init

.opencode/skills/threejs/scripts/core.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Three.js Skill Core - BM25 search engine for Three.js examples and API
+"""
+
+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 = 5
+
+CSV_CONFIG = {
+    "examples": {
+        "file": "examples-all.csv",
+        "search_cols": ["Category", "Name", "Keywords", "Use Cases", "Description"],
+        "output_cols": ["ID", "Category", "Name", "File", "Keywords", "URL", "Complexity", "Use Cases", "Description"]
+    },
+    "categories": {
+        "file": "categories.csv",
+        "search_cols": ["Category", "Keywords", "Description", "Primary Use Cases"],
+        "output_cols": ["Category", "Keywords", "Description", "Complexity Range", "Example Count", "Primary Use Cases", "Related Categories"]
+    },
+    "use-cases": {
+        "file": "use-cases.csv",
+        "search_cols": ["Use Case", "Keywords", "Description", "Technologies"],
+        "output_cols": ["Use Case", "Keywords", "Recommended Examples", "Complexity", "Technologies", "Description"]
+    },
+    "api": {
+        "file": "api-reference.csv",
+        "search_cols": ["Category", "Class", "Keywords", "Description", "Common Methods"],
+        "output_cols": ["Category", "Class", "Keywords", "Description", "Common Methods", "Related Classes"]
+    }
+}
+
+# Domain keyword mapping for auto-detection
+DOMAIN_KEYWORDS = {
+    "examples": ["example", "demo", "showcase", "webgl", "webgpu", "animation", "loader", "material", "geometry", "light", "shadow", "postprocessing", "effect", "particle", "physics", "vr", "xr"],
+    "categories": ["category", "group", "section", "list all", "types of"],
+    "use-cases": ["use case", "project", "application", "build", "create", "make", "implement", "for", "suitable"],
+    "api": ["api", "class", "method", "function", "property", "how to", "what is", "parameter", "constructor"]
+}
+
+
+# ============ 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) > 1]
+
+    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()
+
+    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 "examples"
+
+
+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["examples"])
+    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_by_complexity(complexity, max_results=MAX_RESULTS):
+    """Search examples by complexity level"""
+    filepath = DATA_DIR / "examples-all.csv"
+    if not filepath.exists():
+        return {"error": f"File not found: {filepath}"}
+
+    data = _load_csv(filepath)
+    results = [row for row in data if row.get("Complexity", "").lower() == complexity.lower()][:max_results]
+
+    return {
+        "domain": "examples",
+        "complexity": complexity,
+        "count": len(results),
+        "results": results
+    }
+
+
+def search_by_category(category, max_results=MAX_RESULTS):
+    """Search examples by category"""
+    filepath = DATA_DIR / "examples-all.csv"
+    if not filepath.exists():
+        return {"error": f"File not found: {filepath}"}
+
+    data = _load_csv(filepath)
+    results = [row for row in data if category.lower() in row.get("Category", "").lower()][:max_results]
+
+    return {
+        "domain": "examples",
+        "category": category,
+        "count": len(results),
+        "results": results
+    }
+
+
+def get_recommended_examples(use_case, max_results=MAX_RESULTS):
+    """Get recommended examples for a specific use case"""
+    # First search use cases
+    use_case_result = search(use_case, domain="use-cases", max_results=1)
+
+    if use_case_result.get("count", 0) == 0:
+        return {"error": f"No use case found for: {use_case}"}
+
+    # Get recommended examples
+    recommended = use_case_result["results"][0].get("Recommended Examples", "")
+    example_names = [e.strip() for e in recommended.split(";")]
+
+    # Search for each example
+    all_results = []
+    for name in example_names[:max_results]:
+        result = search(name, domain="examples", max_results=1)
+        if result.get("count", 0) > 0:
+            all_results.extend(result["results"])
+
+    return {
+        "domain": "examples",
+        "use_case": use_case,
+        "count": len(all_results),
+        "results": all_results
+    }