I have a confession: my Obsidian workspace is a high-class graveyard.
It is a sprawling, meticulously formatted repository of dead links, unread PDFs, and Substack posts I saved over the past year because I convinced myself I was “investing in my mind.”
It turns out that clicking a browser bookmark button is just hoarding with better typography.
It is the intellectual equivalent of buying a gym membership and expecting to get fit by staring at the plastic card in your wallet. We have access to more information and ideas than we could access in a lifetime, and so it makes sense that our first impulse is to archive that material. This is what many of us were trained to do within academia. We are the professional organizers and curators of what matters in our field. The problem is that saving something with an outmoded organizing system for its structural DNA is just hoarding with better intentions.
The rub lies in the cognitive tax of cataloging. When you are mid-read, you have exactly zero spare attention to think about where a piece belongs. You just want to capture the spark before it evaporates. So you toss it onto the pile, promising to file it away “this weekend”—a mystical period of infinite productivity that exists only in our imaginations.
This is where standard AI tools let you down. Ask a generic LLM to summarize a piece, and it hands you back a dry, bulleted compression that flattens the author’s argument. It strips out the voice, ignores the structure, and treats every document like a high-school book report. There is a reason we don’t like reading high school book reports: they are just a dumb, often stylistically overfit, compressor.
What you need is a cognitive filter. You need a system that understands how the author is thinking before it records what they said.
That is what this curational engine sets out to solve.
https://box.boodle.ai/a/@CognitiveFilter
You paste in a Substack post (either the URL or the raw text but make sure it actually reads the text if you give it a URL) and the engine performs a structural audit. It does not just ask “what is this about?” It asks: “what is this piece trying to do?”
Specifically, it classifies the argument into one of four distinct intellectual gears:
Factual/Empirical: The piece makes claims about what is true and how a system works.
Conceptual/Definitional: The piece redefines or reframes what something means.
Practical/How-To: The piece provides a repeatable, sequential recipe to achieve a specific outcome.
Philosophical/Values: The piece makes an argument about what matters and why.
This classification is the difference between a functional vault and a digital junk drawer. When you search your database six months from now, you are rarely searching for “that post about AI.” You are searching for “the conceptual argument that reframed AI as a partner rather than a tool.” Those are entirely different intellectual operations. By capturing the cognitive category at the moment of ingestion, you build a retrieval system designed for active thinking, not passive storage.
How to set it up
Below the fold, you will find the complete system prompt engineered with strict XML tags, a structural diagram mapping the logic, and a detailed breakdown of the cognitive mechanics under the hood.
I have also mapped out four advanced ways to hack this engine—including a terminal-native CLI setup using local developer agents like Claude Code or Cowork to bypass web interfaces entirely.
The complete, copy-paste prompt, the logic map, and four ways to hack it for localized CLI workflows are below the fold for paid subscribers.
