Initial commit: Conflict Checker

Cross-discipline design-contradiction checker for construction drawing
sets. Standalone tool broken out from Iron_Bid; a pipeline stage may
later fold back into Iron_Bid.

Pipeline: PDF->images -> per-sheet assertion extraction -> deterministic
clustering by location -> per-cluster reasoning -> report.
Includes CLI (cli/run_check.py) and web UI (backend/main.py).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/pipeline/risk.py

@@ -0,0 +1,63 @@
+"""
+risk.py - Stage 10: risk scoring and prioritization (LLM, text-only).
+
+Scores each validated issue 1-100, annotates it with risk_score,
+recommended_priority and risk_drivers, and returns the list sorted
+highest-risk first. Uses TEXT_MODEL (defaults to MODEL). On failure it falls
+back to a deterministic severity-based ordering so the pipeline still produces
+a prioritized list.
+"""
+
+from typing import Dict, List
+
+from backend import config
+from backend.pipeline._serialize import dumps
+from backend.pipeline._stage import call_stage
+from backend.prompts import RISK_SYSTEM_PROMPT, RISK_USER_INSTRUCTION
+
+_SEV_RANK = {"critical": 90, "high": 70, "medium": 40, "low": 15}
+
+
+def _ensure_ids(issues: List[Dict]) -> None:
+    for i, issue in enumerate(issues, 1):
+        if not issue.get("issue_id"):
+            issue["issue_id"] = f"ISSUE-{i:03d}"
+
+
+def score_and_prioritize(validated: List[Dict]) -> List[Dict]:
+    if not validated:
+        return []
+    _ensure_ids(validated)
+
+    parsed = call_stage(
+        RISK_SYSTEM_PROMPT,
+        RISK_USER_INSTRUCTION,
+        subs={"validated_issues": dumps(validated)},
+        max_tokens=config.RISK_MAX_TOKENS,
+    )
+
+    if isinstance(parsed, list):
+        rows = parsed
+    elif isinstance(parsed, dict):
+        rows = parsed.get("prioritized_issues") or []
+    else:
+        rows = []
+    scores: Dict[str, Dict] = {}
+    for p in rows:
+        if isinstance(p, dict) and p.get("issue_id"):
+            scores[p["issue_id"]] = p
+
+    for issue in validated:
+        p = scores.get(issue["issue_id"])
+        if p and isinstance(p.get("overall_risk_score"), (int, float)):
+            issue["risk_score"] = int(p["overall_risk_score"])
+            issue["recommended_priority"] = p.get("recommended_priority")
+            issue["risk_drivers"] = p.get("risk_drivers") or []
+        else:
+            # Deterministic fallback from severity.
+            issue["risk_score"] = _SEV_RANK.get(issue.get("severity"), 40)
+
+    validated.sort(key=lambda i: i.get("risk_score", 0), reverse=True)
+    print(f"[Risk] scored {len(validated)} issue(s)"
+          f" ({len(scores)} from model, rest by severity)")
+    return validated