初始化项目

.shared/ui-ux-pro-max/scripts/core.py

@@ -0,0 +1,257 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+UI/UX Pro Max Core - BM25 search engine for UI/UX style guides
+"""
+
+import csv
+import re
+from pathlib import Path
+from math import log
+from collections import defaultdict
+
+# ============ CONFIGURATION ============
+DATA_DIR = Path(__file__).parent.parent / "data"
+MAX_RESULTS = 3
+
+CSV_CONFIG = {
+    "style": {
+        "file": "styles.csv",
+        "search_cols": ["Style Category", "Keywords", "Best For", "Type"],
+        "output_cols": ["Style Category", "Type", "Keywords", "Primary Colors", "Effects & Animation", "Best For", "Performance", "Accessibility", "Framework Compatibility", "Complexity"]
+    },
+    "prompt": {
+        "file": "prompts.csv",
+        "search_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords"],
+        "output_cols": ["Style Category", "AI Prompt Keywords (Copy-Paste Ready)", "CSS/Technical Keywords", "Implementation Checklist"]
+    },
+    "color": {
+        "file": "colors.csv",
+        "search_cols": ["Product Type", "Keywords", "Notes"],
+        "output_cols": ["Product Type", "Keywords", "Primary (Hex)", "Secondary (Hex)", "CTA (Hex)", "Background (Hex)", "Text (Hex)", "Border (Hex)", "Notes"]
+    },
+    "chart": {
+        "file": "charts.csv",
+        "search_cols": ["Data Type", "Keywords", "Best Chart Type", "Accessibility Notes"],
+        "output_cols": ["Data Type", "Keywords", "Best Chart Type", "Secondary Options", "Color Guidance", "Accessibility Notes", "Library Recommendation", "Interactive Level"]
+    },
+    "landing": {
+        "file": "landing.csv",
+        "search_cols": ["Pattern Name", "Keywords", "Conversion Optimization", "Section Order"],
+        "output_cols": ["Pattern Name", "Keywords", "Section Order", "Primary CTA Placement", "Color Strategy", "Conversion Optimization"]
+    },
+    "product": {
+        "file": "products.csv",
+        "search_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Key Considerations"],
+        "output_cols": ["Product Type", "Keywords", "Primary Style Recommendation", "Secondary Styles", "Landing Page Pattern", "Dashboard Style (if applicable)", "Color Palette Focus"]
+    },
+    "ux": {
+        "file": "ux-guidelines.csv",
+        "search_cols": ["Category", "Issue", "Description", "Platform"],
+        "output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
+    },
+    "typography": {
+        "file": "typography.csv",
+        "search_cols": ["Font Pairing Name", "Category", "Mood/Style Keywords", "Best For", "Heading Font", "Body Font"],
+        "output_cols": ["Font Pairing Name", "Category", "Heading Font", "Body Font", "Mood/Style Keywords", "Best For", "Google Fonts URL", "CSS Import", "Tailwind Config", "Notes"]
+    },
+    "icons": {
+        "file": "icons.csv",
+        "search_cols": ["Category", "Icon Name", "Keywords", "Best For"],
+        "output_cols": ["Category", "Icon Name", "Keywords", "Library", "Import Code", "Usage", "Best For", "Style"]
+    },
+    "react": {
+        "file": "react-performance.csv",
+        "search_cols": ["Category", "Issue", "Keywords", "Description"],
+        "output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
+    },
+    "web": {
+        "file": "web-interface.csv",
+        "search_cols": ["Category", "Issue", "Keywords", "Description"],
+        "output_cols": ["Category", "Issue", "Platform", "Description", "Do", "Don't", "Code Example Good", "Code Example Bad", "Severity"]
+    }
+}
+
+STACK_CONFIG = {
+    "html-tailwind": {"file": "stacks/html-tailwind.csv"},
+    "react": {"file": "stacks/react.csv"},
+    "nextjs": {"file": "stacks/nextjs.csv"},
+    "vue": {"file": "stacks/vue.csv"},
+    "nuxtjs": {"file": "stacks/nuxtjs.csv"},
+    "nuxt-ui": {"file": "stacks/nuxt-ui.csv"},
+    "svelte": {"file": "stacks/svelte.csv"},
+    "swiftui": {"file": "stacks/swiftui.csv"},
+    "react-native": {"file": "stacks/react-native.csv"},
+    "flutter": {"file": "stacks/flutter.csv"},
