Developer Tooling
AV Wire Extract
AV Wire Extract packages a repeatable extraction procedure as a skill an AI agent can execute. Hand it an AV single-line diagram and it returns a structured wire list — the input Clean Taggy was built to consume.
01 — The problem
What was actually happening
The wire list a project actually needs already exists inside the drawing — but it exists as scattered text scattered across CAD geometry, not as data. Rebuilding it by hand means reading a dense diagram line by line and retyping every cable tag, which is exactly the step that introduces errors downstream.
- CAD drawings are a binary format built for drafting, not for querying.
- Cable tags live as loose text entities with no structural relationship to the connections they label.
- The manual read-and-retype pass is slow and produces the errors the rest of the workflow then has to catch.
- The same extraction was being repeated per drawing, with no captured procedure to make it consistent.
02 — The solution
What I built, and why
Encode the extraction as an explicit, versioned procedure rather than a one-off script — a skill an AI agent loads on demand, so the knowledge of how to read these drawings is captured once and reused on every sheet.
- Converts the drawing to a text-parseable interchange format before any interpretation happens.
- Walks text and geometry together, associating each label with the connection it belongs to instead of dumping strings.
- Normalises inconsistent tag conventions into a single predictable shape.
- Outputs clean source-to-destination pairs, ready to feed directly into Clean Taggy.
- Triggers in plain language — in English or Spanish — so it is usable by whoever has the drawing, not only by its author.
03 — Process
How the work was sequenced
- 01
Study the drawings
Worked through real single-line diagram sheets to learn how tags, connections and labels are actually laid out — and where the conventions quietly break.
- 02
Find a parseable path
Established a conversion route out of the binary CAD format into something that can be read programmatically without losing the geometry.
- 03
Associate, don't scrape
Built the association logic — matching label text to the line it annotates — which is what separates a usable wire list from a list of strings.
- 04
Normalise
Reduced inconsistent real-world tag formats to one canonical structure so downstream tools can rely on the shape.
- 05
Package as a skill
Wrapped the whole procedure as an agent skill with explicit triggers, so it is invoked by describing the task rather than by remembering a command.
- 06
Close the loop
Wired the output format directly to Clean Taggy's input, turning two separate tools into one continuous workflow from drawing to finished tags.
04 — Technology
The stack, as decisions
Extraction
Agent layer
Integration
05 — Results
What it changed
Drawing → data
A manual read-and-retype pass replaced by a repeatable extraction
Reusable
The procedure is captured once and applied to every sheet the same way
End to end
Output feeds Clean Taggy directly — one workflow, not two tools
The interesting part of applied AI is rarely the model — it is deciding what to hand it. Converting the drawing into a structure the agent could reason over, and capturing the procedure as a reusable skill, is what made the result consistent instead of impressive once.
Have a problem worth solving?
Tell me what the process looks like today and where it breaks. If technology is the right answer, I will tell you what I would build — and if it is not, I will tell you that too.