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.opencode/skills/design/scripts/cip/core.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+CIP Design Core - BM25 search engine for Corporate Identity Program design guidelines
+"""
+
+import csv
+import re
+from pathlib import Path
+from math import log
+from collections import defaultdict
+
+# ============ CONFIGURATION ============
+DATA_DIR = Path(__file__).parent.parent.parent / "data" / "cip"
+MAX_RESULTS = 3
+
+CSV_CONFIG = {
+    "deliverable": {
+        "file": "deliverables.csv",
+        "search_cols": ["Deliverable", "Category", "Keywords", "Description", "Mockup Context"],
+        "output_cols": ["Deliverable", "Category", "Keywords", "Description", "Dimensions", "File Format", "Logo Placement", "Color Usage", "Typography Notes", "Mockup Context", "Best Practices", "Avoid"]
+    },
+    "style": {
+        "file": "styles.csv",
+        "search_cols": ["Style Name", "Category", "Keywords", "Description", "Mood"],
+        "output_cols": ["Style Name", "Category", "Keywords", "Description", "Primary Colors", "Secondary Colors", "Typography", "Materials", "Finishes", "Mood", "Best For", "Avoid For"]
+    },
+    "industry": {
+        "file": "industries.csv",
+        "search_cols": ["Industry", "Keywords", "CIP Style", "Mood"],
+        "output_cols": ["Industry", "Keywords", "CIP Style", "Primary Colors", "Secondary Colors", "Typography", "Key Deliverables", "Mood", "Best Practices", "Avoid"]
+    },
+    "mockup": {
+        "file": "mockup-contexts.csv",
+        "search_cols": ["Context Name", "Category", "Keywords", "Scene Description"],
+        "output_cols": ["Context Name", "Category", "Keywords", "Scene Description", "Lighting", "Environment", "Props", "Camera Angle", "Background", "Style Notes", "Best For", "Prompt Modifiers"]
+    }
+}
+
+
+# ============ 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 = {
+        "deliverable": ["card", "letterhead", "envelope", "folder", "shirt", "cap", "badge", "signage", "vehicle", "car", "van", "stationery", "uniform", "merchandise", "packaging", "banner", "booth"],
+        "style": ["style", "minimal", "modern", "luxury", "vintage", "industrial", "elegant", "bold", "corporate", "organic", "playful"],
+        "industry": ["tech", "finance", "legal", "healthcare", "hospitality", "food", "fashion", "retail", "construction", "logistics"],
+        "mockup": ["mockup", "scene", "context", "photo", "shot", "lighting", "background", "studio", "lifestyle"]
+    }
+
+    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 "deliverable"
+
+
+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["deliverable"])
+    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_all(query, max_results=2):
+    """Search across all domains and combine results"""
+    all_results = {}
+    for domain in CSV_CONFIG.keys():
+        result = search(query, domain, max_results)
+        if result.get("results"):
+            all_results[domain] = result["results"]
+    return all_results
+
+
+def get_cip_brief(brand_name, industry_query, style_query=None):
+    """Generate a comprehensive CIP brief for a brand"""
+    # Search industry
+    industry_results = search(industry_query, "industry", 1)
+    industry = industry_results.get("results", [{}])[0] if industry_results.get("results") else {}
+
+    # Search style (use industry style if not specified)
+    style_query = style_query or industry.get("CIP Style", "corporate minimal")
+    style_results = search(style_query, "style", 1)
+    style = style_results.get("results", [{}])[0] if style_results.get("results") else {}
+
+    # Get recommended deliverables for the industry
+    key_deliverables = industry.get("Key Deliverables", "").split()
+    deliverable_results = []
+    for d in key_deliverables[:5]:
+        result = search(d, "deliverable", 1)
+        if result.get("results"):
+            deliverable_results.append(result["results"][0])
+
+    return {
+        "brand_name": brand_name,
+        "industry": industry,
+        "style": style,
+        "recommended_deliverables": deliverable_results,
+        "color_system": {
+            "primary": style.get("Primary Colors", industry.get("Primary Colors", "")),
+            "secondary": style.get("Secondary Colors", industry.get("Secondary Colors", ""))
+        },
+        "typography": style.get("Typography", industry.get("Typography", "")),
+        "materials": style.get("Materials", ""),
+        "finishes": style.get("Finishes", "")
+    }