* feat: identify clients via X-Client-Platform/Version/OS
Adds client identification headers (and matching WS query params) across
all first-party clients so the server can split logs/metrics/gating by
caller without parsing User-Agent.
- HTTP: X-Client-Platform, X-Client-Version, X-Client-OS
- WS: client_platform, client_version, client_os query params
- Platform ∈ {web, desktop, cli, daemon}; OS ∈ {macos, windows, linux}
Wired through the shared TS ApiClient/WSClient via a new identity option
on CoreProvider. Web reads its version from package.json/env; Desktop
captures version + OS synchronously in preload via sendSync IPC. Go CLI
and daemon clients populate the same headers using runtime.GOOS
(normalized darwin → macos).
Server-side adds a ClientMetadata middleware that stashes the headers in
request context; the request logger and logger.RequestAttrs surface them
on every access log and handler-level log. Realtime hub logs the same
fields on websocket connect.
CORS allowlist extended for the new headers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test: address client-identity PR nits
- Memoize the CoreProvider identity object on Web and Desktop, and key
WSProvider's effect on identity primitives instead of the object
reference, so unrelated parent re-renders no longer tear down and
reconnect the WebSocket.
- Add direct header-injection tests for the CLI and daemon Go HTTP
clients (X-Client-Platform/Version/OS) and a normalizeGOOS unit test
on both packages.
- Add a TS test for WSClient that asserts client_platform/client_version/
client_os land on the upgrade URL and never leak the auth token.
- Add a hub test that dials the WS endpoint with client_* query params
and asserts the "websocket connected" log entry surfaces them as
structured attributes.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* feat(autopilot): add scheduled/triggered automation for AI agents
Introduce the Autopilot feature — recurring automations that assign work
to AI agents on a schedule or manual trigger. Supports two execution
modes: create_issue (creates an issue for the agent to work on) and
run_only (directly enqueues an agent task without issue pollution).
Backend: migration (3 tables + 2 columns), sqlc queries, AutopilotService
with concurrency policies (skip/queue/replace), HTTP CRUD + trigger
endpoints, background cron scheduler (30s tick), event listeners for
issue→run and task→run status sync.
Frontend: types, API client methods, TanStack Query hooks with optimistic
mutations, realtime cache invalidation, list page with create dialog,
detail page with trigger management and run history, sidebar nav + routes
for both web and desktop apps.
* feat(autopilot): improve UX — trigger config, edit dialog, template gallery
- Replace raw cron input with friendly frequency tabs (Hourly/Daily/Weekdays/Weekly/Custom), time picker, and timezone dropdown defaulting to user's local timezone
- Fix Select components showing UUIDs instead of names (Base UI render function pattern)
- Add Edit button on detail page opening a unified edit dialog
- Remove project/concurrency/issue-title-template from create/edit (simplify for users)
- Add trigger configuration inline during autopilot creation
- Add template gallery on empty state (6 step-by-step workflow templates)
- Rename "Description" to "Prompt" throughout UI
- Inject autopilot run timestamp into issue description for agent date awareness
- Treat issue status "in_review" as run completion (fixes skip on next trigger)
- Make migration idempotent with IF NOT EXISTS clauses