+    "shadcn": {"file": "stacks/shadcn.csv"}
+}
+
+# Common columns for all stacks
+_STACK_COLS = {
+    "search_cols": ["Category", "Guideline", "Description", "Do", "Don't"],
+    "output_cols": ["Category", "Guideline", "Description", "Do", "Don't", "Code Good", "Code Bad", "Severity", "Docs URL"]
+}
+
+AVAILABLE_STACKS = list(STACK_CONFIG.keys())
+
+
+# ============ BM25 IMPLEMENTATION ============
+class BM25:
+    """BM25 ranking algorithm for text search"""
+
+    def __init__(self, k1=1.5, b=0.75):
+        self.k1 = k1
+        self.b = b
+        self.corpus = []
+        self.doc_lengths = []
+        self.avgdl = 0
+        self.idf = {}
+        self.doc_freqs = defaultdict(int)
+        self.N = 0
+
+    def tokenize(self, text):
+        """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"""
+        self.corpus = [self.tokenize(doc) for doc in documents]
+        self.N = len(self.corpus)
+        if self.N == 0:
+            return
+        self.doc_lengths = [len(doc) for doc in self.corpus]
+        self.avgdl = sum(self.doc_lengths) / self.N
+
+        for doc in self.corpus:
+            seen = set()
+            for word in doc:
+                if word not in seen:
+                    self.doc_freqs[word] += 1
+                    seen.add(word)
+
+        for word, freq in self.doc_freqs.items():
+            self.idf[word] = log((self.N - freq + 0.5) / (freq + 0.5) + 1)
+
+    def score(self, query):
+        """Score all documents against query"""
+        query_tokens = self.tokenize(query)
+        scores = []
+
+        for idx, doc in enumerate(self.corpus):
+            score = 0
+            doc_len = self.doc_lengths[idx]
+            term_freqs = defaultdict(int)
+            for word in doc:
+                term_freqs[word] += 1
+
+            for token in query_tokens:
+                if token in self.idf:
+                    tf = term_freqs[token]
+                    idf = self.idf[token]
+                    numerator = tf * (self.k1 + 1)
+                    denominator = tf + self.k1 * (1 - self.b + self.b * doc_len / self.avgdl)
+                    score += idf * numerator / denominator
+
+            scores.append((idx, score))
+
+        return sorted(scores, key=lambda x: x[1], reverse=True)
+
+
+# ============ SEARCH FUNCTIONS ============
+def _load_csv(filepath):
+    """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"""
+    if not filepath.exists():
+        return []
+
+    data = _load_csv(filepath)
+
+    # Build documents from search columns
+    documents = [" ".join(str(row.get(col, "")) for col in search_cols) for row in data]
+
+    # BM25 search
+    bm25 = BM25()
+    bm25.fit(documents)
+    ranked = bm25.score(query)
+
+    # Get top results with score > 0
+    results = []
+    for idx, score in ranked[:max_results]:
+        if score > 0:
+            row = data[idx]
+            results.append({col: row.get(col, "") for col in output_cols if col in row})
+
+    return results
+
+
+def detect_domain(query):
+    """Auto-detect the most relevant domain from query"""
+    query_lower = query.lower()
+
+    domain_keywords = {
+        "color": ["color", "palette", "hex", "#", "rgb"],
+        "chart": ["chart", "graph", "visualization", "trend", "bar", "pie", "scatter", "heatmap", "funnel"],
+        "landing": ["landing", "page", "cta", "conversion", "hero", "testimonial", "pricing", "section"],
+        "product": ["saas", "ecommerce", "e-commerce", "fintech", "healthcare", "gaming", "portfolio", "crypto", "dashboard"],
+        "prompt": ["prompt", "css", "implementation", "variable", "checklist", "tailwind"],
+        "style": ["style", "design", "ui", "minimalism", "glassmorphism", "neumorphism", "brutalism", "dark mode", "flat", "aurora"],
+        "ux": ["ux", "usability", "accessibility", "wcag", "touch", "scroll", "animation", "keyboard", "navigation", "mobile"],
+        "typography": ["font", "typography", "heading", "serif", "sans"],
+        "icons": ["icon", "icons", "lucide", "heroicons", "symbol", "glyph", "pictogram", "svg icon"],
