The Kickstart

Complex regulatory requirements, Salesforce-heavy workflows, data integrity across multiple systems. That's a lot of context to carry around in your head every day. Organizing it is what matters most.

This isn't a playbook. It's a kickstart. Everything I'd walk you through if you sat down next to me and said "okay, show me."

1. Watch These Two Videos and Follow Along

If you're motivated and ready to go, this is all you need.

These two videos break it down better than I can in text. Don't just watch them. Pause, follow along, do what they do. By the end you'll have a working setup and you'll be ready to do everything I talk about below.

Seriously. If you do nothing else on this page, do this.

Not Sold Yet? Here's What It Actually Does.

If you're still on the fence about whether this is worth the time, these are real results from my first few weeks.

You don't need a six-skill pipeline to get results like these. Start with the videos above, then pick one real task from your actual work and try it.

2. The Folder System

Claude doesn't read your mind. It reads your folder.

Most people hit one of two problems with AI. Either they give it zero context and get back something generic and useless, or they dump 50 pages of documents into a conversation and the answers wander all over the place.

The real question isn't "how do I give AI more context?" It's "how do I give it the right context, automatically, every time?"

That's what the folder system solves.

Three layers. Each one loads automatically based on where you're working.

This is especially powerful when you work across multiple teams or problem spaces. There might be tons of technical deep-dive details or tons of history in your workspace. But for a specific conversation, all you really need is the mid-level problem and how (or if) it aligns to a company objective. The folder system lets the AI navigate to the right depth instead of drowning in everything.

Going Deeper: The PM Brain Architecture

The three-layer folder system above is the starting point. For product management specifically, I evolved it into a four-layer architecture that maps how raw signal becomes strategy. The short version of why it exists: I went from being a vibe PM to a receipts PM, and this architecture is what made the switch possible.

From Vibes to Receipts

Before this existed, my day looked like ten tabs and a prayer. One tab for customer research. One for the strategic debate partner. One for the big rocks. One for the Slack thread I was about to lose. One for the doc I was trying to write. And four others I'd open and forget. I'd manually copy everything into one place, lose the higher-level objective somewhere around the third paste, and end up throwing a dart at the wall sooner than I wanted to. "I think this is one of the bigger problems. Let's go work on it for three months."

My short-term working memory is exceptionally bad, which is why I used to live in Lucid and Miro. My favorite thing in product management was being in a room with smart people, an architect, and a whiteboard, hashing out the customer's actual problem. The whiteboard was my second brain. Now the PM Brain Architecture is.

Today I can multithread. Customer research, mockups, expert outreach, architecture gut-checks, and synthesis all happen at the same time. Things that used to eat a week now eat half an afternoon. More importantly, I don't show up to leadership with vibes anymore. I show up with receipts. A book of signals, a synthesis document that sits on top of those signals, and a one-page strategy brief that ties to the company's actual objectives. The arguments are about evidence now, not opinion.

The Four Layers

Each layer feeds the one above. Signals are the receipts. Synthesis is the analysis. Strategy is the decision. Persistent memory keeps the whole thing coherent across sessions and folders.

Layer 1

Signals

Per-component evidence dossiers. Customer quotes, ARR impact, support tickets, the link back to the source for every claim. Raw material, organized by what it's about, not when it arrived. This is where the receipts live.

Layer 2

Synthesis

Themed research docs. After looking at the signals and grouping the patterns, this is where I add competitor research, market context, and my own expertise. The output is two or three well-formed problem statements with the highest-value actions laid out as options. 5-15 pages each. Evidence becomes analysis.

Layer 3

Strategy

Why we're doing this and not the other thing. A one to two page executive brief plus a three to four page presentation, tied to the company's big rocks. Includes a "what would change this" section so future-me can audit the decision instead of guessing why past-me made it. This is what I take to the head of product.

Memory

Persistent Memory

CLAUDE.md and MEMORY.md. This layer touches everything because every session has to start oriented and end with the context preserved. More on these two files in a moment.

Signal sources (Gong, Granola, Slack, Jira, support tickets, help docs) feed into Layer 1. From there, evidence flows up through synthesis into strategy. Outputs get published to Confluence and backed up to Google Drive. Active work happens in Cowork sessions. Obsidian sits over the same folder for browsing and graph view, with zero duplication.

A Real Example

Take error messaging between Salesforce and a third-party platform. Layer 1 captures every piece of evidence: which customer, what the issue was, the business impact, and a link back to the source. Support tickets, Gong calls, Slack threads, the works. Within ten seconds I can kick off five searches in parallel and bring back the top trends in priority order, ranked by how many customers reported them in the last 90 days.

