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
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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.
- 01Claude 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.
- 02Vault
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.
- 03Skill
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.
- 04Requirements
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.
- 05Generate
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.
- 06Python 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.
- 07Markdown
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.
- 08Obsidian
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.
- 09VS 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.
- 10Converter
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.
- 11FileMaker
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.
- 12Live 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.
- Inner orbit · Claude builds
- Middle orbit · Claude’s context
- Outer orbit · I review, convert and verify
- Distance from Claude shows how much of each step Claude does.
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
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.
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Guided by the NIST AI RMF
Responsible AI practices from NIST’s AI Risk Management Framework shape how AI is used on every project.
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No sensitive data, credentials or keys
Sensitive customer data, credentials and private keys are never sent to an AI model.
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Your NDA comes first
If your agreement prohibits the use of AI, development reverts to the classic method: human-derived code, design and testing.