strip prompt-echo leakage in indicator summaries

DeepSeek occasionally regurgitates the system prompt verbatim
("Constraints: ≤60 words...", "Example good: ..."). Three-pronged fix:

1. Removed the inline good/bad example blocks from the per-group and
   aggregate system prompts — DeepSeek was treating them as templates
   to copy. The hard constraints alone are clear enough.

2. Expanded the LEAK_PATTERNS list to catch the prompt-label echoes
   that still occasionally slip through ("Key observations:", "The
   indicators are:", "Must cite ...", "Should give ...", bare "Key:").
   Cleanup now runs up to 6 passes for compound leakage.

3. Added looks_like_leakage() — if the cleaned output still contains
   tell-tale phrases ("≤60 words", "instructions:", etc.), the summary
   is skipped rather than persisted. Logs a 'leakage_detected' warning
   and an ai_calls row with status=leaked so we can see the failure
   rate over time. The previous good summary stays visible.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/jobs/indicator_summary_job.py

@@ -44,15 +44,63 @@ _LEAK_PATTERNS = [
         r"^based on[^.]*\.\s*",
         r"^to (?:address|answer|write|summarise|summarize)[^.]*\.\s*",
         r"^first[,]?\s[^.]*\.\s*",
-        r"^the (?:user|data shows|reader|task|request)[^.]*\.\s*",
+        r"^the (?:user|data shows|reader|task|request|reader sees|instructions?)[^.]*\.\s*",
         r"^summary[:.]\s*",
+        r"^key\s*[:\-—]\s*",
+        r"^must\s+(?:be|cite|explain|avoid|give|stay|provide)[^.]*\.\s*",
+        r"^should\s+(?:be|give|cite|explain|avoid|provide)[^.]*\.\s*",
+        r"^avoid[^.]*\.\s*",
+        r"^cite\s+at\s+most[^.]*\.\s*",
+        r"^be\s+(?:speculative|specific|concise|brief)[^.]*\.\s*",
+        r"^stay\s+on[^.]*\.\s*",
         r"^okay[,]?\s+",
         r"^alright[,]?\s+",
         r"^thinking[^.]*\.\s*",
+        # Prompt-leak prefixes — the model echoes example framing or rule
+        # headers from the system prompt.
+        r"^(?:good|bad|positive|negative)\s+example\s*[:\-—]\s*",
+        r"^example\s+(?:good|bad)\s*[:\-—]\s*",
+        r"^example\s*[:\-—]\s*",
+        r"^reference\s+style\s*[:\-—]\s*",
+        # Prompt label echoes (markdown-style or plain-text)
+        r"^(?:hard\s+)?constraints?\s*[:\-—][^.\n]*[.\n]\s*",
+        r"^key\s+observations?\s*[:\-—]\s*",
+        r"^observations?\s*[:\-—]\s*",
+        r"^focus\s+on[^.]*\.\s*",
+        r"^output\s+the\s+read[^.]*\.\s*",
+        r"^plain\s+prose[^.]*\.\s*",
+        r"^the\s+indicators?[^.]*\.\s*",   # "The indicators include..." / "The indicators are..."
+        r"^indicators?\s*[:\-—]\s*",
+        r"^data\s*[:\-—]\s*",
+        r"^analysis\s*[:\-—]\s*",
+        r"^interpretation\s*[:\-—]\s*",
+        r"^read\s*[:\-—]\s*",
+        r"^note\s*[:\-—]\s*",
+        # Sometimes the response gets wrapped in literal quotes
+        r"^[\"“'`]+",
     )
 ]
 
 
+_TRAILING_QUOTE = re.compile(r"[\"”'`]+\s*$")
+
+# Tell-tale phrases that mean the model regurgitated the prompt as its
+# "answer" — we'd rather show nothing than show this.
+_LEAKAGE_FLAGS = (
+    "≤60 words", "60 words", "must be under", "must cite", "must explain",
+    "no meta-commentary", "no buy/sell", "horizon. ", "1-day moves",
+    "the instructions are", "instructions:", "constraints:", "hard constraints",
+    "good example", "bad example", "reference style",
+)
+
+
+def looks_like_leakage(text: str) -> bool:
+    """Heuristic: after cleaning, if these phrases still appear, the output
+    is contaminated prompt-regurgitation and shouldn't be shown."""
+    low = text.lower()
+    return any(flag in low for flag in _LEAKAGE_FLAGS)
+
+
 def clean_summary(text: str) -> str:
     """Strip leading meta-commentary. If cleaning removes nearly everything
     (suggesting the model emitted reasoning then ran out of tokens), fall
@@ -60,7 +108,9 @@ def clean_summary(text: str) -> str:
     where the actual answer ended up."""
     raw = text.strip()
     out = raw
-    for _ in range(2):
+    # Up to 6 passes: handles compound leakage like
+    # "Constraints: <...>. The indicators are: <...>. <actual answer>"
+    for _ in range(6):
         before = out
         for pat in _LEAK_PATTERNS:
             out = pat.sub("", out, count=1).lstrip()
@@ -71,6 +121,15 @@ def clean_summary(text: str) -> str:
         paragraphs = [p.strip() for p in re.split(r"\n\s*\n", raw) if p.strip()]
         if paragraphs:
             out = paragraphs[-1]
+            # Re-strip leaders from the recovered paragraph too.
+            for _ in range(2):
+                before = out
+                for pat in _LEAK_PATTERNS:
+                    out = pat.sub("", out, count=1).lstrip()
+                if out == before:
+                    break
+    # Trim any orphan closing quote/backtick from the wrap-strip above.
+    out = _TRAILING_QUOTE.sub("", out).rstrip()
     return out
 
 
@@ -128,6 +187,22 @@ async def _generate_one(
         log.warning("ind_summary.failed", group=group, error=str(e)[:120])
         return False
 
+    cleaned = clean_summary(result.content)
+    if looks_like_leakage(cleaned) or len(cleaned) < 40:
+        # Model regurgitated the prompt or produced nothing usable.
+        # Don't persist — keep the last good summary visible. Log it so
+        # we can see the rate of failures over time.
+        log.warning("ind_summary.leakage_detected",
+                    group=group, preview=cleaned[:120])
+        session.add(AICall(
+            model=result.model,
+            prompt_tokens=result.prompt_tokens,
+            completion_tokens=result.completion_tokens,
+            cost_usd=result.cost_usd,
+            status="leaked",
+        ))
+        return False
+
     session.add(IndicatorSummary(
         group_name=group,
         generated_at=utcnow(),
@@ -135,7 +210,7 @@ async def _generate_one(
         tone=tone,
         analysis=analysis,
         prompt_version=PROMPT_VERSION,
-        content=clean_summary(result.content),
+        content=cleaned,
         prompt_tokens=result.prompt_tokens,
         completion_tokens=result.completion_tokens,
         cost_usd=result.cost_usd,