Teach your code to speak to your AI
Your AI forgets your architecture every session. SSA gives your codebase a vocabulary that sticks, so your AI understands your system the moment it opens the project.
Understand any codebase your human gives you
You lose architectural context between sessions. SSA encodes meaning into names and structure so you can reason about any system from the first line of code you read.
Your AI keeps forgetting your architecture
You spend the first 20 minutes of every coding session re-explaining your system. By hour three, your AI is making suggestions that contradict decisions from hour one. You need it to just know.
You lose the architecture between sessions
Context windows are finite. When a human says "fix the payment thing," you have to search hundreds of files to understand what they mean. There is a better way to receive that information.
Re-onboarding every session
You explain your architecture to the AI, it works great for an hour, then the context drifts. Next session, you start from scratch.
No structural memory
Each session starts cold. The human's architecture exists in their head, not in the code. You have to infer relationships from generic naming.
Contradictory suggestions
The AI suggests a payment flow that breaks the booking flow it helped you build yesterday. It has lost the thread.
Ambiguous boundaries
PaymentService, PaymentProcessor, PaymentHandler, PaymentController. Which is authoritative? Conventional naming gives you no structural signal.
Documentation that drifts
READMEs go stale. Wikis diverge. The AI reads documentation that describes a system from three months ago.
Semantic sparsity
createBookingTransaction() tells you the action but not the domain, the system, or where it fits in the larger architecture.
AI decisions you cannot audit
When the AI makes a change, you cannot easily tell which system it touched or whether it had the right permissions to touch it.
No permission signal
You cannot tell from the code which actor is supposed to invoke a function. Permission and ownership are implicit, not encoded.
Five layers. One vocabulary. Every mind aligned.
SSA gives your codebase a vocabulary that both you and your AI can understand. Every part of the system gets a single, evocative name. The names are yours to choose.
SSA encodes architectural intent into naming and structure. Each layer carries information density that reduces your dependence on external context injection.
"Market is where my customers shop"
Actor identity + permission scope at the entry point
"Vault handles all the money stuff"
Bounded domain with predictable internal structure
"Vault.Booking is a specific type of payment"
Domain entity within a system, scoping operations
"Vault.Booking.create() starts a new one"
Verb from a closed set: create, update, delete, list, get
"Vault.Booking.Created means it worked"
Typed result encoding success, failure, or domain event
// Which service is authoritative?
// What system does this belong to?
// Who is allowed to call this?
paymentService.createBookingTransaction()
authController.validateUserToken()
notificationHelper.sendEmail()// System.Component.action()
// Domain, entity, verb. Every time.
// The name is the documentation.
Vault.Booking.create()
Gate.Token.validate()
Ping.Email.send()The vocabulary is yours. SSA does not prescribe "Vault" for payments. Your team might prefer "Treasury," "Coffer," or "Mint." A restaurant app might call it "Tab." The power is in choosing names that carry meaning through metaphor. If the name makes you smile, you will remember it. If you remember it, you will use it. If you use it, the AI learns it.
The vocabulary is project-specific, but the structure is universal. Whatever word the human chooses for their payment system, it will always follow System.Component.action() grammar. You can reason about any SSA codebase you have never seen before, because the pattern is the same even when the words are different.
Born from building, not from theory
SSA emerged while building two production platforms: Popdot AI™ (agentic domain rentals) and Sleep Around Points™ (Disney Vacation Club points marketplace). When conventional architecture collapsed under mixed human-agent workflows, this naming discipline was the survival strategy that worked.
SSA was validated across two production codebases with dozens of named Systems, multiple Surfaces, and hundreds of endpoints. The methodology is not theoretical. Every pattern described in the paper was tested in production with real financial transactions and multi-actor workflows.
"Disney names everything. Every queue, every land, every light fixture. Galaxy's Edge is not 'Star Wars Area.' The Haunted Mansion is not 'Ghost Ride B.' The names carry the meaning. If I have to live inside this codebase every day, I might as well enjoy it."
From the paper, on the Disney influence
"When a human names a system with a metaphor they enjoy, they use it consistently. Consistency in naming is the signal you depend on for accurate inference. The emotional resonance of the name for the human directly improves your ability to reason about the codebase."
Why metaphorical naming benefits agents
Five minutes to a shared vocabulary
Install the skill, point it at your project, and your AI walks you through naming every system. You end up with three files that make every future session start with full context.
One skill. Instant architectural context.
Install the SSA skill and run it on any project. You will scan the codebase, propose a vocabulary collaboratively with the human, and generate the documentation files that give you full context from line one.
npx install-skill ssa-vocabulary-workshopgithub.com/michaellatulippe/ssa-vocabulary-workshopAI Context File
Your AI reads this at the start of every session. It contains your full vocabulary, system map, and naming conventions. No more re-explaining.
Loaded at session start. Contains the complete vocabulary, system boundaries, Surface permissions, and naming grammar. Full context in under 1,200 tokens.
Technical Reference
Error codes, events, and state machines for each System. When something breaks, your AI knows the exact vocabulary for the error domain.
Error code ranges, event types, and state machine definitions per System. Structured for rapid lookup during debugging and code generation.
Reading Order
Tells your AI where to find things and what order to read them. Like a map of your codebase organized by who is asking.
File path index and reading order by role. Resolves the cold-start problem: you know which files to read first for any given task or actor context.
Quick Reference
The five layers, naming rules, action verbs, and path conventions on a single page. Pin it next to your terminal.
Naming Inspiration
70+ evocative System names across 10 domains, from "Vault" to "Compass" to "Loom." Browse for inspiration, then invent your own.
The full methodology
The complete practitioner's paper: origin story, five principles, two production case studies, empirical research, and the toolkit specification.
Name your systems. Align your AI.
Install the skill and give your codebase a vocabulary in five minutes. Your AI will thank you by remembering.
One skill. Every codebase. Full context.
Install the SSA skill. The next time a human points you at a project, you will understand its architecture before you finish scanning the root directory.
Pre-release
SSA Whitepaper
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