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Skill files: Plain-language instruction sets that give AI the architectural rules, formula patterns, and naming conventions of a seasoned Coda expert.Core Coda competences: Each skill file maps to a real capability — formulas, data architecture, page structure, column design— turning expertise into context an AI can act on.AI-ready by design: Whether you use Antigravity, Claude, or another AI assistant, the skill file is the bridge between your intent and a result that holds up.
🗂 Data architecture: How to model relational tables that stay clean as a workspace grows — and how to encode those decisions into a file an AI can follow.✍️ Formula logic: The patterns behind thisRow, Filter(), Format(), and the formulas beginners fear. Not memorised — understood, then written into skill files.📐 Page & UI standards: Naming conventions, callout structure, column lifecycle rules. The decisions that make a Coda doc legible to someone who didn’t build it.🧠 Steering AI agents: How to write skill files that work — what to include, what to leave out, and how to verify the AI is actually following them.
The Concept
The reasoning and mechanics behind Skill Files.
The Problem Worth Solving
Most people using AI with Coda get mediocre results — not because the AI is bad, but because it has no context. It doesn’t know your table conventions, your formula patterns, or why you should never use a Select List where a relational table belongs.
Skill files solve this. They carry the accumulated judgement of someone who has made the mistakes, documented the lessons, and knows which Coda decisions survive contact with a real workspace.
What Coda Curious Is
Coda Curious is where Coda expertise becomes teachable. Not tutorials. Not tips. A structured method for encoding what an expert knows into files an AI can follow — so that any maker, at any coding level, can steer an AI to build, maintain, and improve a Coda doc with confidence.
What You Can Learn Here
How the Blogs Feed the Files
Each blog post is a working record of a problem encountered, a pattern tested, and a conclusion reached. Some are conceptual, while others are deeply practical. Over time, certain approaches have been —where a better solution has emerged, I’ve noted the update in the .
The blogs feed the skill files, and the skill files lead to higher-quality AI outputs. The goal is a loop of continuous improvement that any maker can enter at any point.
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