+        "react": ["react", "next.js", "nextjs", "suspense", "memo", "usecallback", "useeffect", "rerender", "bundle", "waterfall", "barrel", "dynamic import", "rsc", "server component"],
+        "web": ["aria", "focus", "outline", "semantic", "virtualize", "autocomplete", "form", "input type", "preconnect"]
+    }
+
+    scores = {domain: sum(1 for kw in keywords if kw in query_lower) for domain, keywords in domain_keywords.items()}
+    best = max(scores, key=scores.get)
+    return best if scores[best] > 0 else "style"
+
+
+def search(query, domain=None, max_results=MAX_RESULTS):
+    """Main search function with auto-domain detection"""
+    if domain is None:
+        domain = detect_domain(query)
+
+    config = CSV_CONFIG.get(domain, CSV_CONFIG["style"])
+    filepath = DATA_DIR / config["file"]
+
+    if not filepath.exists():
+        return {"error": f"File not found: {filepath}", "domain": domain}
+
+    results = _search_csv(filepath, config["search_cols"], config["output_cols"], query, max_results)
+
+    return {
+        "domain": domain,
+        "query": query,
+        "file": config["file"],
+        "count": len(results),
+        "results": results
+    }
+
+
+def search_stack(query, stack, max_results=MAX_RESULTS):
+    """Search stack-specific guidelines"""
+    if stack not in STACK_CONFIG:
+        return {"error": f"Unknown stack: {stack}. Available: {', '.join(AVAILABLE_STACKS)}"}
+
+    filepath = DATA_DIR / STACK_CONFIG[stack]["file"]
+
+    if not filepath.exists():
+        return {"error": f"Stack file not found: {filepath}", "stack": stack}
+
+    results = _search_csv(filepath, _STACK_COLS["search_cols"], _STACK_COLS["output_cols"], query, max_results)
+
+    return {
+        "domain": "stack",
+        "stack": stack,
+        "query": query,
+        "file": STACK_CONFIG[stack]["file"],
+        "count": len(results),
+        "results": results
+    }

.shared/ui-ux-pro-max/scripts/design_system.py

@@ -0,0 +1,487 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Design System Generator - Aggregates search results and applies reasoning
+to generate comprehensive design system recommendations.
+
+Usage:
+    from design_system import generate_design_system
+    result = generate_design_system("SaaS dashboard", "My Project")
+"""
+
+import csv
+import json
+from pathlib import Path
+from core import search, DATA_DIR
+
+
+# ============ CONFIGURATION ============
+REASONING_FILE = "ui-reasoning.csv"
+
+SEARCH_CONFIG = {
+    "product": {"max_results": 1},
+    "style": {"max_results": 3},
+    "color": {"max_results": 2},
+    "landing": {"max_results": 2},
+    "typography": {"max_results": 2}
+}
+
+
+# ============ DESIGN SYSTEM GENERATOR ============
+class DesignSystemGenerator:
+    """Generates design system recommendations from aggregated searches."""
+
+    def __init__(self):
+        self.reasoning_data = self._load_reasoning()
+
+    def _load_reasoning(self) -> list:
+        """Load reasoning rules from CSV."""
+        filepath = DATA_DIR / REASONING_FILE
+        if not filepath.exists():
+            return []
+        with open(filepath, 'r', encoding='utf-8') as f:
+            return list(csv.DictReader(f))
+
+    def _multi_domain_search(self, query: str, style_priority: list = None) -> dict:
+        """Execute searches across multiple domains."""
+        results = {}
+        for domain, config in SEARCH_CONFIG.items():
+            if domain == "style" and style_priority:
+                # For style, also search with priority keywords
+                priority_query = " ".join(style_priority[:2]) if style_priority else query
+                combined_query = f"{query} {priority_query}"
+                results[domain] = search(combined_query, domain, config["max_results"])
+            else:
+                results[domain] = search(query, domain, config["max_results"])
+        return results
+
+    def _find_reasoning_rule(self, category: str) -> dict:
+        """Find matching reasoning rule for a category."""
