v1.2.0:6 — AI "generate today's workout" from a brain-dump

Add a single-session AI flow alongside program generation: describe a
workout in plain words and get a ready-to-log workout back — exercises
with suggested weights, target reps, and set counts grounded in the
user's recent history. The suggestion can be inline-edited or refined
by sending a follow-up instruction back to the model, then "Use this
workout" pre-fills the normal New Workout form (nothing persists until
the user saves through the regular path).

Why reuse, not fork: the existing program-generation spine (detached
background runner, SSE streaming, lenient-JSON preview, 5 providers,
history context, library name->id mapping) already does the hard parts.
A new AIGeneration.kind discriminant ("program" | "workout", default
"program" via boot-time guarded ALTER) selects the parser and keeps the
ephemeral workout rows out of the program-shaped AI history. Refine is a
fresh generation seeded with the prior suggestion (validated through the
same schema before it re-enters the prompt).

Hand-off is sessionStorage -> /main/workouts/new?from=ai -> AiWorkoutPrefill,
which expands each suggestion into N sets and maps effort by cardio-ness
(Gear for cardio, RPE for strength). EditWorkoutData.id is now optional so
the prefill CREATEs rather than PATCHing a nonexistent id. The AI suggests
each weight in that exercise's effective logging unit (the library JSON
carries a per-exercise unit) so the stored number and unit never diverge.

Built + sideloaded to immense-voyage.local as 1.2.0:6; on-box ALTER and
non-root launch confirmed via start-cli. tsc clean (app + packaging),
251 tests pass, next build + s9pk build succeed.

docs/guides/ai-subsystem.md

@@ -20,6 +20,29 @@ generate/generations route handlers). Whole-repo rules live in `AGENTS.md`.
 - Multi-config: `AIConfigProfile` rows per user; `UserPreferences.activeAIConfigId`
   points at the active one and is mirrored into the legacy `ai*` columns for back-compat.
 
+## Two generation kinds (`AIGeneration.kind`)
+
+The runner spine is shared by two output shapes, discriminated by `AIGeneration.kind`
+("program" | "workout", default "program"). The runner picks the parser by kind and
+stores the JSON in the (reused) `parsedProgram` column.
+
+- **program** (`kind: 'program'`) — `generate/route.ts` → `programSchema.ts`
+  (`PROGRAM_OUTPUT_SHAPE` / `parseAIProgram`). Applied to DB rows via `apply.ts`.
+  Shown in AI · History (which filters `kind: 'program'`).
+- **workout** (`kind: 'workout'`) — `generate-workout/route.ts` (uses
+  `workoutPrompt.ts` + `workoutSchema.ts`: `WORKOUT_OUTPUT_SHAPE` / `parseAIWorkout`).
+  A single day's session. **No server-side apply**: the client (`GenerateWorkoutClient.tsx`)
+  stashes the reviewed suggestion in `sessionStorage` and routes to
+  `/main/workouts/new?from=ai`, where `AiWorkoutPrefill.tsx` expands it (via
+  `workoutDraft.ts::buildPrefillExercises`) and pre-fills the normal `WorkoutForm` —
+  nothing persists until the user saves through the regular workout path.
+  **Refine = a new workout generation** seeded with the prior suggestion JSON
+  (`priorWorkout` in the route body → REVISION mode in `workoutPrompt.ts`). These rows
+  are ephemeral, so they're excluded from the program-shaped AI · History.
+- Adding a new kind: extend the union in `KickoffOpts`, add a parser + output-shape,
+  branch the parser selection in `generationRunner.ts`, and decide whether it belongs in
+  History (filtered by kind).
+
 ## Provider abstraction
 
 - Each provider yields an async iterable of `GenerateChunk` (`text` / `usage` / `done` /