feat(auth): sign in with a ChatGPT subscription for inference

Add an OAuth-based path to run Strix on a user's ChatGPT Plus/Pro
subscription instead of a metered API key, modeled on OpenAI's Codex CLI.

Auth:
- strix/auth: Codex OAuth login (authorization-code + PKCE), a 0600 token
  store, refresh-on-expiry, and an AsyncOpenAI client that routes inference
  through the ChatGPT backend (chatgpt.com/backend-api/codex) with a
  per-request auth hook so long scans survive token expiry.
- `strix auth login|logout|status` CLI (browser loopback on :1455 with a
  manual-paste fallback); STRIX_AUTH_MODE=subscription persisted to config.

Inference wiring:
- Subscription branch in configure_sdk_model_defaults installs the Codex
  client and the Responses API.
- _CodexResponsesModel always streams (the backend rejects non-streamed
  requests) and aggregates back for the non-streaming get_response path.
- store=false + encrypted reasoning for the stateless backend; models
  coerced to plan-available names (default gpt-5.4 — 5.5+ apply stricter
  content moderation that interferes with security testing).

UX / reporting:
- Track tokens but report $0.00 in the TUI, completion panel, and web
  viewer run details; record auth_mode in run.json and PostHog/Scarf.
- Graceful, actionable errors for unavailable models and expired sign-in.
- Restyled OAuth callback page (Strix branding + link to strix.ai).

Tests: PKCE/URL/redirect parsing, token refresh + account-id, streaming
aggregation, cost zeroing, CLI routing/provider aliasing.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/test_codex_streaming.py

@@ -0,0 +1,138 @@
+"""Regression test for the ChatGPT Codex backend's streaming requirement.
+
+The backend rejects non-streamed requests with ``{"detail": "Stream must be set
+to true"}``. ``_CodexResponsesModel`` must therefore issue a streamed request
+even from the non-streaming ``get_response`` path and aggregate the events into
+a single response. A local server that mimics that behaviour proves the wrapper
+works where the stock responses model would fail.
+"""
+
+from __future__ import annotations
+
+import json
+import threading
+from http.server import BaseHTTPRequestHandler, HTTPServer
+from typing import TYPE_CHECKING, Any
+
+import pytest
+from agents.model_settings import ModelSettings
+from agents.models.interface import ModelTracing
+from agents.models.openai_responses import OpenAIResponsesModel
+from openai import AsyncOpenAI, BadRequestError
+
+from strix.config.models import _CodexResponsesModel
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+
+def _response_payload() -> dict[str, Any]:
+    return {
+        "id": "resp_1",
+        "object": "response",
+        "created_at": 0,
+        "status": "completed",
+        "model": "gpt-5.5",
+        "output": [
+            {
+                "type": "message",
+                "id": "m1",
+                "status": "completed",
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": "OK", "annotations": []}],
+            }
+        ],
+        "usage": {
+            "input_tokens": 1,
+            "output_tokens": 1,
+            "total_tokens": 2,
+            "input_tokens_details": {"cached_tokens": 0},
+            "output_tokens_details": {"reasoning_tokens": 0},
+        },
+        "parallel_tool_calls": False,
+        "tool_choice": "auto",
+        "tools": [],
+        "metadata": {},
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "error": None,
+        "incomplete_details": None,
+        "instructions": None,
+        "max_output_tokens": None,
+    }
+
+
+class _Handler(BaseHTTPRequestHandler):
+    def log_message(self, *args: Any) -> None:
+        pass
+
+    def do_POST(self) -> None:
+        length = int(self.headers.get("Content-Length", 0))
+        body = json.loads(self.rfile.read(length) or b"{}")
+        if not body.get("stream"):
+            payload = json.dumps({"detail": "Stream must be set to true"}).encode()
+            self.send_response(400)
+            self.send_header("Content-Type", "application/json")
+            self.send_header("Content-Length", str(len(payload)))
+            self.end_headers()
+            self.wfile.write(payload)
+            return
+        event = {
+            "type": "response.completed",
+            "sequence_number": 0,
+            "response": _response_payload(),
+        }
+        frame = f"event: response.completed\ndata: {json.dumps(event)}\n\n".encode()
+        self.send_response(200)
+        self.send_header("Content-Type", "text/event-stream")
+        self.end_headers()
+        self.wfile.write(frame)
+
+
+@pytest.fixture
+def backend_url() -> Iterator[str]:
+    server = HTTPServer(("127.0.0.1", 0), _Handler)
+    thread = threading.Thread(target=server.serve_forever, daemon=True)
+    thread.start()
+    try:
+        yield f"http://127.0.0.1:{server.server_address[1]}/backend-api/codex"
+    finally:
+        server.shutdown()
+        server.server_close()
+
+
+def _client(base_url: str) -> AsyncOpenAI:
+    return AsyncOpenAI(api_key="tok", base_url=base_url)
+
+
+def _call_kwargs() -> dict[str, Any]:
+    return {
+        "system_instructions": "s",
+        "input": "hi",
+        "model_settings": ModelSettings(
+            store=False, response_include=["reasoning.encrypted_content"]
+        ),
+        "tools": [],
+        "output_schema": None,
+        "handoffs": [],
+        "tracing": ModelTracing.DISABLED,
+        "previous_response_id": None,
+        "conversation_id": None,
+        "prompt": None,
+    }
+
+
+@pytest.mark.asyncio
+async def test_stock_model_fails_on_non_streamed_backend(backend_url: str) -> None:
+    model = OpenAIResponsesModel(model="gpt-5.5", openai_client=_client(backend_url))
+    with pytest.raises(BadRequestError, match="Stream must be set to true"):
+        await model.get_response(**_call_kwargs())
+
+
+@pytest.mark.asyncio
+async def test_codex_model_streams_and_aggregates(backend_url: str) -> None:
+    model = _CodexResponsesModel(model="gpt-5.5", openai_client=_client(backend_url))
+    response = await model.get_response(**_call_kwargs())
+    assert response.output[0].content[0].text == "OK"
+    assert response.usage.total_tokens == 2