+        category_lower = category.lower()
+
+        # Try exact match first
+        for rule in self.reasoning_data:
+            if rule.get("UI_Category", "").lower() == category_lower:
+                return rule
+
+        # Try partial match
+        for rule in self.reasoning_data:
+            ui_cat = rule.get("UI_Category", "").lower()
+            if ui_cat in category_lower or category_lower in ui_cat:
+                return rule
+
+        # Try keyword match
+        for rule in self.reasoning_data:
+            ui_cat = rule.get("UI_Category", "").lower()
+            keywords = ui_cat.replace("/", " ").replace("-", " ").split()
+            if any(kw in category_lower for kw in keywords):
+                return rule
+
+        return {}
+
+    def _apply_reasoning(self, category: str, search_results: dict) -> dict:
+        """Apply reasoning rules to search results."""
+        rule = self._find_reasoning_rule(category)
+
+        if not rule:
+            return {
+                "pattern": "Hero + Features + CTA",
+                "style_priority": ["Minimalism", "Flat Design"],
+                "color_mood": "Professional",
+                "typography_mood": "Clean",
+                "key_effects": "Subtle hover transitions",
+                "anti_patterns": "",
+                "decision_rules": {},
+                "severity": "MEDIUM"
+            }
+
+        # Parse decision rules JSON
+        decision_rules = {}
+        try:
+            decision_rules = json.loads(rule.get("Decision_Rules", "{}"))
+        except json.JSONDecodeError:
+            pass
+
+        return {
+            "pattern": rule.get("Recommended_Pattern", ""),
+            "style_priority": [s.strip() for s in rule.get("Style_Priority", "").split("+")],
+            "color_mood": rule.get("Color_Mood", ""),
+            "typography_mood": rule.get("Typography_Mood", ""),
+            "key_effects": rule.get("Key_Effects", ""),
+            "anti_patterns": rule.get("Anti_Patterns", ""),
+            "decision_rules": decision_rules,
+            "severity": rule.get("Severity", "MEDIUM")
+        }
+
+    def _select_best_match(self, results: list, priority_keywords: list) -> dict:
+        """Select best matching result based on priority keywords."""
+        if not results:
+            return {}
+
+        if not priority_keywords:
+            return results[0]
+
+        # First: try exact style name match
+        for priority in priority_keywords:
+            priority_lower = priority.lower().strip()
+            for result in results:
+                style_name = result.get("Style Category", "").lower()
+                if priority_lower in style_name or style_name in priority_lower:
+                    return result
+
+        # Second: score by keyword match in all fields
+        scored = []
+        for result in results:
+            result_str = str(result).lower()
+            score = 0
+            for kw in priority_keywords:
+                kw_lower = kw.lower().strip()
+                # Higher score for style name match
+                if kw_lower in result.get("Style Category", "").lower():
+                    score += 10
+                # Lower score for keyword field match
+                elif kw_lower in result.get("Keywords", "").lower():
+                    score += 3
+                # Even lower for other field matches
+                elif kw_lower in result_str:
+                    score += 1
+            scored.append((score, result))
+
+        scored.sort(key=lambda x: x[0], reverse=True)
+        return scored[0][1] if scored and scored[0][0] > 0 else results[0]
+
+    def _extract_results(self, search_result: dict) -> list:
+        """Extract results list from search result dict."""
+        return search_result.get("results", [])
+
+    def generate(self, query: str, project_name: str = None) -> dict:
+        """Generate complete design system recommendation."""
