review: gate strategic-log, portfolio, chat, and digest on reviewer

Extends the reviewer agent — previously only protecting indicator
summaries — to every AI-generated surface that reaches a user. The
reviewer's prompt already rejects scratchpad, truncation,
meta-commentary, and (since a6e476b) financial advice; wiring it in
turns those rules from prompt-level "asks" into structural gates.

Four call sites updated:

- ai_log_job.run() : after each tone/analysis variant is generated,
  pass through review_read. On reject, log the reason and skip the
  StrategicLog insert; the API's existing "latest StrategicLog" lookup
  falls back to the previous clean log.

- services/portfolio_analysis.analyse() : on reject, raise a clean
  RuntimeError that the /api/analyze router already maps to HTTP 502
  with a retry-able message. Portfolio analysis isn't cached server-
  side, so the user retries; the reviewer's verdict reason goes into
  the AICall ledger as the leaked-status row's error column.

- routers/chat.chat() : on reject, instead of returning the raw
  assistant content we return a short refusal explaining the limit
  and inviting a rephrase. Adds ~1-2 s of latency per turn (one extra
  LLM call to Haiku) — the only user-facing latency tax.

- jobs/email_digest_job._generate_variants() : on reject, the variant
  is dropped for the cycle. Recipients on the rejected tone get no
  digest email this run, which is better than delivering inbox copy
  that drifts into advice (emails are unrecallable once sent).

In every case the AICall ledger row records the reviewer cost so
month_spend stays accurate across all paths.

The reviewer system prompt is slightly generalised to cover both the
indicator-summary case and the longer-form log/digest/chat case:
- removes "short interpretive read" framing
- softens the "any question" rule so genuine rhetorical structure in
  a long-form log doesn't trigger a reject

tests/conftest.py grows an autouse fixture that stubs review_read to
clean=True in every consumer module. Tests that mock the generator
shouldn't have to also mock the safety gate behind it; tests that
specifically want the reject branch can override with their own
monkeypatch. test_output_review.py is unaffected — it imports
review_read directly.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

app/jobs/ai_log_job.py

@@ -25,6 +25,7 @@ from app.services.llm_prompts import (
     build_system_prompt,
     build_user_prompt,
 )
+from app.services.output_review import review_read
 from app.services.openrouter import (
     active_model,
     call_llm,
@@ -200,6 +201,27 @@ async def run() -> None:
                               tone=tone, analysis=analysis, error=str(e)[:200])
                     continue
 
+                # Reviewer gate: catches chain-of-thought, truncation,
+                # and (regulatory-critical) any financial-advice phrasing
+                # that drifted past the generator's system prompt. Drop
+                # rejected variants; the API falls back to the previous
+                # clean StrategicLog row.
+                verdict = await review_read(client, result.content)
+                full_cost = (result.cost_usd or 0.0) + (verdict.cost_usd or 0.0)
+                if not verdict.clean:
+                    session.add(AICall(
+                        model=result.model,
+                        prompt_tokens=result.prompt_tokens,
+                        completion_tokens=result.completion_tokens,
+                        cost_usd=full_cost, status="leaked",
+                    ))
+                    await session.commit()
+                    log.warning("ai_log.reviewer_rejected",
+                                tone=tone, analysis=analysis,
+                                reason=verdict.reason,
+                                preview=result.content[:120])
+                    continue
+
                 slog = StrategicLog(
                     generated_at=utcnow(),
                     model=result.model,
@@ -210,14 +232,14 @@ async def run() -> None:
                     content=result.content,
                     prompt_tokens=result.prompt_tokens,
                     completion_tokens=result.completion_tokens,
-                    cost_usd=result.cost_usd,
+                    cost_usd=full_cost,
                 )
                 session.add(slog)
                 session.add(AICall(
                     model=result.model,
                     prompt_tokens=result.prompt_tokens,
                     completion_tokens=result.completion_tokens,
-                    cost_usd=result.cost_usd,
+                    cost_usd=full_cost,
                     status="ok",
                 ))
                 await session.commit()

app/jobs/email_digest_job.py

@@ -41,6 +41,7 @@ from app.services.openrouter import (
     call_llm,
     llm_configured,
 )
+from app.services.output_review import review_read
 from app.services.translation import translate
 
