## Why
The TUI can run against a remote app server, but several high-traffic
settings still persisted by editing the local config file. That sends
remote sessions' preference writes to the wrong machine and lets local
disk state drift from the app-server-owned config.
This is **[1 of 4]** in a stacked series that moves TUI-owned config
mutations onto app-server APIs.
## What changed
- Added a small TUI helper for typed app-server config writes.
- Routed primary interactive preference writes through
`config/batchWrite`.
- Preserved existing profile scoping for settings that already support
`profiles.<profile>.*` overrides.
## Config keys affected
- `model`
- `model_reasoning_effort`
- `personality`
- `service_tier`
- `plan_mode_reasoning_effort`
- `approvals_reviewer`
- `notice.fast_default_opt_out`
- Profile-scoped equivalents under `profiles.<profile>.*`
## Suggested manual validation
- Connect the TUI to a remote app server, change `model` and
`model_reasoning_effort`, reconnect, and confirm the remote config
retained both values while the local `config.toml` did not change.
- Change `personality`, `plan_mode_reasoning_effort`, and the explicit
auto-review selection, then reconnect and confirm those choices persist
through the app server.
- Clear the service tier back to default and confirm `service_tier` is
cleared while `notice.fast_default_opt_out = true` is persisted
remotely.
- Repeat one setting change with an active profile and confirm the write
lands under `profiles.<profile>.*`.
## Stack
1. [#22913](https://github.com/openai/codex/pull/22913) `[1 of 4]`
primary settings writes
2. [#22914](https://github.com/openai/codex/pull/22914) `[2 of 4]` app
and skill enablement
3. [#22915](https://github.com/openai/codex/pull/22915) `[3 of 4]`
feature and memory toggles
4. [#22916](https://github.com/openai/codex/pull/22916) `[4 of 4]`
startup and onboarding bookkeeping
## Why
The `spawn_agent` model override guidance is uncapped and bloating
context. We need to trim down each entry and cap total entries.
picked 5 as cap, we can change
## What changed
- Cap the model override summaries shown in `spawn_agent` to the first 5
picker-visible models, preserving the existing priority ordering from
the models manager.
- Condense each rendered entry to the actionable pieces the model needs:
- use the model slug as the label
- render compact reasoning effort lists with the default marked inline
- render only service tier IDs, and omit the clause when no tiers are
available
- Update coverage so the compact formatter shape and the top-5 cap are
exercised, and keep the end-to-end request assertion aligned with real
model metadata.
## Example
Before:
`- gpt-5.4 ('gpt-5.4\'): Strong model for everyday coding. Default
reasoning effort: medium. Supported reasoning efforts: low (Fast
responses with lighter reasoning), medium (Balances speed and reasoning
depth for everyday tasks), high (Greater reasoning depth for complex
problems), xhigh (Extra high reasoning depth for complex problems).
Supported service tiers: priority (Fast: 1.5x speed, increased usage).`
After:
`- 'gpt-5.4': Strong model for everyday coding. Reasoning efforts: low,
medium (default), high, xhigh. Service tiers: priority.`
