Tyler McMaster

Enjoys thinking, learning, and chewing on problems.

If you’ve maintained a Knack app for any length of time, you know the moment: you’re staring at a field named something like Status (OLD - do not use?) and wondering… can I actually delete this? Is it feeding a formula somewhere? A filter? A page rule buried three scenes deep?

The Knack builder is great for building, but answering “where is this used?” means clicking through every scene and view, one at a time, taking notes like a detective canvassing a neighborhood. I got tired of doing that by hand, so I built a tool to do the canvassing for me.

KnackSleuth investigates your Knack app’s metadata and reports where objects, fields, and connections are used throughout your application — data relationships, view dependencies, and the references hiding in formulas and filters.

Quick start

No install required if you have uv:

uvx knack-sleuth --help

Or install it properly:

pip install knack-sleuth
# or
uv tool install knack-sleuth

Point it at your app with KNACK_APP_ID set (or pass a metadata JSON export), and start asking questions.

Where is this object used?

knack-sleuth search-object "Work Orders"

This reports every place the object shows up: connections from other objects, the views that display it, and field-level usages — columns, sorts, formulas, filters. It even prints direct links to the specific builder pages you’d want to review, so the “go check it” step is one click instead of a scavenger hunt.

A map of your whole app

knack-sleuth list-objects --sort-by-rows

Beyond row and field counts, this borrows a couple of ideas from software engineering — afferent/efferent coupling and an instability score for each object:

  • High Ca, low Ce — hub objects that everything else depends on. Change these carefully.
  • Low Ca, high Ce — objects with lots of outbound dependencies; potentially fragile.
  • High both — central and complex. Prime refactoring candidates.

It turns “I think this table is important?” into something you can actually see.

It’s a library, too

The foundation of KnackSleuth is a Pydantic model of the entire Knack application metadata, so you can build your own analysis on top of it:

import json
from pathlib import Path

from knack_sleuth import KnackAppMetadata, KnackSleuth

data = json.loads(Path("my_app_metadata.json").read_text())
app_export = KnackAppMetadata(**data)

sleuth = KnackSleuth(app_export)
usages = sleuth.get_object_info("object_12")

Everything is typed and validated, which makes exploring the metadata in a notebook a pleasant experience instead of a dict["nested"]["key"]["guessing"] game.

The details

  • Metadata fetched from the Knack API is cached locally for 24 hours, so repeated queries don’t hammer the API (use --refresh to force a fresh pull).
  • Works from a local JSON export if you’d rather not touch the API at all.
  • Source on GitHub, package on PyPI.

If you try it on your own app, I’d love to hear what it finds — especially if it catches a dependency you’d forgotten about. That’s the whole point: fewer surprises, faster detective work. 🕵️

You May Also Enjoy