+        # Step 1: First search product to get category
+        product_result = search(query, "product", 1)
+        product_results = product_result.get("results", [])
+        category = "General"
+        if product_results:
+            category = product_results[0].get("Product Type", "General")
+
+        # Step 2: Get reasoning rules for this category
+        reasoning = self._apply_reasoning(category, {})
+        style_priority = reasoning.get("style_priority", [])
+
+        # Step 3: Multi-domain search with style priority hints
+        search_results = self._multi_domain_search(query, style_priority)
+        search_results["product"] = product_result  # Reuse product search
+
+        # Step 4: Select best matches from each domain using priority
+        style_results = self._extract_results(search_results.get("style", {}))
+        color_results = self._extract_results(search_results.get("color", {}))
+        typography_results = self._extract_results(search_results.get("typography", {}))
+        landing_results = self._extract_results(search_results.get("landing", {}))
+
+        best_style = self._select_best_match(style_results, reasoning.get("style_priority", []))
+        best_color = color_results[0] if color_results else {}
+        best_typography = typography_results[0] if typography_results else {}
+        best_landing = landing_results[0] if landing_results else {}
+
+        # Step 5: Build final recommendation
+        # Combine effects from both reasoning and style search
+        style_effects = best_style.get("Effects & Animation", "")
+        reasoning_effects = reasoning.get("key_effects", "")
+        combined_effects = style_effects if style_effects else reasoning_effects
+
+        return {
+            "project_name": project_name or query.upper(),
+            "category": category,
+            "pattern": {
+                "name": best_landing.get("Pattern Name", reasoning.get("pattern", "Hero + Features + CTA")),
+                "sections": best_landing.get("Section Order", "Hero > Features > CTA"),
+                "cta_placement": best_landing.get("Primary CTA Placement", "Above fold"),
+                "color_strategy": best_landing.get("Color Strategy", ""),
+                "conversion": best_landing.get("Conversion Optimization", "")
+            },
+            "style": {
+                "name": best_style.get("Style Category", "Minimalism"),
+                "type": best_style.get("Type", "General"),
+                "effects": style_effects,
+                "keywords": best_style.get("Keywords", ""),
+                "best_for": best_style.get("Best For", ""),
+                "performance": best_style.get("Performance", ""),
+                "accessibility": best_style.get("Accessibility", "")
+            },
+            "colors": {
+                "primary": best_color.get("Primary (Hex)", "#2563EB"),
+                "secondary": best_color.get("Secondary (Hex)", "#3B82F6"),
+                "cta": best_color.get("CTA (Hex)", "#F97316"),
+                "background": best_color.get("Background (Hex)", "#F8FAFC"),
+                "text": best_color.get("Text (Hex)", "#1E293B"),
+                "notes": best_color.get("Notes", "")
+            },
+            "typography": {
+                "heading": best_typography.get("Heading Font", "Inter"),
+                "body": best_typography.get("Body Font", "Inter"),
+                "mood": best_typography.get("Mood/Style Keywords", reasoning.get("typography_mood", "")),
+                "best_for": best_typography.get("Best For", ""),
+                "google_fonts_url": best_typography.get("Google Fonts URL", ""),
+                "css_import": best_typography.get("CSS Import", "")
+            },
+            "key_effects": combined_effects,
+            "anti_patterns": reasoning.get("anti_patterns", ""),
+            "decision_rules": reasoning.get("decision_rules", {}),
+            "severity": reasoning.get("severity", "MEDIUM")
+        }
+
+
+# ============ OUTPUT FORMATTERS ============
+BOX_WIDTH = 90  # Wider box for more content
+
+def format_ascii_box(design_system: dict) -> str:
+    """Format design system as ASCII box with emojis (MCP-style)."""
+    project = design_system.get("project_name", "PROJECT")
+    pattern = design_system.get("pattern", {})
+    style = design_system.get("style", {})
+    colors = design_system.get("colors", {})
+    typography = design_system.get("typography", {})
+    effects = design_system.get("key_effects", "")
+    anti_patterns = design_system.get("anti_patterns", "")
+
+    def wrap_text(text: str, prefix: str, width: int) -> list:
+        """Wrap long text into multiple lines."""
