chore(typing): exclude skill dirs from ty analysis

.agent/skills/ui-ux-pro-max/scripts/core.py

@@ -1,13 +1,14 @@
 #!/usr/bin/env python3
-"""UI/UX Pro Max Core - BM25 search engine for UI/UX style guides.
+# -*- coding: utf-8 -*-
+"""
+UI/UX Pro Max Core - BM25 search engine for UI/UX style guides
 """
 
 import csv
 import re
-from collections import defaultdict
-from math import log
 from pathlib import Path
-
+from math import log
+from collections import defaultdict
 
 # ============ CONFIGURATION ============
 DATA_DIR = Path(__file__).parent.parent / "data"
@@ -93,7 +94,7 @@
 
 # ============ BM25 IMPLEMENTATION ============
 class BM25:
-    """BM25 ranking algorithm for text search."""
+    """BM25 ranking algorithm for text search"""
 
     def __init__(self, k1=1.5, b=0.75):
         self.k1 = k1
@@ -106,12 +107,12 @@ def __init__(self, k1=1.5, b=0.75):
         self.N = 0
 
     def tokenize(self, text):
-        """Lowercase, split, remove punctuation, filter short words."""
+        """Lowercase, split, remove punctuation, filter short words"""
         text = re.sub(r'[^\w\s]', ' ', str(text).lower())
         return [w for w in text.split() if len(w) > 2]
 
     def fit(self, documents):
-        """Build BM25 index from documents."""
+        """Build BM25 index from documents"""
         self.corpus = [self.tokenize(doc) for doc in documents]
         self.N = len(self.corpus)
         if self.N == 0:
@@ -130,7 +131,7 @@ def fit(self, documents):
             self.idf[word] = log((self.N - freq + 0.5) / (freq + 0.5) + 1)
 
     def score(self, query):
-        """Score all documents against query."""
+        """Score all documents against query"""
         query_tokens = self.tokenize(query)
         scores = []
 
@@ -156,13 +157,13 @@ def score(self, query):
 
 # ============ SEARCH FUNCTIONS ============
 def _load_csv(filepath):
-    """Load CSV and return list of dicts."""
-    with open(filepath, encoding='utf-8') as f:
+    """Load CSV and return list of dicts"""
+    with open(filepath, 'r', encoding='utf-8') as f:
         return list(csv.DictReader(f))
 
 
 def _search_csv(filepath, search_cols, output_cols, query, max_results):
-    """Core search function using BM25."""
+    """Core search function using BM25"""
     if not filepath.exists():
         return []
 
@@ -187,7 +188,7 @@ def _search_csv(filepath, search_cols, output_cols, query, max_results):
 
 
 def detect_domain(query):
-    """Auto-detect the most relevant domain from query."""
+    """Auto-detect the most relevant domain from query"""
     query_lower = query.lower()
 
     domain_keywords = {
@@ -209,7 +210,7 @@ def detect_domain(query):
 
 
 def search(query, domain=None, max_results=MAX_RESULTS):
-    """Main search function with auto-domain detection."""
+    """Main search function with auto-domain detection"""
     if domain is None:
         domain = detect_domain(query)
 
@@ -231,7 +232,7 @@ def search(query, domain=None, max_results=MAX_RESULTS):
 
 
 def search_stack(query, stack, max_results=MAX_RESULTS):
-    """Search stack-specific guidelines."""
+    """Search stack-specific guidelines"""
     if stack not in STACK_CONFIG:
         return {"error": f"Unknown stack: {stack}. Available: {', '.join(AVAILABLE_STACKS)}"}