Add evaluated Curated writer and reviewer profiles

README.md

@@ -131,14 +131,31 @@ Feature-complete across the planned phases:
   change Curated state. Ask for `preview_curated` with `developing_stories=true` to inspect the
   bounded story-memory path. It follows confirmed sent reports across source updates, records
   explicit publish/update/combine/hold/skip decisions, and stays disabled until
-  `CURATED_DEVELOPING_STORIES=true`. Story drafts and the one allowed revision have a fixed 4,096
+  `CURATED_DEVELOPING_STORIES=true`. With the default `CURATED_REQUEST_PROFILE=legacy`, story
+  drafts and the one allowed revision have a fixed 4,096
   token output ceiling; legacy Curated drafts and reviews keep the 900-token ceiling, and every
   call spends from the same Curated daily token and dollar budgets. Set `CURATED_CHANNEL_ID` only
   after reviewing preview quality. See
   [ADR-0012](docs/decisions/0012-scout-is-the-item-source.md) for the Scout boundary. Scout currently
   fetches only newly reported items; [Scout #21](https://github.com/R055LE/scout/issues/21) tracks
   same-page refresh evidence.
 
+  `CURATED_REQUEST_PROFILE=sol-astra-v1` requires developing stories and selects the evaluated
+  Sol 20260929 writer (16,384 output tokens, 1,200-second timeout) and Astra 20260903 reviewer
+  (8,192 output tokens, 900-second timeout). Both use OpenAI, default service tier, medium
+  reasoning, no temperature, and one transport attempt with no provider fallback. Response
+  caching is disabled; prompt caching uses explicit mode with no cache markers. Unknown cache
+  usage stays unknown. Reported cache activity, a mismatched route, or an unusable response
+  prevents publication; trustworthy usage is recorded even on rejected responses. Missing usage
+  and transport failures are recorded as uncertain, without retaining response text. Selected
+  profiles reject legacy Curated calls and do not automatically replay uncertain calls.
+
+  The scheduled job keeps its 30-minute overall lease and deadline, which can interrupt the
+  four-call workflow before all per-call timeouts elapse. The selector defaults to `legacy` and
+  does not change deployed channels, schedules, or budgets. Existing budget checks compare
+  already-reported usage, so they are not hard spend reservations; [#117](https://github.com/R055LE/roger/issues/117)
+  tracks reservation enforcement before activating this profile in production.
+
 Runs as a non-root, read-only-rootfs container. More than 300 tests cover the guard rules, the tool loop
 (including channel creation with access presets and the confirm-gated edit, post, and reorder
 tools), the rate limiter, and the Scout ingestion path.

compose.yaml

@@ -39,6 +39,7 @@ services:
       MODEL_GIGABRAIN: ${MODEL_GIGABRAIN:-}
       MODEL_CURATED: ${MODEL_CURATED:-}
       MODEL_CURATED_REVIEW: ${MODEL_CURATED_REVIEW:-}
+      CURATED_REQUEST_PROFILE: ${CURATED_REQUEST_PROFILE:-legacy}
       DAILY_TOKENS_ADMIN: ${DAILY_TOKENS_ADMIN:-150000}
       DAILY_TOKENS_AMBIENT: ${DAILY_TOKENS_AMBIENT:-40000}
       DAILY_TOKENS_GIGABRAIN: ${DAILY_TOKENS_GIGABRAIN:-100000}

roger.env.example

@@ -26,6 +26,8 @@ MODEL_GIGABRAIN=anthropic/claude-opus-4.5,openai/gpt-5,google/gemini-2.5-pro
 MODEL_CURATED=google/gemini-2.5-flash-lite,anthropic/claude-haiku-4.5
 # independent fact/excerpt review; no Curated draft can post when this is blank
 MODEL_CURATED_REVIEW=google/gemini-2.5-pro
+# Opt-in story profiles; requires CURATED_DEVELOPING_STORIES=true.
+CURATED_REQUEST_PROFILE=legacy
 
