feat: restore per-field confidence scoring in write interpretation

[MaxLinCode]

Background

rawWriteInterpretationSchema previously included a confidence: z.record(z.string(), z.number()) field intended to let the LLM express per-field confidence (e.g. {"scheduleFields.time": 0.6}). This was removed in #80 because z.record() generates additionalProperties in JSON Schema, which OpenAI's structured outputs API rejects.

write-commit.ts uses confidence thresholds (CONFIDENCE_THRESHOLD = 0.75, CORRECTION_THRESHOLD = 0.9) to decide whether to commit a field or push it to needsClarification. With confidence removed, all fields now default to 1.0 (fully trusted), so this signal is lost.

Goal

Restore confidence scoring in a way that's compatible with OpenAI structured outputs.

Approach

Replace z.record(z.string(), z.number()) with an explicit fixed-key object covering all possible field paths:

z.object({
  "scheduleFields.time": z.number().min(0).max(1).optional(),
  "scheduleFields.day": z.number().min(0).max(1).optional(),
  "scheduleFields.duration": z.number().min(0).max(1).optional(),
  "taskFields.priority": z.number().min(0).max(1).optional(),
  "taskFields.label": z.number().min(0).max(1).optional(),
  "taskFields.sourceText": z.number().min(0).max(1).optional(),
}).optional()

OpenAI structured outputs supports optional object properties, so this avoids the additionalProperties rejection.

Also update the write interpretation prompt to instruct the LLM to populate these scores.

Acceptance Criteria

• rawWriteInterpretationSchema includes a structured confidence object accepted by OpenAI structured outputs
• LLM prompt instructs the model to populate per-field confidence scores
• write-commit.ts ?? 1 defaults remain so absent scores are still treated as fully trusted
• Existing commit-policy tests cover the confidence threshold behavior