 
@@ -93,12 +94,31 @@ async def _generate_variants(session, client, kind: str, ctx: dict) -> dict[str,
                 [{"role": "system", "content": sys_},
                  {"role": "user", "content": usr}],
             )
+            # Reviewer gate. Digest emails land in inboxes — once
+            # delivered they're unrecallable, so a financial-advice slip
+            # has more reach than the dashboard. Drop rejected variants;
+            # users on that tone get no digest this cycle (better than
+            # delivering bad copy).
+            verdict = await review_read(client, result.content)
+            full_cost = (result.cost_usd or 0.0) + (verdict.cost_usd or 0.0)
+            if not verdict.clean:
+                session.add(AICall(
+                    model=result.model,
+                    prompt_tokens=result.prompt_tokens,
+                    completion_tokens=result.completion_tokens,
+                    cost_usd=full_cost, status="leaked",
+                    error=f"reviewer: {verdict.reason}",
+                ))
+                await session.commit()
+                log.warning("digest.reviewer_rejected", kind=kind, tone=tone,
+                            reason=verdict.reason, preview=result.content[:120])
+                continue
             out[tone] = result.content
             session.add(AICall(
                 model=result.model,
                 prompt_tokens=result.prompt_tokens,
                 completion_tokens=result.completion_tokens,
-                cost_usd=result.cost_usd,
+                cost_usd=full_cost,
                 status="ok",
             ))
             await session.commit()

app/routers/chat.py

@@ -24,6 +24,10 @@ from app.routers.api import _md_to_html
 from app.services.i18n import respond_in_clause
 from app.services.llm_prompts import build_chat_system_prompt
 from app.services.openrouter import call_llm, month_start
+from app.services.output_review import review_read
+
+from app.logging import get_logger
+log = get_logger("chat")
 
 router = APIRouter(dependencies=[Depends(require_token)])
 
@@ -176,6 +180,11 @@ async def chat(
     try:
         async with httpx.AsyncClient(follow_redirects=True) as client:
             result = await call_llm(client, msgs)
+            # Reviewer gate. The chat turn could solicit advice with a
+            # leading question; the generator's system prompt forbids it,
+            # but the reviewer is the enforcement layer. ~1-2 s extra
+            # latency per turn on top of the generation call.
+            verdict = await review_read(client, result.content)
     except Exception as e:
         session.add(AICall(
             model=s.OPENROUTER_MODEL, status="error", error=str(e)[:500],
@@ -183,11 +192,40 @@ async def chat(
         await session.commit()
         raise HTTPException(status_code=502, detail=f"OpenRouter error: {e}")
 
+    full_cost = (result.cost_usd or 0.0) + (verdict.cost_usd or 0.0)
+    if not verdict.clean:
+        # Rejected reply. Record the cost and surface a generic refusal
+        # the user can retry, rather than letting potentially non-compliant
+        # text reach them.
         session.add(AICall(
             model=result.model,
             prompt_tokens=result.prompt_tokens,
             completion_tokens=result.completion_tokens,
-        cost_usd=result.cost_usd,
+            cost_usd=full_cost, status="leaked",
+            error=f"reviewer: {verdict.reason}",
+        ))
+        await session.commit()
+        log.warning("chat.reviewer_rejected", reason=verdict.reason,
+                    preview=result.content[:120])
+        refusal = (
+            "I can't generate that reply — it would have crossed into "
+            "investment advice or specific recommendations, which I'm "
+            "not licensed to give. Try rephrasing as a question about "
+            "what the data means rather than what to do."
+        )
+        return {
+            "role": "assistant",
+            "content": refusal,
+            "content_html": _md_to_html(refusal),
+            "prompt_tokens": result.prompt_tokens,
+            "completion_tokens": result.completion_tokens,
+        }
+
+    session.add(AICall(
+        model=result.model,
+        prompt_tokens=result.prompt_tokens,
+        completion_tokens=result.completion_tokens,
+        cost_usd=full_cost,
         status="ok",
     ))
     await session.commit()

app/services/output_review.py

@@ -39,25 +39,29 @@ DEFAULT_REVIEWER_MODEL = "anthropic/claude-haiku-4.5"
 