One trend was that errors were happening but the Salesforce flow wasn't surfacing them. Customers thought everything was fine when it wasn't. Another was that errors were displayed but the codes were hard to read. Same category of problem, very different urgency, very different fix. With the receipts in hand I could go to leadership and say "we need to surface messages where customers actually are when they take the action," and back it with the count, the customer ARR exposure, and the source for every claim.

That used to be a week of guessing. Now it's a morning of confirming.

The Receipts Compound

The unlock isn't only for me. I had an initiative I'd intentionally sliced thin into four epics, expecting roughly a month of work. One of our developers grabbed the signals doc I'd linked to the epic, a Confluence page with every acceptance criterion and pain point in priority order, and imported the whole context into his own Claude session. He shipped epics one, two, and four in two days. Day three is tomorrow. What I thought was a month is closing in three days. The developer didn't get faster. The context did.

CLAUDE.md and MEMORY.md

These are the two files that make persistent memory actually persistent. They sound similar and they do very different jobs.

CLAUDE.md is the orientation doc

It tells the AI who I am, what I do, what tools I use, and how I like things. It's per folder. It loads every time I open that folder. It rarely changes. Think of it as the new-hire onboarding doc for an infinitely fast coworker.

MEMORY.md is the context bridge

It's where I save the things I want to remember between sessions. Decisions in flight, parked ideas, the current state of whatever I'm working on. It changes constantly. It's how a session that ends Monday picks up coherently on Wednesday.

The folder structure does the organizing. Obsidian's graph view sits on top and shows me visually how the notes connect to each other. The graph is the map. The folders are the territory. The two memory files are how the AI stays oriented inside both.

Where It Still Creaks

A few things this doesn't solve yet, in case you're thinking of building your own version.

Sharing across the team

Confluence pages attached to epics work, but they're a snapshot. The living version still lives in my workspace. I haven't found a clean way to share the brain itself, only its outputs.

Slicing to a winnable objective

The architecture helps me see the whole problem, which is great for strategy and brutal for scoping. Cutting a five-month vision into a two-week win is still mostly a human judgment call.

Technical feasibility gut-checks

I still lean on architects and senior developers to tell me if a direction is even possible. Pointing the system at GitHub repos for a faster gut check is on the list. Not done yet.

The connective tissue between all four layers is a set of six skills I built to automate the workflow I used to do by hand. That's its own page, coming soon.

Who This Is For

A product manager at a 50 to 300 person SaaS company who's drowning in customer signal and tired of making decisions on vibes. Also a no-code builder or solo founder who needs to think clearly about what to build next without a team to do the synthesis for them. If your work is context-heavy and your short-term memory is finite, this is for you.

View the full architecture diagram →

3. Skills

Skills aren't settings. They're coworkers you trained.

If you don't know where to start, start here: download a plugin. The productivity plugin is a great first one, or whatever plugin matches the role you're in. You don't need to build anything from scratch.

Here's what happens next. You run a skill, and the output isn't quite right. Instead of starting over or tweaking your prompt, you just talk to it. Literally in that moment:

That feedback accumulates. The skill slowly becomes yours. Over time, your AI starts to feel like your AI. Not a generic assistant. Yours.

And when you're ready to go further, you can take any skill and say: "I like what this does, but I want to create my own version customized to me. Here's my workflow, here are the tasks I do, here's what's special about my job. Help me build it." Walk through it step by step, and you can automate a piece of your workflow that used to eat hours.

What I've Built

I have a six-skill pipeline that takes raw customer signal (Gong calls, Slack threads, meeting notes) and turns it into stakeholder-ready output. Problem synthesis, theme mapping, RICE scoring, value slicing, epic generation, and deck creation. Chained together.

You don't need a six-skill pipeline to get value. Start with one plugin. Use it. Talk to it. Make it better. Then pick the next thing.

4. Projects

Two sentences. That's all you need to understand this.

Claude Projects aren't for organizing files. They're for injecting persistent context so every conversation starts informed. The folder structure you build around the project matters more than the project itself. The project is the anchor.

If you read the Folder System section above, you already get the principle. Projects are the mechanism that makes it work. Drop your instruction files in, set the context, and every new chat starts with everything the AI needs to be useful right away.

5. Go Play With It

Structure is in place. Now use it. Don't try to optimize. Just go.

If You're Stuck

What's Coming

Everything above is what works today. Here's where this is going.

Ask Yourself This