feat: stream Reginald's replies token-by-token

The 26b is slow (~30s/call); the chat now shows the answer forming instead of
freezing until it's done. The final prose streams over SSE; tool-calling turns
stay structured (no partial tokens), so streaming kicks in for the narration.

core (@commitea/core):
- chat-client.complete gains an optional onToken — when set, it requests
  stream:true and parses the OpenAI SSE stream, emitting content deltas and
  assembling streamed tool-call argument fragments into the final result.
- GiteaHttpResponse exposes the optional `body` stream (real fetch has it; stubs
  don't). agent-loop threads onToken to each completion.

app:
- model:chat forwards each delta to the renderer (event.sender.send); preload
  exposes model.onToken(cb) → unsubscribe. useChat accumulates the live stream
  into a growing bubble (with a cursor), replaced by the authoritative final
  content when the turn resolves. Unconfigured → scripted reply, unchanged.

Verified: 118 core tests green (2 streaming: SSE content deltas + tool-call
fragment assembly), desktop typecheck clean, 14 fixture e2e green. Live: a real
turn against gemma-4-26b assembles the correct answer via the streaming path
(live-reginald green) — the reply now renders token-by-token.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

apps/desktop/src/preload/index.ts

@@ -25,6 +25,12 @@ const api = {
     status: () => ipcRenderer.invoke('model:status'),
     /** One agent turn: messages in, Reginald's prose + the tools it consulted out. */
     chat: (messages: unknown) => ipcRenderer.invoke('model:chat', messages),
+    /** Subscribe to streamed prose tokens for the in-flight turn; returns an unsubscribe. */
+    onToken: (cb: (delta: string) => void) => {
+      const listener = (_e: unknown, delta: string) => cb(delta)
+      ipcRenderer.on('model:chat:token', listener)
+      return () => ipcRenderer.removeListener('model:chat:token', listener)
+    },
     /** Decompose a braindump into a proposed issue set (capture_work). */
     capture: (braindump: string) => ipcRenderer.invoke('model:capture', braindump),
   },