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/alert_summarization.py

@@ -0,0 +1,46 @@
+"""Task F: alert summarization. Produces the concise, non-exaggerated text
+shown in the alert/email/SMS (Phase 8) - short by design, honest about
+confidence, no hype language.
+"""
+
+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 write concise, factual alert summaries for a competitive intelligence tool. Never "
+    "exaggerate. State what changed, cite the evidence type, and note the confidence level "
+    "plainly. The title must be under 100 characters and must not use hype words like "
+    "'huge', 'massive', or 'game-changing'."
+)
+
+
+class AlertSummary(BaseModel):
+    title: str = Field(max_length=100)
+    summary: str
+    why_it_matters: str
+
+
+async def summarize_alert(
+    llm: LLMProvider,
+    *,
+    company_name: str,
+    change_type: str,
+    change_summary: str,
+    severity: str,
+    confidence: float,
+    evidence_snippets: list[str],
+) -> AlertSummary:
+    evidence = {
+        "company_name": company_name,
+        "change_type": change_type,
+        "change_summary": change_summary,
+        "severity": severity,
+        "confidence": confidence,
+        "evidence_snippets": evidence_snippets[:10],
+    }
+    user_prompt = build_user_prompt("Write a concise alert summary for this change.", evidence)
+    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, AlertSummary)