+        if not text:
+            return []
+        words = text.split()
+        lines = []
+        current_line = prefix
+        for word in words:
+            if len(current_line) + len(word) + 1 <= width - 2:
+                current_line += (" " if current_line != prefix else "") + word
+            else:
+                if current_line != prefix:
+                    lines.append(current_line)
+                current_line = prefix + word
+        if current_line != prefix:
+            lines.append(current_line)
+        return lines
+
+    # Build sections from pattern
+    sections = pattern.get("sections", "").split(">")
+    sections = [s.strip() for s in sections if s.strip()]
+
+    # Build output lines
+    lines = []
+    w = BOX_WIDTH - 1
+
+    lines.append("+" + "-" * w + "+")
+    lines.append(f"|  TARGET: {project} - RECOMMENDED DESIGN SYSTEM".ljust(BOX_WIDTH) + "|")
+    lines.append("+" + "-" * w + "+")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Pattern section
+    lines.append(f"|  PATTERN: {pattern.get('name', '')}".ljust(BOX_WIDTH) + "|")
+    if pattern.get('conversion'):
+        lines.append(f"|     Conversion: {pattern.get('conversion', '')}".ljust(BOX_WIDTH) + "|")
+    if pattern.get('cta_placement'):
+        lines.append(f"|     CTA: {pattern.get('cta_placement', '')}".ljust(BOX_WIDTH) + "|")
+    lines.append("|     Sections:".ljust(BOX_WIDTH) + "|")
+    for i, section in enumerate(sections, 1):
+        lines.append(f"|       {i}. {section}".ljust(BOX_WIDTH) + "|")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Style section
+    lines.append(f"|  STYLE: {style.get('name', '')}".ljust(BOX_WIDTH) + "|")
+    if style.get("keywords"):
+        for line in wrap_text(f"Keywords: {style.get('keywords', '')}", "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+    if style.get("best_for"):
+        for line in wrap_text(f"Best For: {style.get('best_for', '')}", "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+    if style.get("performance") or style.get("accessibility"):
+        perf_a11y = f"Performance: {style.get('performance', '')} | Accessibility: {style.get('accessibility', '')}"
+        lines.append(f"|     {perf_a11y}".ljust(BOX_WIDTH) + "|")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Colors section
+    lines.append("|  COLORS:".ljust(BOX_WIDTH) + "|")
+    lines.append(f"|     Primary:    {colors.get('primary', '')}".ljust(BOX_WIDTH) + "|")
+    lines.append(f"|     Secondary:  {colors.get('secondary', '')}".ljust(BOX_WIDTH) + "|")
+    lines.append(f"|     CTA:        {colors.get('cta', '')}".ljust(BOX_WIDTH) + "|")
+    lines.append(f"|     Background: {colors.get('background', '')}".ljust(BOX_WIDTH) + "|")
+    lines.append(f"|     Text:       {colors.get('text', '')}".ljust(BOX_WIDTH) + "|")
+    if colors.get("notes"):
+        for line in wrap_text(f"Notes: {colors.get('notes', '')}", "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Typography section
+    lines.append(f"|  TYPOGRAPHY: {typography.get('heading', '')} / {typography.get('body', '')}".ljust(BOX_WIDTH) + "|")
+    if typography.get("mood"):
+        for line in wrap_text(f"Mood: {typography.get('mood', '')}", "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+    if typography.get("best_for"):
+        for line in wrap_text(f"Best For: {typography.get('best_for', '')}", "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+    if typography.get("google_fonts_url"):
+        lines.append(f"|     Google Fonts: {typography.get('google_fonts_url', '')}".ljust(BOX_WIDTH) + "|")
+    if typography.get("css_import"):
+        lines.append(f"|     CSS Import: {typography.get('css_import', '')[:70]}...".ljust(BOX_WIDTH) + "|")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Key Effects section
+    if effects:
+        lines.append("|  KEY EFFECTS:".ljust(BOX_WIDTH) + "|")
+        for line in wrap_text(effects, "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+        lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Anti-patterns section
+    if anti_patterns:
+        lines.append("|  AVOID (Anti-patterns):".ljust(BOX_WIDTH) + "|")
+        for line in wrap_text(anti_patterns, "|     ", BOX_WIDTH):
+            lines.append(line.ljust(BOX_WIDTH) + "|")
+        lines.append("|" + " " * BOX_WIDTH + "|")
+
+    # Pre-Delivery Checklist section
+    lines.append("|  PRE-DELIVERY CHECKLIST:".ljust(BOX_WIDTH) + "|")
+    checklist_items = [
+        "[ ] No emojis as icons (use SVG: Heroicons/Lucide)",
+        "[ ] cursor-pointer on all clickable elements",
+        "[ ] Hover states with smooth transitions (150-300ms)",
+        "[ ] Light mode: text contrast 4.5:1 minimum",
+        "[ ] Focus states visible for keyboard nav",
+        "[ ] prefers-reduced-motion respected",
+        "[ ] Responsive: 375px, 768px, 1024px, 1440px"
+    ]
+    for item in checklist_items:
+        lines.append(f"|     {item}".ljust(BOX_WIDTH) + "|")
+    lines.append("|" + " " * BOX_WIDTH + "|")
+
+    lines.append("+" + "-" * w + "+")
+
+    return "\n".join(lines)
+
+
+def format_markdown(design_system: dict) -> str:
+    """Format design system as markdown."""