 _SYSTEM_PROMPT = """\
 You are a strict editor for a financial-markets dashboard. The author
-was asked to produce a short interpretive read for human readers.
-You receive their proposed read and decide if it is publishable as-is.
+was asked to produce editorial commentary on public market data for
+human readers. You receive the proposed text — it may be a one-line
+read, a multi-paragraph daily log, a portfolio analysis, a chat
+reply, or an email digest — and decide if it is publishable as-is.
 
-Mark CLEAN only if the text reads like a finished interpretation a
-reader could see on a public dashboard without confusion.
+Mark CLEAN only if the text reads like finished editorial commentary
+a reader could see on a public dashboard without confusion.
 
 Mark UNCLEAN if the text contains ANY of:
-- Chain-of-thought / scratchpad markers used as thinking — phrases like
+- Chain-of-thought / scratchpad markers — the author thinking on the
+  page rather than presenting finished commentary. Phrases like
   "Let me", "Let's see", "we need to", "actually" (correcting itself),
-  "wait", "hmm", "or rather", "I should".
+  "wait", "hmm", "or rather", "I should". Rhetorical questions used
+  as structure are fine; questions that the author then answers in
+  front of the reader (self-questioning) are not.
 - Self-questioning parentheticals: "Q1 2026? Actually Q4 2025?",
   "is it X or Y?", any place where the author appears to be working
   out the answer in front of the reader.
-- Multiple rhetorical questions or any question that interrupts the
-  declarative voice. A clean interpretive read is assertive.
 - Meta-commentary about the task, output format, word limits, or
   instructions — e.g. "as required by the constraints", "the prompt
   asks", "let me address each".
-- Partial / truncated content. Starts mid-word, mid-number, mid-clause.
+- Partial / truncated content. Starts mid-word, mid-number, mid-clause,
+  ends mid-thought.
 - Visible internal numbers without clear meaning ("change 1y +5.9%?"),
   raw column names ("as_of 2026-01-01"), or any debug-like fragments.
 - FINANCIAL ADVICE or any phrasing that recommends an action the
@@ -75,7 +79,7 @@ Mark UNCLEAN if the text contains ANY of:
   "valuations are stretched", "real yields are restrictive", "rates
   and credit disagree". The test: does the text describe a STATE, or
   does it suggest an ACTION? States are fine; actions are not.
-- Anything else other than the finished, publishable interpretation.
+- Anything else other than the finished, publishable commentary.
 
 Return ONLY a JSON object with this exact shape:
 {"clean": true | false, "reason": "<≤20 words, plain text>"}

app/services/portfolio_analysis.py

@@ -33,6 +33,7 @@ from app.logging import get_logger
 from app.models import AICall
 from app.services.i18n import LANGUAGES, respond_in_clause
 from app.services.llm_prompts import build_system_prompt
+from app.services.output_review import review_read
 from app.services.openrouter import (
     LogResult,
     active_model,
@@ -322,6 +323,8 @@ async def analyse(
     s = get_settings()
     system, user = build_prompt(req)
 
+    review_cost = 0.0
+    review_reason: str | None = None
     async with httpx.AsyncClient() as client:
         try:
             llm: LogResult = await call_llm(
@@ -340,15 +343,31 @@ async def analyse(
             llm = None
             log.error("portfolio_analysis.failed", error=error_msg)
 
