my own Markdown-to-XML converter, Python tests and live verification in FileMaker.

    Carries forward

    Pick any stop on the line to jump there.

    The whole loop

    How does AI-assisted FileMaker development work?

    Every build starts as a Claude Code project inside my Obsidian vault, with a FileMaker-WebDev skill that holds my script and web standards. Claude turns my requirements into a web app and FileMaker scripts, Python tests check them, and I review everything before converting the scripts to fmxmlsnippet XML, pasting them into FileMaker and verifying the result live.

    View the loop as
    1. 01
      Claude Code

      Start a Claude Code project

      Every build starts as a new Claude Code project. Its folder becomes the one place where requirements, scripts, web code and tests live.

    2. 02
      Vault

      Give it context through Obsidian

      The project folder sits inside my Obsidian vault. The requirements and schema notes I write in Obsidian are the same files Claude reads, so nothing gets copied between tools.

    3. 03
      Skill

      Load the FileMaker-WebDev skill

      My FileMaker-WebDev skill rides along with every session. It holds the best practices and script step rules I expect, so everything Claude writes follows them.

    4. 04
      Requirements

      Describe my requirements to Claude

      I describe what I need in plain language and point Claude at the requirements in the vault. Claude reads them with the schema notes and lays out a plan before writing anything.

    5. 05
      Generate

      Claude generates the web app and FileMaker scripts

      Claude writes both halves: the HTML, CSS and JavaScript for the web app, and the FileMaker scripts that work with it. Every file lands in the project folder, inside the vault.

    6. 06
      Python tests

      Verify with Python tests

      Right after generation, Claude writes a Python test suite for the new scripts and web code. It runs from the project folder, and any failure goes straight back to Claude as a fix.

    7. 07
      Markdown

      Save the scripts as Markdown

      Once the tests pass, each FileMaker script is saved as plain-text steps in a Markdown file, one step per line, the way the Script Workspace prints them. The files go into the project’s scripts folder in the vault.

    8. 08
      Obsidian

      Review it in Obsidian

      Because the script lands in the vault, Obsidian renders it right away. I read and check it there as a document before it goes anywhere near FileMaker.

    9. 09
      VS Code

      Review the web code in VS Code

      With the scripts reviewed, I open the web app in VS Code on macOS and read its HTML, CSS and JavaScript straight from the project’s web folder.

    10. 10
      Converter

      Convert it to fmxmlsnippet

      I paste the Markdown into my own converter page. It turns each plain-text step into the fmxmlsnippet XML that FileMaker reads, ready to copy to the clipboard.

    11. 11
      FileMaker

      Paste scripts and save the web app in FileMaker

      One paste in the Script Workspace turns the clipboard into real script steps. The web app’s HTML, CSS and JavaScript go into text fields on a tagged record, and the web viewer builds its source from those three by querying the tag.

    12. 12
      Live tests

      Run live tests for verification

      Last, I run everything for real in FileMaker: the script against live data, the web viewer form, and the server schedule. Anything that misses becomes the next request.

    The tools and their order are my real process. The project, script, file, test and result names in the animation are examples. How client data is handled

    Responsible AI

    How is client data protected when AI is part of the build?

    My AI use follows responsible AI practices from the NIST AI Risk Management Framework. Sensitive customer data, credentials and private keys are never sent to an AI model. If a client’s NDA prohibits AI, the project runs the classic way, with human-derived code, design and testing.

    • Guided by the NIST AI RMF

      Responsible AI practices from NIST’s AI Risk Management Framework shape how AI is used on every project.

    • No sensitive data, credentials or keys

      Sensitive customer data, credentials and private keys are never sent to an AI model.

    • Your NDA comes first

      If your agreement prohibits the use of AI, development reverts to the classic method: human-derived code, design and testing.

    Let's Build Something

    Want this process behind your next FileMaker project?

    Tell me about the workflow you're trying to fix. I'll get back to you within one business day with honest next steps.

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