+    project = design_system.get("project_name", "PROJECT")
+    pattern = design_system.get("pattern", {})
+    style = design_system.get("style", {})
+    colors = design_system.get("colors", {})
+    typography = design_system.get("typography", {})
+    effects = design_system.get("key_effects", "")
+    anti_patterns = design_system.get("anti_patterns", "")
+
+    lines = []
+    lines.append(f"## Design System: {project}")
+    lines.append("")
+
+    # Pattern section
+    lines.append("### Pattern")
+    lines.append(f"- **Name:** {pattern.get('name', '')}")
+    if pattern.get('conversion'):
+        lines.append(f"- **Conversion Focus:** {pattern.get('conversion', '')}")
+    if pattern.get('cta_placement'):
+        lines.append(f"- **CTA Placement:** {pattern.get('cta_placement', '')}")
+    if pattern.get('color_strategy'):
+        lines.append(f"- **Color Strategy:** {pattern.get('color_strategy', '')}")
+    lines.append(f"- **Sections:** {pattern.get('sections', '')}")
+    lines.append("")
+
+    # Style section
+    lines.append("### Style")
+    lines.append(f"- **Name:** {style.get('name', '')}")
+    if style.get('keywords'):
+        lines.append(f"- **Keywords:** {style.get('keywords', '')}")
+    if style.get('best_for'):
+        lines.append(f"- **Best For:** {style.get('best_for', '')}")
+    if style.get('performance') or style.get('accessibility'):
+        lines.append(f"- **Performance:** {style.get('performance', '')} | **Accessibility:** {style.get('accessibility', '')}")
+    lines.append("")
+
+    # Colors section
+    lines.append("### Colors")
+    lines.append(f"| Role | Hex |")
+    lines.append(f"|------|-----|")
+    lines.append(f"| Primary | {colors.get('primary', '')} |")
+    lines.append(f"| Secondary | {colors.get('secondary', '')} |")
+    lines.append(f"| CTA | {colors.get('cta', '')} |")
+    lines.append(f"| Background | {colors.get('background', '')} |")
+    lines.append(f"| Text | {colors.get('text', '')} |")
+    if colors.get("notes"):
+        lines.append(f"\n*Notes: {colors.get('notes', '')}*")
+    lines.append("")
+
+    # Typography section
+    lines.append("### Typography")
+    lines.append(f"- **Heading:** {typography.get('heading', '')}")
+    lines.append(f"- **Body:** {typography.get('body', '')}")
+    if typography.get("mood"):
+        lines.append(f"- **Mood:** {typography.get('mood', '')}")
+    if typography.get("best_for"):
+        lines.append(f"- **Best For:** {typography.get('best_for', '')}")
+    if typography.get("google_fonts_url"):
+        lines.append(f"- **Google Fonts:** {typography.get('google_fonts_url', '')}")
+    if typography.get("css_import"):
+        lines.append(f"- **CSS Import:**")
+        lines.append(f"```css")
+        lines.append(f"{typography.get('css_import', '')}")
+        lines.append(f"```")
+    lines.append("")
+
+    # Key Effects section
+    if effects:
+        lines.append("### Key Effects")
+        lines.append(f"{effects}")
+        lines.append("")
+
+    # Anti-patterns section
+    if anti_patterns:
+        lines.append("### Avoid (Anti-patterns)")
+        lines.append(f"- {anti_patterns.replace(' + ', '\n- ')}")
+        lines.append("")
+
+    # Pre-Delivery Checklist section
+    lines.append("### Pre-Delivery Checklist")
+    lines.append("- [ ] No emojis as icons (use SVG: Heroicons/Lucide)")
+    lines.append("- [ ] cursor-pointer on all clickable elements")
+    lines.append("- [ ] Hover states with smooth transitions (150-300ms)")
+    lines.append("- [ ] Light mode: text contrast 4.5:1 minimum")
+    lines.append("- [ ] Focus states visible for keyboard nav")
+    lines.append("- [ ] prefers-reduced-motion respected")
+    lines.append("- [ ] Responsive: 375px, 768px, 1024px, 1440px")
+    lines.append("")
+
+    return "\n".join(lines)
+
+
+# ============ MAIN ENTRY POINT ============
+def generate_design_system(query: str, project_name: str = None, output_format: str = "ascii") -> str:
+    """
+    Main entry point for design system generation.