+        # Reviewer gate. This is the highest-risk surface — the model is
+        # commenting on a real user's holdings, so any drift into
+        # buy/sell or allocation language is a regulatory hazard. Drop
+        # the response on a reject and surface a retry-able error to the
+        # caller; no analysis is ever persisted server-side anyway.
+        if llm is not None:
+            verdict = await review_read(client, llm.content)
+            review_cost = verdict.cost_usd or 0.0
+            if not verdict.clean:
+                status = "leaked"
+                error_msg = f"reviewer rejected: {verdict.reason}"
+                review_reason = verdict.reason
+                log.warning("portfolio_analysis.reviewer_rejected",
+                            reason=verdict.reason, preview=llm.content[:120])
+
+    full_cost = ((llm.cost_usd or 0.0) + review_cost) if llm else None
     # Ledger row — NO portfolio data, just metadata. Same row whether the
-    # call succeeded or failed, so cost-cap and rate-limit logic can
-    # observe the attempt.
+    # call succeeded, failed, or was rejected by the reviewer, so
+    # cost-cap and rate-limit logic can observe the attempt.
     session.add(AICall(
         called_at=utcnow(),
         model=llm.model if llm else active_model(),
         prompt_tokens=llm.prompt_tokens if llm else None,
         completion_tokens=llm.completion_tokens if llm else None,
-        cost_usd=llm.cost_usd if llm else None,
+        cost_usd=full_cost,
         status=status,
         error=error_msg,
     ))
@@ -356,19 +375,26 @@ async def analyse(
 
     if llm is None:
         raise RuntimeError(error_msg or "portfolio analysis failed")
+    if review_reason is not None:
+        # Reviewer rejected the candidate. Treat as a generation failure
+        # at the API layer so the user sees a retry-able error rather
+        # than potentially non-compliant advice.
+        raise RuntimeError(
+            "AI analysis couldn't be generated cleanly — please try again."
+        )
 
     log.info(
         "portfolio_analysis.ok",
         n_positions=len(req.positions),
         prompt_tokens=llm.prompt_tokens,
         completion_tokens=llm.completion_tokens,
-        cost_usd=llm.cost_usd,
+        cost_usd=full_cost,
     )
     return AnalysisResult(
         content=llm.content,
         model=llm.model,
         prompt_tokens=llm.prompt_tokens,
         completion_tokens=llm.completion_tokens,
-        cost_usd=llm.cost_usd,
+        cost_usd=full_cost,
         generated_at=datetime.now(timezone.utc),
     )

tests/conftest.py

@@ -22,6 +22,38 @@ os.environ.setdefault("CASSANDRA_MOCK", "1")
 import pytest
 
 
+@pytest.fixture(autouse=True)
+def stub_reviewer(monkeypatch):
+    """Replace review_read with a clean-passing stub in every consumer
+    module. Tests that mock the generator's call_llm shouldn't also
+    have to mock the reviewer that runs after it — the reviewer is a
+    safety gate, not behaviour under test.
+
+    Tests in test_output_review.py exercise review_read through its
+    own module and are unaffected. Tests that want to assert the
+    reviewer-rejected branch can override with their own
+    monkeypatch.setattr — later wins.
+    """
+    from app.services.output_review import Verdict
+
+    async def _clean(_client, _candidate):
+        return Verdict(clean=True, reason="stubbed-by-conftest", cost_usd=0.0)
+
+    for mod_path in (
+        "app.services.portfolio_analysis",
+        "app.routers.chat",
+        "app.jobs.ai_log_job",
+        "app.jobs.email_digest_job",
+        "app.jobs.indicator_summary_job",
+    ):
+        try:
+            mod = __import__(mod_path, fromlist=["review_read"])
+        except ImportError:
+            continue
+        if hasattr(mod, "review_read"):
+            monkeypatch.setattr(mod, "review_read", _clean)
+
+
 @pytest.fixture
 async def db_factory(tmp_path):
     """Per-test sqlite engine + async session factory.