Initial commit: CI Agent competitive-intelligence monitoring app

FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring,
source collection, LLM-based change analysis, enrichment, and account
security (Turnstile, escalating lockout, email verification).

apps/api/app/prompts/change_significance.py

@@ -0,0 +1,60 @@
+"""Task E: change significance narrative.
+
+Severity itself stays deterministic (app/change_detection/scoring.py) - see
+ARCHITECTURE.md: "the LLM narrates why a change matters; it does not decide
+how much it matters." This task supplies the narrative explanation and a
+second, independent opinion on whether the change looks real/meaningful and
+notification-worthy, which the notification layer (Phase 8) can use as an
+extra signal alongside the deterministic severity - it never overrides it.
+"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+from app.analysis.llm.base import LLMProvider
+from app.prompts.base import build_user_prompt
+
+SYSTEM_PROMPT = (
+    "You are a competitive intelligence analyst reviewing one detected change between two "
+    "snapshots of a company's public information. Explain in plain language why this change "
+    "would or wouldn't matter to someone monitoring this company, given their stated focus. "
+    "Be honest about uncertainty - do not overstate a routine wording tweak as significant, "
+    "and do not undersell a genuine signal."
+)
+
+
+class ChangeSignificanceAssessment(BaseModel):
+    is_real_change: bool = Field(description="Does this look like a genuine change, not noise?")
+    is_meaningful: bool
+    why_it_matters: str
+    confidence: float = Field(ge=0.0, le=1.0)
+    should_notify: bool
+
+
+async def assess_change_significance(
+    llm: LLMProvider,
+    *,
+    company_name: str,
+    monitoring_focus: str | None,
+    change_type: str,
+    change_summary: str,
+    added_text: list[str],
+    removed_text: list[str],
+    deterministic_severity: str,
+    deterministic_confidence: float,
+) -> ChangeSignificanceAssessment:
+    evidence = {
+        "company_name": company_name,
+        "monitoring_focus": monitoring_focus,
+        "change_type": change_type,
+        "change_summary": change_summary,
+        "text_added": added_text[:20],
+        "text_removed": removed_text[:20],
+        "deterministic_severity": deterministic_severity,
+        "deterministic_confidence": deterministic_confidence,
+    }
+    user_prompt = build_user_prompt(
+        "Assess this detected change and explain why it does or doesn't matter.", evidence
+    )
+    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, ChangeSignificanceAssessment)