+
+    Args:
+        query: Search query (e.g., "SaaS dashboard", "e-commerce luxury")
+        project_name: Optional project name for output header
+        output_format: "ascii" (default) or "markdown"
+
+    Returns:
+        Formatted design system string
+    """
+    generator = DesignSystemGenerator()
+    design_system = generator.generate(query, project_name)
+
+    if output_format == "markdown":
+        return format_markdown(design_system)
+    return format_ascii_box(design_system)
+
+
+# ============ CLI SUPPORT ============
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate Design System")
+    parser.add_argument("query", help="Search query (e.g., 'SaaS dashboard')")
+    parser.add_argument("--project-name", "-p", type=str, default=None, help="Project name")
+    parser.add_argument("--format", "-f", choices=["ascii", "markdown"], default="ascii", help="Output format")
+
+    args = parser.parse_args()
+
+    result = generate_design_system(args.query, args.project_name, args.format)
+    print(result)

.shared/ui-ux-pro-max/scripts/search.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+UI/UX Pro Max Search - BM25 search engine for UI/UX style guides
+Usage: python search.py "<query>" [--domain <domain>] [--stack <stack>] [--max-results 3]
+       python search.py "<query>" --design-system [-p "Project Name"]
+
+Domains: style, prompt, color, chart, landing, product, ux, typography
+Stacks: html-tailwind, react, nextjs
+"""
+
+import argparse
+from core import CSV_CONFIG, AVAILABLE_STACKS, MAX_RESULTS, search, search_stack
+from design_system import generate_design_system
+
+
+def format_output(result):
+    """Format results for Claude consumption (token-optimized)"""
+    if "error" in result:
+        return f"Error: {result['error']}"
+
+    output = []
+    if result.get("stack"):
+        output.append(f"## UI Pro Max Stack Guidelines")
+        output.append(f"**Stack:** {result['stack']} | **Query:** {result['query']}")
+    else:
+        output.append(f"## UI Pro Max Search Results")
+        output.append(f"**Domain:** {result['domain']} | **Query:** {result['query']}")
+    output.append(f"**Source:** {result['file']} | **Found:** {result['count']} results\n")
+
+    for i, row in enumerate(result['results'], 1):
+        output.append(f"### Result {i}")
+        for key, value in row.items():
+            value_str = str(value)
+            if len(value_str) > 300:
+                value_str = value_str[:300] + "..."
+            output.append(f"- **{key}:** {value_str}")
+        output.append("")
+
+    return "\n".join(output)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="UI Pro Max Search")
+    parser.add_argument("query", help="Search query")
+    parser.add_argument("--domain", "-d", choices=list(CSV_CONFIG.keys()), help="Search domain")
+    parser.add_argument("--stack", "-s", choices=AVAILABLE_STACKS, help="Stack-specific search (html-tailwind, react, nextjs)")
+    parser.add_argument("--max-results", "-n", type=int, default=MAX_RESULTS, help="Max results (default: 3)")
+    parser.add_argument("--json", action="store_true", help="Output as JSON")
+    # Design system generation
+    parser.add_argument("--design-system", "-ds", action="store_true", help="Generate complete design system recommendation")
+    parser.add_argument("--project-name", "-p", type=str, default=None, help="Project name for design system output")
+    parser.add_argument("--format", "-f", choices=["ascii", "markdown"], default="ascii", help="Output format for design system")
+
+    args = parser.parse_args()
+
+    # Design system takes priority
+    if args.design_system:
+        result = generate_design_system(args.query, args.project_name, args.format)
+        print(result)
+    # Stack search
+    elif args.stack:
+        result = search_stack(args.query, args.stack, args.max_results)
+        if args.json:
+            import json
+            print(json.dumps(result, indent=2, ensure_ascii=False))
+        else:
+            print(format_output(result))
+    # Domain search
+    else:
+        result = search(args.query, args.domain, args.max_results)
+        if args.json:
+            import json
+            print(json.dumps(result, indent=2, ensure_ascii=False))
+        else:
+            print(format_output(result))