some organization pass, still need to review a bunch

src/tool_bias.py

@@ -6,7 +6,7 @@ from src.models import Tool, ToolPreset, BiasProfile
 class ToolBiasEngine:
  def apply_semantic_nudges(self, tool_definitions: List[Dict[str, Any]], preset: ToolPreset) -> List[Dict[str, Any]]:
   """
-    [C: tests/test_tool_bias.py:test_apply_semantic_nudges, tests/test_tool_bias.py:test_parameter_bias_nudging]
+  [C: tests/test_tool_bias.py:test_apply_semantic_nudges, tests/test_tool_bias.py:test_parameter_bias_nudging]
   """
   weight_map = {
    5: "[HIGH PRIORITY] ",
@@ -42,7 +42,7 @@ class ToolBiasEngine:
 
  def generate_tooling_strategy(self, preset: ToolPreset, global_bias: BiasProfile) -> str:
   """
-    [C: tests/test_tool_bias.py:test_generate_tooling_strategy]
+  [C: tests/test_tool_bias.py:test_generate_tooling_strategy]
   """
   lines = ["### Tooling Strategy"]
   
@@ -51,23 +51,17 @@ class ToolBiasEngine:
   for cat_tools in preset.categories.values():
    for t in cat_tools:
     if not isinstance(t, Tool): continue
-    if t.weight >= 5:
-     preferred.append(f"{t.name} [HIGH PRIORITY]")
-    elif t.weight == 4:
-     preferred.append(f"{t.name} [PREFERRED]")
-    elif t.weight == 2:
-     low_priority.append(f"{t.name} [NOT RECOMMENDED]")
-    elif t.weight <= 1:
-     low_priority.append(f"{t.name} [LOW PRIORITY]")
+    if   t.weight >= 5: preferred.append(f"{t.name} [HIGH PRIORITY]")
+    elif t.weight == 4: preferred.append(f"{t.name} [PREFERRED]")
+    elif t.weight == 2: low_priority.append(f"{t.name} [NOT RECOMMENDED]")
+    elif t.weight <= 1: low_priority.append(f"{t.name} [LOW PRIORITY]")
   
-  if preferred:
-   lines.append(f"Preferred tools: {', '.join(preferred)}.")
-  if low_priority:
-   lines.append(f"Low-priority tools: {', '.join(low_priority)}.")
+  if preferred:    lines.append(f"Preferred tools: {', '.join(preferred)}.")
+  if low_priority: lines.append(f"Low-priority tools: {', '.join(low_priority)}.")
   
   if global_bias.category_multipliers:
    lines.append("Category focus multipliers:")
    for cat, mult in global_bias.category_multipliers.items():
     lines.append(f"- {cat}: {mult}x")
     
-  return "\n\n".join(lines)
+  return "\n\n".join(lines)