 # --- budgets (daily in+out tokens per brain) ---
 DAILY_TOKENS_ADMIN=150000

roger/brains/curated.py

@@ -19,7 +19,7 @@
 from openai import OpenAIError
 
 from roger.identity import ROGER_IDENTITY
-from roger.llm import LLM, BudgetExceeded, LLMConfigError
+from roger.llm import LLM, BudgetExceeded, CuratedModelError, LLMConfigError
 from roger.scout_source import collect_from_scout
 from roger.store import CURATED_CHECK_LEASE_SECONDS, Store
 
@@ -555,7 +555,13 @@ async def finish(status: str, *, retry_at: float = 0) -> None:
         except LLMConfigError:
             await finish("configuration")
             return await result("curated brain not configured")
+        except CuratedModelError as exc:
+            await finish("rejected")
+            return await result(f"model response rejected: {exc.status}; skipped")
         except (OpenAIError, TimeoutError):
+            if getattr(settings, "curated_request_profile", "legacy") != "legacy":
+                await finish("rejected")
+                return await result("model request interrupted; usage uncertain; skipped")
             log.exception("curated model request failed")
             await finish("transient", retry_at=clock().timestamp() + 3600)
             return await result("model request failed; skipped")
@@ -684,6 +690,8 @@ async def preview_curated_job(
             return {"status": "budget exceeded; skipped"}
         except LLMConfigError:
             return {"status": "curated brain not configured"}
+        except CuratedModelError as exc:
+            return {"status": f"model response rejected: {exc.status}; skipped"}
         except OpenAIError:
             log.exception("developing story preview model request failed")
             return {"status": "model request failed; skipped"}

roger/brains/stories.py

@@ -1050,6 +1050,9 @@ def input_fingerprint(
         DECISION_RESPONSE_FORMAT,
         REVIEW_RESPONSE_FORMAT,
     ]
+    profile = getattr(settings, "curated_request_profile", "legacy")
+    if profile != "legacy":
+        payload.append({"request_profile": profile})
     return hashlib.sha256(
         json.dumps(payload, sort_keys=True, ensure_ascii=False).encode()
     ).hexdigest()

roger/config.py

@@ -7,11 +7,14 @@
 from __future__ import annotations
 
 import pathlib
+from typing import Literal
 from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
 
-from pydantic import Field, field_validator
+from pydantic import Field, field_validator, model_validator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
+from roger.curated_protocol import CURATED_REVIEWER, CURATED_WRITER
+
 
 def _split_csv(value: str) -> list[str]:
     return [item.strip() for item in value.split(",") if item.strip()]
@@ -40,6 +43,7 @@ class Settings(BaseSettings):
     model_gigabrain: str = ""
     model_curated: str = ""
     model_curated_review: str = ""
+    curated_request_profile: Literal["legacy", "sol-astra-v1"] = "legacy"
 
     # --- budgets (daily in+out tokens per brain) ---
     daily_tokens_admin: int = Field(default=150_000, gt=0)
@@ -137,6 +141,12 @@ def _timezone_must_be_loadable(cls, value: str) -> str:
             raise ValueError("must name a loadable IANA timezone") from exc
         return value
 
+    @model_validator(mode="after")
+    def _curated_profile_requires_stories(self) -> Settings:
+        if self.curated_request_profile != "legacy" and not self.curated_developing_stories:
+            raise ValueError("CURATED_REQUEST_PROFILE requires CURATED_DEVELOPING_STORIES")
+        return self
+
     @property
     def admin_models(self) -> list[str]:
         return _split_csv(self.model_admin)
@@ -151,10 +161,14 @@ def gigabrain_models(self) -> list[str]:
 
     @property
     def curated_models(self) -> list[str]:
+        if self.curated_request_profile != "legacy":
+            return [CURATED_WRITER.model]
         return _split_csv(self.model_curated)
 
     @property
     def curated_review_models(self) -> list[str]:
+        if self.curated_request_profile != "legacy":
+            return [CURATED_REVIEWER.model]
         return _split_csv(self.model_curated_review)
 
     @property